diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index edba43b..071fa48 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -17,7 +17,12 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install pylint + pip install pylint autopep8 + - name: Fixing Indentation with AutoPEP8 + working-directory: ./src/MAGIST + run: | + autopep8 --in-place --recursive . - name: Analysing the code with pylint + working-directory: ./src/MAGIST run: | - pylint $(git ls-files '*.py') + pylint $(git ls-files '*.py') --disable=C0103 --fail-under=3 diff --git a/.gitignore b/.gitignore index 2c062ea..ba30966 100644 --- a/.gitignore +++ b/.gitignore @@ -178,3 +178,8 @@ testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/_mask.so testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/coco.pyc testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/cocoeval.pyc testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/mask.pyc + + +# Added by cargo + +/target diff --git a/.idea/MAGIST-Algorithm.iml b/.idea/MAGIST-Algorithm.iml index 2cb3f33..52e7d6d 100644 --- a/.idea/MAGIST-Algorithm.iml +++ b/.idea/MAGIST-Algorithm.iml @@ -2,11 +2,13 @@ + + - + diff --git a/.idea/misc.xml b/.idea/misc.xml index 34a91dc..58f07a9 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,4 +1,4 @@ - + \ No newline at end of file diff --git a/Cargo.lock b/Cargo.lock new file mode 100644 index 0000000..f222b4b --- /dev/null +++ b/Cargo.lock @@ -0,0 +1,14 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 3 + +[[package]] +name = "MAGIST-Algorithm" +version = "0.1.0" +dependencies = [ + "MAGIST_Rust", +] + +[[package]] +name = "MAGIST_Rust" +version = "0.1.0" diff --git a/Cargo.toml b/Cargo.toml new file mode 100644 index 0000000..832ff3c --- /dev/null +++ b/Cargo.toml @@ -0,0 +1,9 @@ +[package] +name = "MAGIST-Algorithm" +version = "0.1.0" +edition = "2021" + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +MAGIST_Rust = { path="./src/MAGIST/MAGIST_Rust" } \ No newline at end of file diff --git a/README.md b/README.md index 64cdeb6..c8a7fde 100644 --- a/README.md +++ b/README.md @@ -136,7 +136,7 @@ Please consider contributing. Even minute changes to our README will be greatly ### Project Zeta We are building a fully biomimetic robot dog to implement MAGIST into. This will serve as the gateway between MAGIST and the physical world. -[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=DeepShift-Labs&repo=Project-Zeta)](https://github.com/DeepShift-Labs/Project-Zeta) +[![Readme Card](https://github-readme-stats-git-masterrstaa-rickstaa.vercel.app/api/pin/?username=DeepShift-Labs&theme=transparent&repo=Project-Zeta)](https://github.com/DeepShift-Labs/Project-Zeta) *** diff --git a/build_conventional/src/MAGIST/NLP/AudioTranscriber.py b/build_conventional/src/MAGIST/NLP/AudioTranscriber.py new file mode 100644 index 0000000..6b139de --- /dev/null +++ b/build_conventional/src/MAGIST/NLP/AudioTranscriber.py @@ -0,0 +1,82 @@ +"""This has classes to process audio files and microphone data and transcribe them. + +This main class uses the Google Speech API to transcribe audio files. It contains 2 main functions for microphone and +audio file processing respectively. +""" + +import speech_recognition as sr +import time +import pathlib + +from ..Utils.LogMaster.log_init import MainLogger + +class GoogleAudioTranscriber(): + """Google Audio Transcriber Class.""" + + def __init__(self, config): + """Initialize the Google Audio Transcriber and Microphone object. + + :param config: The config file(config.json). + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("GoogleAudioTranscriber") # Create a script specific logging instance + + self.log.info("GoogleAudioTranscriber Recognizer Initialized Successfully") + + def microphone_listener(self): + """Listen to the microphone and transcribe the audio. + + :return: The transcription of the audio as a string. + """ + + self.r = sr.Recognizer() + self.m = sr.Microphone() + + with self.m as source: + self.log.info("GoogleAudioTranscriber Listening...") + audio = self.r.listen(source) + self.log.info("GoogleAudioTranscriber Listening Complete") + try: + start = time.time() + transcription = self.r.recognize_google(audio) + self.log.info("Transcribed Prediction: " + transcription) + end = time.time() + except sr.UnknownValueError: + self.log.warning("Google could not understand audio") + + self.log.info("Time taken: " + str(end - start)) + return transcription + + def file_transcriber(self, file): + """Transcribe an audio file. + + :param file: The file to be transcribed. + + :return: The transcription of the audio as a string. + """ + + self.r = sr.Recognizer() + self.m = sr.Microphone() + + file = pathlib.Path(file) + file = file.resolve() # Find absolute path from a relative one. + file = str(file) + + audio = sr.AudioFile(file) + + with audio as source: + self.log.info("GoogleAudioTranscriber Processing...") + audio = self.r.record(source) + self.log.info("GoogleAudioTranscriber Processing Complete") + try: + start = time.time() + transcription = self.r.recognize_google(audio) + self.log.info("Transcribed Prediction: " + transcription) + end = time.time() + except sr.UnknownValueError: + self.log.warning("Google could not understand audio") + + self.log.info("Time taken: " + str(end - start)) + + return transcription \ No newline at end of file diff --git a/build_conventional/src/MAGIST/NLP/SelfAttention.py b/build_conventional/src/MAGIST/NLP/SelfAttention.py new file mode 100644 index 0000000..d93b713 --- /dev/null +++ b/build_conventional/src/MAGIST/NLP/SelfAttention.py @@ -0,0 +1,246 @@ +"""Contains all necessary functions to train and use unsupervised text prioritization. + +This class has numerous functions to run the text prioritization algorithm. You can simply call the class instance to +run the entire algorithm. +""" + + +import tensorflow as tf +import numpy as np +from ..Utils.LogMaster.log_init import MainLogger + +class TextPreprocessing(): + def __init__(self, config): + """Initializes the TextPreprocessing class and config. + + :param config: The config file containing all necessary parameters(config.json). + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("TextPreprocessing") # Create a script specific logging instance + + def __format_data(self, in_text): + """Formats the input text to be compatible with the model. + + :param in_text: The text(string) to be formatted. + + :return: The formatted text. + """ + punctuation = [".", ",", ";", ":", "!", "?", "\"", "'", "`", "~", "`", "´", "’", "‘", "“", "”", "„", "‟", "‹", "›", + "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", + "„", + "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", + "’", + "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", + "‹", + "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", + "›", + "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", + "„", + "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’"] + def __remove_puctuation(text): + """Removes punctuation from the text. + + :param text: The text to be formatted. + + :return: The formatted text. + """ + for i in punctuation: + text = text.replace(i, "") + return text + + def __lowercase(text): + """Converts the text to lowercase. + + :param text: The text to be formatted. + + :return: The formatted text. + """ + return text.lower() + + in_text = __remove_puctuation(in_text) + in_text = __lowercase(in_text) + return in_text + + def __tokenize(self, input_text): + """Splits the input text into tokens. + + :param input_text: The text to be split. + + :return: The tokens. + """ + input_text = self.__format_data(input_text) + return input_text.split(" ") + + def split_positional_encodings(self, input_text): + """Splits the input text into positional encodings. + + :param input_text: The text to be split. + + :return: The positional encodings. + """ + input_text = self.__format_data(input_text) + split_text = self.__tokenize(input_text) + + position_dict = [] + count = 1 + for s in split_text: + position_dict.append([count, s]) + count += 1 + + return position_dict + + def vectorize_text(self, raw_input_text): + """Vectorizes the input text. + + :param raw_input_text: The text to be vectorized. + + :return: The vectorized text. + """ + input_text = self.__format_data(raw_input_text) + self.vectorize_layer = tf.keras.layers.TextVectorization() + self.vectorize_layer.adapt([raw_input_text]) + vec = self.vectorize_layer(raw_input_text) + + return np.array(vec) + + def __positional_embedding_function(self, n, scale=0.1, scalar=1): + """Computes the positional embedding. + + :param n: The position to be computed. + :param scale: The scale of the positional embedding function. + :param scalar: The scalar multiplier to the output of the positional embedding. + + :return: The positional embedding. + """ + x = np.multiply(n, scale) + part1 = np.sin(np.multiply(n, x)) + part2 = np.sin(np.divide(1, x)) + return np.multiply(np.abs(np.multiply(part1, part2)), scalar) + + def positional_embedding(self, split_positional_encodings, vectorized_text, scalar=3): + """Computes the positional embedding. + + :param split_positional_encodings: The positional encodings to be computed. + :param vectorized_text: The vectorized text. + :param scalar: The scalar multiplier to the output of the positional embedding. + + :return: The positional embedding. + """ + position_computed = [] + for i in split_positional_encodings: + pos = self.__positional_embedding_function(i[0], scalar=scalar) + position_computed.append(pos) + + final_position_embedding = [] + + for i in range(len(position_computed)): + final_position_embedding.append(int(vectorized_text[i]) + position_computed[i]) + + return final_position_embedding + + def SelfAttention(self, value, query, key, in_len): + """Computes the self attention. + + :param value: The value to be computed. + :param query: The query to be computed. + :param key: The key to be computed. + :param in_len: The length of the input. + + Note: The value, query, and key must be the same length and are the same thing usually for self-attention. + """ + value_mod = tf.keras.models.Sequential() + value_mod.add(tf.keras.layers.Dense(in_len, use_bias=False)) + + key_mod = tf.keras.models.Sequential() + key_mod.add(tf.keras.layers.Dense(in_len, use_bias=False)) + + query_mod = tf.keras.models.Sequential() + query_mod.add(tf.keras.layers.Dense(in_len, use_bias=False)) + + # value = value_mod.predict(value) + # key = key_mod.predict(key) + # query = query_mod.predict(query) + + value = np.array(value).astype(np.float64) + key = np.array(key).astype(np.float64) + query = np.array(query).astype(np.float64) + + value = np.squeeze(value) + key = np.squeeze(key) + query = np.squeeze(query) + + val_mag = np.sqrt(np.dot(value, value)) + key_mag = np.sqrt(np.dot(key, key)) + query_mag = np.sqrt(np.dot(query, query)) + + matmul = key_mag * query_mag + theta = np.dot(value, query) / matmul + + # theta = np.arccos(theta) + + softmax = 1 / ((np.e) ** (-theta) + 1) + + out = softmax * value + + return out + + def compute_theshold(self, attention_weights): + """Computes the threshold for relevant and irrelevant text. + + :param attention_weights: The attention weights to be computed. + + :return: The threshold(int). + """ + threshold = 0 + for b in attention_weights: + if b != 0: + threshold += b + return threshold / len(attention_weights) + + def print_results(self, attention_weights, threshold, vectorized_text, show_only_important=False): + """Prints the results. + """ + for i in range(len(attention_weights)): + stat = "" + if attention_weights[i] > threshold: + stat = "Good" + elif attention_weights[i] == threshold: + stat = "Meh" + elif attention_weights[i] < threshold: + stat = "Not" + if show_only_important: + if attention_weights[i] >= threshold: + self.log.info(f"{attention_weights[i]:.2f}, {self.vectorize_layer.get_vocabulary()[vectorized_text[i]]}, {stat}") + else: + self.log.info( + f"{attention_weights[i]:.2f}, {self.vectorize_layer.get_vocabulary()[vectorized_text[i]]}, {stat}") + + def __call__(self, input_text): + """Computes everything and returns the importance matrix. + + :param input_text: The text to be computed. + + :return: The simplified importance matrix. + """ + s = self.split_positional_encodings(input_text) + vec = self.vectorize_text(input_text) + + out_fin = self.positional_embedding(s, vec) + + attention_weights = self.SelfAttention(out_fin, out_fin, out_fin, len(out_fin)) + threshold = self.compute_theshold(attention_weights) + + final_output_array = [] + + for i in range(len(attention_weights)): + stat = "" + if attention_weights[i] > threshold: + stat = "Good" + elif attention_weights[i] == threshold: + stat = "Meh" + elif attention_weights[i] < threshold: + stat = "Not" + + final_output_array.append([attention_weights[i], self.vectorize_layer.get_vocabulary()[vec[i]], stat]) + + return final_output_array diff --git a/build_conventional/src/MAGIST/NLP/WordScraper.py b/build_conventional/src/MAGIST/NLP/WordScraper.py new file mode 100644 index 0000000..11165ef --- /dev/null +++ b/build_conventional/src/MAGIST/NLP/WordScraper.py @@ -0,0 +1,107 @@ +import requests, json + +from ..Utils.LogMaster.log_init import MainLogger + + +class ClassDeprecated(Exception): + pass + + +class UrbanDictionary(): + def __init__(self, config): + """Initialize the Urban Dictionary API. + :param config: The config file(config.json). + """ + + raise ClassDeprecated("This class is deprecated") + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("UrbanDictionary") + + self.url = "https://mashape-community-urban-dictionary.p.rapidapi.com/define" + + def define(self, word): + """Define a word. + :param word: The word to be defined. + :return: The definition of the word. + """ + + querystring = {"term": word} + + headers = { + "X-RapidAPI-Key": "xxx", + "X-RapidAPI-Host": "mashape-community-urban-dictionary.p.rapidapi.com" + } + + response = requests.request("GET", self.url, headers=headers, params=querystring) + + json_data = json.loads(response.text) + + definition = json_data["list"][0]["definition"] + + self.log.info(f"Definition of {word}: " + definition) + + return definition + +class DicitonaryAPIDev(): + def __init__(self, config): + """Initialize the Dictionary API.. + + :param config: The config file(config.json). + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("DisctionaryAPI") + + self.url = "https://api.dictionaryapi.dev/api/v2/entries/en/" + + def define(self, word): + """Define a word. + + :param word: The word to be defined. + + :return: The definition of the word. + """ + + querystring = {"term": word} + + self.url = self.url + word + + response = requests.request("GET", self.url, params=querystring) + + json_data = json.loads(response.text) + + definition = json_data + try: + definition = definition[0]["meanings"][0]["definitions"][0]["definition"] + except KeyError: + definition = "No definition found" + + self.log.info(f"Definition of {word}: " + definition) + + self.url = "https://api.dictionaryapi.dev/api/v2/entries/en/" + + return definition + + + +class FullDictionarySearch(): + def __init__(self, config): + """Initialize the Dictionary API.. + + :param config: The config file(config.json). + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("FullDictionarySearch") + + self.dictdev = DicitonaryAPIDev(config) + + def define(self, word): + definition = self.dictdev.define(word) + + if definition == "No definition found": + self.log.info("No definition found in DictionaryAPI.dev.") + definition = None + + return definition \ No newline at end of file diff --git a/build_conventional/src/MAGIST/NLP/__init__.py b/build_conventional/src/MAGIST/NLP/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/NeuralDB/ElasticSearch.py b/build_conventional/src/MAGIST/NeuralDB/ElasticSearch.py new file mode 100644 index 0000000..3b2fc64 --- /dev/null +++ b/build_conventional/src/MAGIST/NeuralDB/ElasticSearch.py @@ -0,0 +1,248 @@ +import json +import requests +import pathlib + +from ..Utils.LogMaster.log_init import MainLogger + + +class ESDB(): + def __init__(self, config, es_uri, queries_file, schema_file, auto_check_server=True): + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("NeuralDB - ElasticSearchClient") # Create a script specific logging instance + + self.es_uri = es_uri + + schema_file = pathlib.Path(schema_file) + schema_file = schema_file.resolve() # Find absolute path from a relative one. + queries_file = pathlib.Path(queries_file) + queries_file = queries_file.resolve() # Find absolute path from a relative one. + + self.schema_file = f = open(schema_file, 'r') + self.schema_file_data = json.load(self.schema_file) + self.queries_file = f = open(queries_file, 'r') + self.queries_file_data = json.load(self.queries_file) + + self.log.debug(f"ElasticSearch Client initialized with {self.es_uri}. Config files: {self.schema_file} and {self.queries_file} parsed!") + + if auto_check_server: + self.__check_es_status() + + def __check_es_status(self): + es_status = requests.get(self.es_uri, timeout=10) + es_status = json.dumps(str(es_status)) + if "200" not in str(es_status): + raise RuntimeError(f"ElasticSearch Server is unreachable!") + else: + self.log.info(f"ElasticSearch Server is reachable!") + return True + + def create_index(self, index_name, schema_name): + available_schemas = ['object_db_schema', 'word_db_schema'] + success_status = "" + + try: + specific_schema = self.schema_file_data[schema_name] + except KeyError: + self.log.error(f"Schema not found from available schemas: {available_schemas}") + return + + # print(json.dumps(specific_schema, indent=2)) + + schema_uri = self.es_uri + "/" + index_name + + schema_stat = requests.put(schema_uri, json=specific_schema) + + schema_stat = json.dumps(str(schema_stat)) + + check_stat = requests.get(schema_uri + "/_settings") + check_stat = json.dumps(str(check_stat)) + + if "200" in str(schema_stat) and "200" in str(check_stat): + self.log.info(f"Index {index_name} with {schema_name} schema successfully created and verified!") + elif "200" in str(schema_stat) and "200" not in str(check_stat): + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + elif "200" not in str(schema_stat) and "200" in str(check_stat): + self.log.warning(f'Error creating index {index_name} with {schema_name} schema! The schema named {schema_name} likely ' + f'already exists.') + else: + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + + #////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + + def add_doc(es_uri, index_name, data_type, data, update="add"): + data_type_valid = ['object_db_schema', 'word_db_schema'] + if data_type not in data_type_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {data_type_valid}") + + update_valid = ["add", "concatenate", "overwrite", "blind"] + if update not in update_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {update_valid}") + + success_status = "" + + if data_type == 'object_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + name = data['name'] + description = data['description'] + users = data['users'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + queries_file = open('queries.json', 'r') + queries = json.load(queries_file) + + queries["object_exists"]["query"]["query_string"]["query"] = name + + object_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["object_exists"]) + object_exists_simple = json.dumps(str(object_exists)) + object_exists_full = json.loads(str(object_exists.text)) + + print(object_exists_full["hits"]["total"]["value"]) + + if "200" in object_exists_simple and object_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {name} already exists in index {index_name}!") + if object_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = object_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["name"] = name + hit_source["description"] += description + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["name"] = name + hit_source["description"] = description + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in object_exists_simple and object_exists_full["hits"]["total"][ + "value"] == 0 and update == "add": + print(f"Object {name} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {name} successfully added to index {index_name}!") + else: + print(f"Error adding object {name} to index {index_name}!") + else: + print(f"Error checking if object {name} exists in index {index_name}!") + + elif data_type == 'word_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + word = data['word'] + definition = data['definition'] + users = data['users'] + related_words = data['related_words'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + queries_file = open('queries.json', 'r') + queries = json.load(queries_file) + + queries["word_exists"]["query"]["query_string"]["query"] = word + + word_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["word_exists"]) + word_exists_simple = json.dumps(str(word_exists)) + word_exists_full = json.loads(str(word_exists.text)) + + print(word_exists_full["hits"]["total"]["value"]) + + if "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {word} already exists in index {index_name}!") + if word_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = word_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["word"] += word + hit_source["description"] += definition + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["related_words"] += related_words + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["word"] = word + hit_source["description"] = definition + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["related_words"] = related_words + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] == 0 and update == "add": + print(f"Object {word} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {word} successfully added to index {index_name}!") + else: + print(f"Error adding object {word} to index {index_name}!") + else: + print(f"Error checking if object {word} exists in index {index_name}!") diff --git a/build_conventional/src/MAGIST/NeuralDB/MongoUtils.py b/build_conventional/src/MAGIST/NeuralDB/MongoUtils.py new file mode 100644 index 0000000..bf4427d --- /dev/null +++ b/build_conventional/src/MAGIST/NeuralDB/MongoUtils.py @@ -0,0 +1,92 @@ +"""Mongo Utils file that manages and initializes the MongoDB connection. + +This file contains the main Admin functions and the rest is done using the PrimaryNeuralDB file. +""" + +import json +import os +import pathlib + +import pymongo as mongo + +from ..Utils.LogMaster.log_init import MainLogger + + +class AdminUtils(): + """Class that manages the MongoDB connection.""" + + def __init__(self, config): + """Initialize the AdminUtils class. + + :param config: The config file(string). + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("MongoAdminUtils") # Create a script specific logging instance + + self.log.info("Firing up MongoDB Neural Database! Standby...") + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['system_administration']: + try: + self.passcode = i["sudo_password"] + except KeyError: + pass + + for j in config['neural_db']: + try: + self.mgsocket = j["mongo_socket"] + except KeyError: + pass + + def initialize_neuraldb(self): + """Initialize the MongoDB connection. + + :return: The MongoDB client. + """ + + command = 'systemctl start mongod' + p = os.system('echo %s|sudo -S %s' % (self.passcode, command)) + self.log.info("NeuralDB Launched Successfully! Attempting to connect to local socket...") + + self.db_client = mongo.MongoClient(self.mgsocket) + + if self.db_client: + self.log.info("Mongo client linked successfully. Local DB Agent is running.") + else: + self.log.error("Mongo client failed to connect. The Mongo socket URL could be incorrect. It should look " + "something like this: mongodb://localhost:27017/") + + return self.db_client + + def stop_db(self): + """Stop the MongoDB connection. + """ + + self.log.info("Shutting down MongoDB Neural Database! Standby...") + command = 'systemctl stop mongod' + p = os.system('echo %s|sudo -S %s' % (self.passcode, command)) + self.log.info("NeuralDB Closed Successfully!") + + def restart_db(self): + """Restart the MongoDB connection. + """ + + self.log.info("Restarting MongoDB Neural Database! Standby...") + + command = 'systemctl restart mongod' + p = os.system('echo %s|sudo -S %s' % (self.passcode, command)) + self.log.info("NeuralDB Re-Launched Successfully! Attempting to connect to local socket...") + + self.db_client = mongo.MongoClient(self.mgsocket) + + if self.db_client: + self.log.info("Mongo client linked successfully. Local DB Agent is running.") + else: + self.log.error("Mongo client failed to connect. The Mongo socket URL could be incorrect. It should look " + "something like this: mongodb://localhost:27017/") + + return self.db_client diff --git a/build_conventional/src/MAGIST/NeuralDB/PrimaryNeuralDB.py b/build_conventional/src/MAGIST/NeuralDB/PrimaryNeuralDB.py new file mode 100644 index 0000000..42b8e78 --- /dev/null +++ b/build_conventional/src/MAGIST/NeuralDB/PrimaryNeuralDB.py @@ -0,0 +1,426 @@ +"""Main NeuralDB updating and querying class + +This class contains functions necessary to add information to the NeuralDB as well as query it for information upon +request. This requires the instantiated client from MongoUtils. +""" + +import json +import pathlib +import time +import re + +from ..Utils.LogMaster.log_init import MainLogger + + +class NeuralDB(): + """Main NeuralDB class""" + def __init__(self, config, db_client): + """Initialize NeuralDB class, parse config.json, and receive MongoDB client + + :param config: The config(config.json) file as a string. + :param db_client: The MongoDB client from MongoUtils. + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("NeuralDB") # Create a script specific logging instance + self.client = db_client + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['neural_db']: + try: + self.db_string = i["db_search_zone"] + except KeyError: + pass + + def recreate_db(self): + """Recreate the databases and collections + """ + + self.log.warning("NeuralDB is about to reset and recreate all Databases and tables. Proceeding in 5 seconds...") + + for i in range(5): + self.log.warning("{}...".format(5 - i)) + time.sleep(1) + self.log.warning("Resetting and recreating all databases and tables...") + + self.dbs = [] + self.collections = [] + + for d in self.db_string: + if d == "vision": + self.vision = self.client['VisionDB'] + + self.obj_desc = self.vision["ObjectDesc"] + self.obj_location = self.vision["ObjectLocation"] + self.obj_obj_relation = self.vision["ObjectObjectRelation"] + self.obj_users = self.vision["ObjectUsers"] + + self.log.info("Vision database is included in NeuralDB search.") + + self.dbs.append(self.vision) + + self.collections.append(self.obj_desc) + self.collections.append(self.obj_location) + self.collections.append(self.obj_obj_relation) + self.collections.append(self.obj_users) + if d == "nlp": + self.nlp = self.client["NLP"] + + self.word_desc = self.nlp["WordDesc"] + self.word_location = self.nlp["WordLocation"] + + self.log.info("NLP database is included in NeuralDB search.") + + self.dbs.append(self.nlp) + + self.collections.append(self.word_desc) + self.collections.append(self.word_location) + if d == "common": + self.common = self.client["Common"] + + self.word_obj_relation = self.common["WordObjectRelation"] + + self.log.info("Common database is included in NeuralDB search.") + + self.dbs.append(self.common) + + self.collections.append(self.word_obj_relation) + + try: + if self.vision is None: + self.log.warning("Vision database was not included from NeuralDB search.") + except AttributeError: + self.log.warning("Vision database was not included from NeuralDB search.") + + try: + if self.nlp is None: + self.log.warning("NLP database was not included from NeuralDB search.") + except AttributeError: + self.log.warning("NLP database was not included from NeuralDB search.") + + try: + if self.common is None: + self.log.warning("Common database was not included from NeuralDB search.") + except AttributeError: + self.log.warning("Common database was not included from NeuralDB search.") + + def insert_obj_desc(self, obj_name, obj_desc): + """Insert object and its description into the Vision database. + + :param obj_name: The name of the object(string). + :param obj_desc: The description of the object(string). + """ + + self.log.info(f"Inserting object description: {obj_name} - {obj_desc}") + self.obj_desc.insert_one({"obj_name": obj_name, "obj_desc": obj_desc}) + def insert_obj_location(self, obj_name, obj_location): + """Insert object and its location into the Vision database. + + :param obj_name: The name of the object(string). + :param obj_location: The location of the object(string). + """ + self.log.info(f"Inserting object location: {obj_name} - {obj_location}") + self.obj_location.insert_one({"obj_name": obj_name, "obj_location": obj_location}) + def insert_obj_obj_relation(self, obj_name, second_obj_name): + """Insert object and its relation to another object into the Vision database. + + :param obj_name: The name of the object(string). + :param second_obj_name: The name of the second object(string). + """ + + self.log.info(f"Inserting object object relation: {obj_name} - {second_obj_name}") + self.obj_obj_relation.insert_one({"obj_name": obj_name, "second_obj_name": second_obj_name}) + def insert_obj_users(self, obj_name, user_name): + """Insert object and its users into the Vision database. + + :param obj_name: The name of the object(string). + :param user_name: The name of the user(string). + """ + + self.log.info(f"Inserting object users: {obj_name} - {user_name}") + self.obj_users.insert_one({"obj_name": obj_name, "user_name": user_name}) + + def insert_word_desc(self, word_name, word_desc): + """Insert word and its description into the NLP database. + + :param word_name: The name of the word(string). + :param word_desc: The description of the word(string). + """ + if word_desc != None: + self.log.info(f"Inserting word description: {word_name} - {word_desc}") + self.word_desc.insert_one({"word_name": word_name, "word_desc": word_desc}) + def insert_word_location(self, word_name, word_location): + """Insert word and its location into the NLP database. + + :param word_name: The name of the word(string). + :param word_location: The location of the word(string). + """ + + self.log.info(f"Inserting word location: {word_name} - {word_location}") + self.word_location.insert_one({"word_name": word_name, "word_location": word_location}) + def insert_word_obj_relation(self, word_name, obj_name): + """Insert word and its relation to an object into the NLP database. + + :param word_name: The name of the word(string). + :param obj_name: The name of the object(string). + """ + + self.log.info(f"Inserting word object relation: {word_name} - {obj_name}") + self.word_obj_relation.insert_one({"word_name": word_name, "word_relation": obj_name}) + + def search_obj_details(self, obj_name): + """Search for object all details in the Vision database. + + :param obj_name: The name of the object(string). + + :return: A dictionary containing the object details. + """ + + data = [] + self.log.info(f"Searching object details: {obj_name}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"obj_name": re.compile(rf"\b{obj_name}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_obj_desc(self, keyword): + """Search for object descriptions by keyword in the Vision database. + + :param keyword: The keyword to search for(string). + + :return: A dictionary containing the object descriptions. + """ + + data = [] + self.log.info(f"Searching object details by keyword: {keyword}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"obj_desc" : re.compile(rf"\b{keyword}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_obj_location(self, location): + """Search for object locations by location in the Vision database. + + :param location: The location to search for(string). + + :return: A dictionary containing the object locations. + """ + + data = [] + self.log.info(f"Searching object details by location: {location}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"obj_location" : re.compile(rf"\b{location}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_obj_user(self, user): + """Search for object users by user in the Vision database. + + :param user: The user to search for(string). + + :return: A dictionary containing the object users. + """ + + data = [] + self.log.info(f"Searching object details by user: {user}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"user_name" : re.compile(rf"\b{user}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + + + + + def search_word_details(self, word): + """Search for word details in the NLP database. + + :param word: The word to search for(string). + + :return: A dictionary containing the word details. + """ + + data = [] + self.log.info(f"Searching word details: {word}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_name" : re.compile(rf"\b{word}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_word_desc(self, keyword): + """Search for word descriptions by keyword in the NLP database. + + :param keyword: The keyword to search for(string). + + :return: A dictionary containing the word descriptions. + """ + + data = [] + self.log.info(f"Searching word details by keyword: {keyword}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_desc" : re.compile(rf"\b{keyword}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_word_location(self, location): + """Search for word locations by location in the NLP database. + + :param location: The location to search for(string). + + :return: A dictionary containing the word locations. + """ + + data = [] + self.log.info(f"Searching word details by location: {location}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_location" : re.compile(rf"\b{location}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_word_relation(self, relation): + """Search for word relations by relation in the NLP database. + + :param relation: The relation to search for(string). + + :return: A dictionary containing the word relations. + """ + + data = [] + self.log.info(f"Searching word details by relation: {relation}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_relation" : re.compile(rf"\b{relation}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + + def search_entire_db(self, term): + """Search for entire database by term in the NLP database. + + :param term: The term to search for(string). + + :return: A dictionary containing the entire database. + """ + + self._locals_search = locals() + + results = [] + + self.log.info(f"Searching entire database for: {term}") + + final_results = [] + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + for c in d.list_collection_names(): + self.log.info(f" ===> Collection: {c}") + exec(f"db_col_search = self.client.{d.name}.{c}", self._locals_search) + db_col_search = self._locals_search['db_col_search'] + + cursor = d[c].find({}) + keys = list(cursor.next().keys()) + + for key in keys: + self.log.info(f" ===> Key: {c}") + search = db_col_search.find({key : re.compile(rf"\b{term}\b", re.IGNORECASE)}) + results.append(search) + try: + a = search.next() + self.log.info(f" ===> Found: {a}") + final_results.append(a) + except: + self.log.info(f" ===> Found: None") + + return final_results + + + + def remove_duplicates(self): + """Remove duplicates from the Vision database. + """ + + self._locals = locals() + print(self.vision.ObjectDesc) + for d in self.dbs: + for i in d.list_collection_names(): + exec(f"db_col = self.client.{d.name}.{i}", self._locals) + db_col = self._locals['db_col'] + + repeated_val = "" + + if "vision" in d.name.lower(): + repeated_val = "obj_name" + print("vision") + if "nlp" in d.name.lower(): + repeated_val = "word_name" + print("nlp") + + replic = db_col.aggregate([ # Cursor with all duplicated documents + {'$group': { + '_id': {repeated_val: f'${repeated_val}'}, # Duplicated field + 'uniqueIDs': {'$addToSet': '$_id'}, + 'total': {'$sum': 1} + } + }, + {'$match': { + 'total': {'$gt': 1} # Holds how many duplicates for each group, if you need it. + } + } + ]) + # Result is a list of lists of ObjectsIds + for i in replic: + for idx, j in enumerate(i['uniqueIDs']): # It holds the ids of all duplicates + if idx != 0: # Jump over first element to keep it + db_col.delete_one({'_id': j}) + diff --git a/build_conventional/src/MAGIST/NeuralDB/__init__.py b/build_conventional/src/MAGIST/NeuralDB/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/TaskManagment/ThreadedQueue.py b/build_conventional/src/MAGIST/TaskManagment/ThreadedQueue.py new file mode 100644 index 0000000..5d879b0 --- /dev/null +++ b/build_conventional/src/MAGIST/TaskManagment/ThreadedQueue.py @@ -0,0 +1,123 @@ +"""Threaded Queue contains all necessary functions for a priority queue. + +The queue class contains methods to add, execute, thread, and process tasks in a queue based on a priority system. +""" + +import queue +import threading +import uuid +import numpy as np +import pathlib +import json + +from ..Utils.LogMaster.log_init import MainLogger + + +class MainPriorityQueue(): + """Main Priority Queue Class.""" + + def __init__(self, config): + """Initialize the queue. + + :param config: The config file(config.json). + """ + + self.q = queue.PriorityQueue() + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("QueueController") # Create a script specific logging instance + + self.function_returns = [] + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['task_management']: + try: + self.worker_threads = i["num_of_worker_threads"] + except KeyError: + pass + + def __worker(self): + """The worker thread. This actually executes the tasks in the queue. + """ + + while True: + items = self.q.get() + items = items[1] + func = items[0] + args = items[1:] + # print(f'Working on {args}') + + last_item = args[-1] # name + second_last_item = args[-2] # priority + third_last_item = args[-3] # guid + + args = args[:len(args) - 3] + + self.log.info(f'Received task: {last_item} with priority {second_last_item}. Unique ID assigned: {third_last_item}. Executing...') + [*returns] = [func(*args)] + self.log.info(f'Finished {last_item} successfully.') + self.q.task_done() + returns = np.array(returns) + self.function_returns.append([third_last_item, returns]) + # self.function_returns = np.squeeze(self.function_returns) + + def put_queue(self, function, *args, name="Unnamed", priority=None): + """Add a task to the queue. + + :param function: The function to be executed. NOTE: This must be in the form of Class.put_queue(function...), + not Class.put_queue(function()...). + :param args: The arguments to be passed to the function. This can be any number of args. + :param name: The name of the task. + :param priority: The priority of the task. + + :return: The unique ID of the task. + """ + + args = list(args) + + self.guid = uuid.uuid4() + + for i in args: + if i == name or i == priority: + raise ValueError( + "Name or priority cannot be used as argument. Please use priority= and name= in the function call.") + + self.q.put((priority, (function, *args, self.guid, priority, name))) + + return self.guid + + def detach_thread(self): + """Detach the thread from the main thread. + """ + + # Turn-on the worker thread. + for i in range(self.worker_threads): + threading.Thread(target=self.__worker, daemon=True).start() + self.log.info("Thread created and daemonized. Queue started...") + + def join_thread(self): + """Join the queue thread with main. + """ + + self.log.info("Attempting to join main thread...") + self.q.join() + self.log.info("Queue merge finished.") + + def search_results(self, query): + """Search the results for a specific task by ID. + + :param query: The unique ID of the task. NOTE: This must be a UUID object. + + :return: The results of the task. + """ + + r = self.function_returns + r = np.array(r) + r = r[r[:, 0] == query] + r = np.squeeze(r) + + return np.squeeze(r[1]) diff --git a/build_conventional/src/MAGIST/TaskManagment/__init__.py b/build_conventional/src/MAGIST/TaskManagment/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Utils/LogMaster/__init__.py b/build_conventional/src/MAGIST/Utils/LogMaster/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Utils/LogMaster/log_init.py b/build_conventional/src/MAGIST/Utils/LogMaster/log_init.py new file mode 100644 index 0000000..db4db52 --- /dev/null +++ b/build_conventional/src/MAGIST/Utils/LogMaster/log_init.py @@ -0,0 +1,94 @@ +"""Establish major logger functions for individual scripts. + +MainLogger is the main class containing 1 main function that provides a unique logging instance to each script. +""" + +import logging +import json +import os, pathlib + +class CustomFormatter(logging.Formatter): + + blue = '\033[34m' + green = '\033[92m' + grey = "\x1b[38;20m" + yellow = "\x1b[33;20m" + red = "\x1b[31;20m" + bold_red = "\x1b[31;1m" + reset = "\x1b[0m" + format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + + FORMATS = { + logging.DEBUG: blue + format + reset, + logging.INFO: green + format + reset, + logging.WARNING: yellow + format + reset, + logging.ERROR: red + format + reset, + logging.CRITICAL: bold_red + format + reset + } + + def format(self, record): + log_fmt = self.FORMATS.get(record.levelno) + formatter = logging.Formatter(log_fmt) + return formatter.format(record) + +class MainLogger(): + # Logging Class + + def __init__(self, config): + """Initialize class and parse config + + :param config: A relative or absolute path to master config JSON file. + """ + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['paths']: + try: + self.log_dir = i["log_dir"] + except KeyError: + pass + for j in config['basic_variables']: + try: + self.verbose = j["verbose"] + except KeyError: + pass + + self.log_dir = pathlib.Path(self.log_dir) + self.log_dir = self.log_dir.resolve() # Find absolute path from a relative one. + self.log_dir = str(self.log_dir) + + def StandardLogger(self, name): + logger = logging.getLogger(name) + if not self.verbose: # Enable verbose depending on flag set by the config file. + logger.setLevel(logging.WARNING) + else: + logger.setLevel(logging.DEBUG) + # create file handler which logs even debug messages + try: + fh = logging.FileHandler(os.path.join(self.log_dir, 'complete.log')) + except FileNotFoundError: + os.makedirs(self.log_dir) + fh = logging.FileHandler(os.path.join(self.log_dir, 'complete.log')) + + # create console handler with a higher log level + error = logging.StreamHandler() + + error.setFormatter(CustomFormatter()) + + # create formatter and add it to the handlers + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + fh.setFormatter(formatter) + # error.setFormatter(formatter) + + # add the handlers to the logger + logger.addHandler(fh) + logger.addHandler(error) + + logger.info(f"{name}'s LogMaster Instance Initialized Successfully ===> {os.path.join(self.log_dir, 'complete.log')}") + + return logger + + + diff --git a/build_conventional/src/MAGIST/Utils/WebScraper/__init__.py b/build_conventional/src/MAGIST/Utils/WebScraper/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Utils/WebScraper/google.py b/build_conventional/src/MAGIST/Utils/WebScraper/google.py new file mode 100644 index 0000000..763e705 --- /dev/null +++ b/build_conventional/src/MAGIST/Utils/WebScraper/google.py @@ -0,0 +1,195 @@ +"""Provides basic functions for Google Reverse Image Search and scraping. + +GoogleScraper is the main class containing 2 functions: reverse_image_search and download_raw_img_dataset. The function +reverse_image_search takes a given image path and uses a Google API as well as some scraping to find the name of the +object. The function download_raw_img_dataset takes a given keyword and downloads a given quantity of images from Google +images. +""" + +import requests +from bs4 import BeautifulSoup +from selenium import webdriver +from selenium.webdriver.firefox.options import Options +from google_images_search import GoogleImagesSearch +import os +import pathlib, json +from googleapiclient.errors import HttpError + +from ..LogMaster.log_init import MainLogger + + +class GoogleScraper: + """Main Google Images scraping and downloading tool.""" + + def __init__(self, config): + """Initializes the class and authenticates Google Search API with credentials and parses config file. It also + initializes the logger. + + :param config: A relative or absolute path to master config JSON file. + :param dev_api_key DEPRECATED: API key acquired from Google Search API webpage. + :param project_cx_id DEPRECATED: The Search Engine ID provided by Google per Google Developer Project. + + Note: The CX ID is hard to find. To find it, first go to: http://www.google.com/cse/manage/all. Select your + project and the ID will be called: "Search engine ID". Go to this StackOverflow question and PyPi Post for more + info: https://stackoverflow.com/questions/6562125/getting-a-cx-id-for-custom-search-google-api-python & + https://pypi.org/project/Google-Images-Search/ + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("GoogleScraper") # Create a script specific logging instance + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['api_authentication']: + try: + google_conf = i["google"] + for j in google_conf: + try: + self.dev_api_key = j["api_key"] + except KeyError: + pass + try: + self.project_cx_id = j["project_cx"] + except KeyError: + pass + try: + self.GIS_verbose = j["GIS_downloader_verbose"] + except KeyError: + pass + except KeyError: + pass + + def __my_progressbar(self, url, progress): + """Defines custom progressbar to visualize the download process for the image downloader. + + :param url: The URL from which the downloader is currently downloading the image from. + :param progress: The percentage of progress in downloading the file. + :return: None + """ + self.log.info(url + ' ' + str(progress) + '%') + + # t = tqdm(total=100, desc=url) + # t.update(progress) + + # try: + # if(progress == 1): + # t = tqdm(total=100, desc=url) + # else: + # t.update(progress) + # except: + # pass + + def reverse_image_search(self, image_path): + """Takes a given image path and finds the object name using Google Reverse Image Search and scraping. + + :param image_path: Relative or absolute image path. + :return: Object name(String) + """ + filePath = image_path + + filePath = pathlib.Path(filePath) + filePath = filePath.resolve() # Find the absolute path from relative one. + filePath = str(filePath) + + searchUrl = 'http://www.google.com/searchbyimage/upload' + headers = { + 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'} # Change header to ensure that Google Search still functions + multipart = {'encoded_image': (filePath, open(filePath, 'rb')), 'image_content': ''} + + response = requests.post(searchUrl, files=multipart, allow_redirects=False) + fetchUrl = response.headers['Location'] + + options = Options() + options.add_argument("--disable-extensions") + options.add_argument("--disable-gpu") + options.add_argument("--no-sandbox") # linux only + options.add_argument("--headless") + options.headless = True # also works + nav = webdriver.Firefox(options=options) + nav.get(fetchUrl) + self.log.info("Selenium reverse search complete.") + + try: + soup = BeautifulSoup(nav.page_source, 'html.parser') + link = soup.find_all("a", {"class": "fKDtNb"})[0] + link = str(link) + start = link.find(">") + len(">") + end = link.find(" Loading data...") + train, test = self.load_data() + self.log.info("Automated Trainer --> Data loaded successfully.") + self.log.info("Automated Trainer --> Building model...") + self.compile_model() + self.log.info("Automated Trainer --> Model built successfully.") + self.log.info("Automated Trainer --> Setting up callbacks...") + self.callbacks_init() + self.log.info("Automated Trainer --> Callbacks setup successfully.") + self.log.info("Automated Trainer --> Training model...") + self.train() + self.log.info("Automated Trainer --> Training completed successfully.") + + +class MAGIST_CNN_Predictor(): + def __init__(self, config): + """Initializes the predictor and config. + + :param config: A dictionary containing the config.json. + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("MAGIST_Lite_Predictor") # Create a script specific logging instance + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['tf_lite_detector']: + try: + self.TF_ckpt_path = i["TF_ckpt_path"] + except KeyError: + pass + try: + self.export_path = i["export_full_model"] + except KeyError: + pass + try: + self.input_image_size = i["input_image_size"] + except KeyError: + pass + try: + self.grayscale = i["grayscale"] + except KeyError: + pass + + self.export_path = pathlib.Path(self.export_path) + self.export_path = self.export_path.resolve() # Find absolute path from a relative one. + self.export_path = str(self.export_path) + + self.TF_ckpt_path = pathlib.Path(self.TF_ckpt_path) + self.TF_ckpt_path = self.TF_ckpt_path.resolve() # Find absolute path from a relative one. + self.TF_ckpt_path = str(self.TF_ckpt_path) + + self.imported = tf.keras.models.load_model(self.export_path, compile=False) + self.log.info("Model imported from {}.".format(self.export_path)) + + # latest = tf.train.latest_checkpoint(self.TF_ckpt_path) + + # self.imported.load_weights(latest) + self.imported.summary() + + def __load(self, filename): + """Loads a file from the given filename. + + :param filename: The filename to load. + + :return: The loaded image file as np.array. + """ + np_image = Image.open(filename) + np_image = np.array(np_image).astype('float32') / 255 + if self.grayscale: + np_image = transform.resize(np_image, (self.input_image_size[0], self.input_image_size[1], 1)) + else: + np_image = transform.resize(np_image, (self.input_image_size[0], self.input_image_size[1], 3)) + np_image = np.expand_dims(np_image, axis=0) + return np_image + + def img_prediction(self, img_path): + """Predicts the class of the given image. + + :param img_path: The path to the image. + + :return: The softmax array of predictions and the id of the prediction from class names. + """ + img_path = pathlib.Path(img_path) + img_path = img_path.resolve() # Find absolute path from a relative one. + img_path = str(img_path) + + image = self.__load(img_path) + p = self.imported.predict(image) + + p_id = np.array(p) + p_id = np.squeeze(p) + max = p_id.max() + id = np.where(p_id == max) + + return p, id + + def predict_from_batch_data(self, in_batch_ds): + """Predicts the class of the given batch of images. + + :param in_batch_ds: The batch of images. + + :return: The softmax array of predictions and the id of the prediction from class names. + """ + test_ds = in_batch_ds + + img, label = next(iter(test_ds)) + # print(len(img)) + self.log.info("Predicting on batch of {} images.".format(len(img))) + + ids = [] + for i in img: + i = np.array(i) + i = np.expand_dims(i, axis=0) + p = self.imported.predict(i) + p = np.array(p) + p = np.squeeze(p) + max = p.max() + id = np.where(p == max) + ids.append(id[0]) + return np.array(label), np.squeeze(np.array(ids)) diff --git a/build_conventional/src/MAGIST/Vision/FullySupervisedModels/__init__.py b/build_conventional/src/MAGIST/Vision/FullySupervisedModels/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Vision/UnsupervisedModels/__init__.py b/build_conventional/src/MAGIST/Vision/UnsupervisedModels/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Vision/UnsupervisedModels/img_cluster.py b/build_conventional/src/MAGIST/Vision/UnsupervisedModels/img_cluster.py new file mode 100644 index 0000000..863431b --- /dev/null +++ b/build_conventional/src/MAGIST/Vision/UnsupervisedModels/img_cluster.py @@ -0,0 +1,112 @@ +""" + +""" + +import pandas as pd +import matplotlib.pyplot as plt +from skimage.io import imread, imshow, imsave +from skimage.transform import resize +from sklearn.cluster import KMeans +from skimage.util import img_as_uint, img_as_ubyte +from ...Utils.LogMaster.log_init import MainLogger +import pathlib, json + + +class RoughCluster(): + def __init__(self, config): + """Initialize the class, logger module and parse config.json. + + :param config: A relative or absolute path to master config JSON file. + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("UnsupervisedClustering") # Create a script specific logging instance + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['basic_variables']: + try: + self.matplot = i["enable_matplot_display"] + except: + pass + + def unsupervised_clusters(self, n_of_clusters, img_location, img_size, masked_img_dir): + """Make, color, and crop unsupervised clusters. + + :param n_of_clusters: Number of expected objects. + :param img_location: Location of input image. + :param img_size: Resized shape of the image in pixels. This is represented as a tuple (length, height). Note: + This is NOT the current size of the image(it can be though), but rather the size it will be scaled down to for + efficient processing. + :param masked_img_dir: Location of the exported image directories. + :return: None + """ + + def image_to_pandas(image): + """ + + :param image: Location of input image. + + :return: Array of masked image locations. + """ + df = pd.DataFrame([image[:, :, 0].flatten(), + image[:, :, 1].flatten(), + image[:, :, 2].flatten()]).T + df.columns = ['Red_Channel', 'Green_Channel', 'Blue_Channel'] + return df + + img_location = pathlib.Path(img_location) + img_location = img_location.resolve() # Find the absolute path from relative one. + img_location = str(img_location) + + masked_img_dir = pathlib.Path(masked_img_dir) + masked_img_dir = masked_img_dir.resolve() # Find the absolute path from relative one. + masked_img_dir = str(masked_img_dir) + + img = imread(img_location) + img = resize(img, img_size) + plt.figure(num=None, figsize=(8, 6), dpi=80) + if (self.matplot): + imshow(img) + + self.log.info("Input image resized and configured for clustering computation.") + + df_img = image_to_pandas(img) + + kmeans = KMeans(n_clusters=n_of_clusters, random_state=0).fit(df_img) + self.log.info("Image clustering complete!") + + result = kmeans.labels_.reshape(img.shape[0], img.shape[1]) + if (self.matplot): + imshow(result, cmap='viridis') + plt.show() + + fig, axes = plt.subplots(1, n_of_clusters, figsize=(15, 12)) + + clustered_img = [] + + for n, ax in enumerate(axes.flatten()): + img2 = imread(img_location) + img2 = resize(img2, img_size) + img2[:, :, 0] = img2[:, :, 0] * (result == [n]) # Disabling pixels of certain type + img2[:, :, 1] = img2[:, :, 1] * (result == [n]) # Disabling pixels of certain type + img2[:, :, 2] = img2[:, :, 2] * (result == [n]) # Disabling pixels of certain type + unit_img = img_as_ubyte(img2) + try: + imsave(f'{masked_img_dir}/masked{n}.jpg', unit_img) + except FileNotFoundError: + pathlib.Path(masked_img_dir).mkdir(parents=True, exist_ok=True) + imsave(f'{masked_img_dir}/masked{n}.jpg', unit_img) + clustered_img.append(f'{masked_img_dir}/masked{n}.jpg') + ax.imshow(img2) + ax.set_axis_off() + fig.tight_layout() + if (self.matplot): + plt.show() + + return clustered_img + +# unsupervised_clusters(3, 'test.jpg', (540, 480), "./Masks") +# unsupervised_clusters(2, 'masked2.jpg', (540, 480), ".") diff --git a/build_conventional/src/MAGIST/Vision/__init__.py b/build_conventional/src/MAGIST/Vision/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/__init__.py b/build_conventional/src/MAGIST/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mkdocs.yml b/mkdocs.yml deleted file mode 100644 index ecdb8ac..0000000 --- a/mkdocs.yml +++ /dev/null @@ -1,82 +0,0 @@ -INHERIT: site_settings.yml - - -################### NO CHANGES BEYOND THIS POINT ################ -# MkDocs Wiki: https://github.com/mkdocs/mkdocs/wiki -docs_dir: "docs" # Default is "docs" -site_dir: "site" # Default is "site" -use_directory_urls: true # Default is true. - - - -theme: - #Material Theme used here: https://github.com/squidfunk/mkdocs-material - # Material Theme Detailed Reference: https://squidfunk.github.io/mkdocs-material/reference/abbreviations/ - #Other Themes: https://github.com/mkdocs/mkdocs/wiki/MkDocs-Themes - name: material - language: en -# logo: assets/favicon.png - favicon: assets/favicon.png - palette: - - scheme: default - primary: blue - toggle: - icon: material/toggle-switch - name: Switch to dark mode - - scheme: slate - primary: blue - toggle: - icon: material/toggle-switch-off-outline - name: Switch to light mode - features: - - navigation.instant - - search.suggest - - search.highlight -# - navigation.tabs -# - navigation.sections - - navigation.top - - content.code.annotate -# - navigation.expand # This will expand navigation on left - - content.tooltips - - navigation.instant - - navigation.tracking - - -plugins: - - search: - lang: en - prebuild_index: true - indexing: full #Default is full. 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"wsproto-1.1.0-py3-none-any.whl", hash = "sha256:2218cb57952d90b9fca325c0dcfb08c3bda93e8fd8070b0a17f048e2e47a521b"}, - {file = "wsproto-1.1.0.tar.gz", hash = "sha256:a2e56bfd5c7cd83c1369d83b5feccd6d37798b74872866e62616e0ecf111bda8"}, + +[[package]] +name = "wsproto" +version = "1.2.0" +description = "WebSockets state-machine based protocol implementation" +optional = false +python-versions = ">=3.7.0" +files = [ + {file = "wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736"}, + {file = "wsproto-1.2.0.tar.gz", hash = "sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065"}, ] + +[package.dependencies] +h11 = ">=0.9.0,<1" + +[metadata] +lock-version = "2.0" +python-versions = ">=3.10,<3.11" +content-hash = "09e034acec2247e3f778d1fcc95ba7e1584e5d1412f6c2297887bcfc6f955686" diff --git a/pyproject.toml b/pyproject.toml index d464224..72a4c2e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,15 +14,16 @@ numpy = "^1.23.1" pandas = "^1.4.3" Pillow-PIL = "^0.1dev" pymongo = "^4.2.0" -requests = "^2.28.1" +requests = "^2.31.0" urllib3 = "^1.26.11" scikit-image = "^0.19.3" scikit-learn = "^1.1.2" selenium = "^4.4.0" SpeechRecognition-ForkedVersion = "^3.9.2" -tensorflow = "^2.9.1" +tensorflow = "^2.11.1" tqdm = "^4.64.0" wikipedia = "^1.4.0" +opencv-python = "^4.6.0" [tool.poetry.dev-dependencies] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..e69de29 diff --git a/site_settings.yml b/site_settings.yml deleted file mode 100644 index 5a55771..0000000 --- a/site_settings.yml +++ /dev/null @@ -1,46 +0,0 @@ -site_name: MAGIST Algorithm Documentation - DeepShift Labs -repo_url: https://github.com/DeepShift-Labs/MAGIST-Algorithm -edit_uri: "edit/main/docs" # No need to make changes here if you are using github -site_description: "MAGIST Algorithm Documentation - DeepShift Labs" -site_author: Krishna Shah -dev_addr: 127.0.0.1:8011 - -nav: - - "Read Me": README.md - - "Getting Started": - - "Installation": "Getting Started/1 - Initial Steps.md" - - "Process Overview": "Getting Started/2 - Process Overview.md" - - "Next Steps": "Getting Started/3 - Next Steps.md" - - "Configuration": - - "Setup Config File": "Configuration/1 - Setup Config File.md" - - "MAGIST Vision": - - "Basics": "MAGIST Vision/1 - Basics.md" - - "K-Means Clustering": "MAGIST Vision/2 - K-Means Clustering.md" - - "Detection Data Management": "MAGIST Vision/3 - Detection Data Management.md" -# - "SOLID Principles": "General/SOLID Principles.md" -# - Markdown: -# - Markdown Syntax: Markdown/Markdown Syntax.md -# - MkDocks Material Syntax: Markdown/MkDocs Material Syntax.md -# - Git: -# - Concepts: Git/Concepts.md -# - "Reset, Revert & Rebase": Git/Reset-Revert-Rebase.md -# - Troubleshoot: Git/Troubleshoot.md -# - Java: -# - Concepts: General/Java/General.md -# - Windows: -# - General: General/Windows/General.md -# - Files and Folders: General/Windows/Files and Folders.md -# - Enterprise Architect: -# - The Disciplined Agile Framework : Enterprise Architect/DA Framework.md -# - Event Sourcing: Enterprise Architect/Event Sourcing.md -# - Project Management: -# - Scrum: Project Management/Scrum.md -# - ELK Stack: -# - ElasticSearch: -# - Basics: ElasticSearch-LogStash-Kibana/EalsticSearch/Basic.md -# - Installation: ElasticSearch-LogStash-Kibana/EalsticSearch/Installation.md -# - Logging: ElasticSearch-LogStash-Kibana/EalsticSearch/Logging.md -# - Querying: -# - Prep Work: ElasticSearch-LogStash-Kibana/EalsticSearch/Querying/Prep Work.md -# - Full-Text Queries: ElasticSearch-LogStash-Kibana/EalsticSearch/Querying/Full-Text Queries.md -# - Term Queries: ElasticSearch-LogStash-Kibana/EalsticSearch/Querying/Term Queries.md diff --git a/src/MAGIST/MAGIST_Rust/Cargo.lock b/src/MAGIST/MAGIST_Rust/Cargo.lock new file mode 100644 index 0000000..160d6c2 --- /dev/null +++ b/src/MAGIST/MAGIST_Rust/Cargo.lock @@ -0,0 +1,7 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 3 + +[[package]] +name = "MAGIST-Rust" +version = "0.1.0" diff --git a/src/MAGIST/MAGIST_Rust/Cargo.toml b/src/MAGIST/MAGIST_Rust/Cargo.toml new file mode 100644 index 0000000..696b4b1 --- /dev/null +++ b/src/MAGIST/MAGIST_Rust/Cargo.toml @@ -0,0 +1,8 @@ +[package] +name = "MAGIST_Rust" +version = "0.1.0" +edition = "2021" + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] diff --git a/src/MAGIST/MAGIST_Rust/src/lib.rs b/src/MAGIST/MAGIST_Rust/src/lib.rs new file mode 100644 index 0000000..7d12d9a --- /dev/null +++ b/src/MAGIST/MAGIST_Rust/src/lib.rs @@ -0,0 +1,14 @@ +pub fn add(left: usize, right: usize) -> usize { + left + right +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn it_works() { + let result = add(2, 2); + assert_eq!(result, 4); + } +} diff --git a/src/MAGIST/NLP/RNN_Fast_Response.py b/src/MAGIST/NLP/RNN_Fast_Response.py new file mode 100644 index 0000000..46e6795 --- /dev/null +++ b/src/MAGIST/NLP/RNN_Fast_Response.py @@ -0,0 +1,12 @@ +from MAGIST.NeuralDB.MongoUtils import AdminUtils +from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB + + +mongo_admin = AdminUtils("config/config.json") +client = mongo_admin.initialize_neuraldb() +neural_db = NeuralDB("config/config.json", client) + +neural_db.recreate_db() + + +neural_db.search_entire_db("") \ No newline at end of file diff --git a/src/MAGIST/NeuralDB/ElasticSearch.py b/src/MAGIST/NeuralDB/ElasticSearch.py new file mode 100644 index 0000000..287b714 --- /dev/null +++ b/src/MAGIST/NeuralDB/ElasticSearch.py @@ -0,0 +1,332 @@ +import json +import requests +import pathlib + +from ..Utils.LogMaster.log_init import MainLogger + + +class ESDB(): + def __init__(self, config, es_uri, queries_file, schema_file, auto_check_server=True): + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("NeuralDB - ElasticSearchClient") # Create a script specific logging instance + + self.es_uri = es_uri + + schema_file = pathlib.Path(schema_file) + schema_file = schema_file.resolve() # Find absolute path from a relative one. + queries_file = pathlib.Path(queries_file) + queries_file = queries_file.resolve() # Find absolute path from a relative one. + + + self.schema_file = f = open(schema_file, 'r') + self.schema_file_data = json.load(self.schema_file) + self.queries_file = f = open(queries_file, 'r') + self.queries_file_data = json.load(self.queries_file) + + self.log.debug(f"ElasticSearch Client initialized with {self.es_uri}. Config files: {self.schema_file} and {self.queries_file} parsed!") + + if auto_check_server: + self.__check_es_status() + + def __check_es_status(self): + es_status = requests.get(self.es_uri, timeout=10) + es_status = json.dumps(str(es_status)) + if "200" not in str(es_status): + raise RuntimeError(f"ElasticSearch Server is unreachable!") + else: + self.log.info(f"ElasticSearch Server is reachable!") + return True + + def create_index(self, index_name, schema_name): + available_schemas = ['object_db_schema', 'word_db_schema'] + success_status = "" + + try: + specific_schema = self.schema_file_data[schema_name] + except KeyError: + self.log.error(f"Schema not found from available schemas: {available_schemas}") + return + + # print(json.dumps(specific_schema, indent=2)) + + schema_uri = self.es_uri + "/" + index_name + + schema_stat = requests.put(schema_uri, json=specific_schema) + + schema_stat = json.dumps(str(schema_stat)) + + check_stat = requests.get(schema_uri + "/_settings") + check_stat = json.dumps(str(check_stat)) + + if "200" in str(schema_stat) and "200" in str(check_stat): + self.log.info(f"Index {index_name} with {schema_name} schema successfully created and verified!") + elif "200" in str(schema_stat) and "200" not in str(check_stat): + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + elif "200" not in str(schema_stat) and "200" in str(check_stat): + self.log.warning(f'Error creating index {index_name} with {schema_name} schema! The schema named {schema_name} likely ' + f'already exists.') + else: + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + + #////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + + def add_doc(self, index_name, data_type, data, update="add"): + es_uri = self.es_uri + data_type_valid = ['object_db_schema', 'word_db_schema'] + if data_type not in data_type_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {data_type_valid}") + + update_valid = ["add", "concatenate", "overwrite", "blind"] + if update not in update_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {update_valid}") + + success_status = "" + + if data_type == 'object_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + name = data['name'] + description = data['description'] + users = data['users'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + # queries_file = open('queries.json', 'r') + queries = self.queries_file_data + + queries["object_exists"]["query"]["query_string"]["query"] = name + + object_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["object_exists"]) + object_exists_simple = json.dumps(str(object_exists)) + object_exists_full = json.loads(str(object_exists.text)) + + print(object_exists_full["hits"]["total"]["value"]) + + if "200" in object_exists_simple and object_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {name} already exists in index {index_name}!") + if object_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = object_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["name"] = name + hit_source["description"] += description + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["name"] = name + hit_source["description"] = description + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in object_exists_simple and object_exists_full["hits"]["total"][ + "value"] == 0 and update == "add": + print(f"Object {name} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {name} successfully added to index {index_name}!") + else: + print(f"Error adding object {name} to index {index_name}!") + else: + print(f"Error checking if object {name} exists in index {index_name}!") + + elif data_type == 'word_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + word = data['word'] + definition = data['definition'] + users = data['users'] + related_words = data['related_words'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + # queries_file = open('queries.json', 'r') + queries = self.queries_file_data + + queries["word_exists"]["query"]["query_string"]["query"] = word + + word_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["word_exists"]) + word_exists_simple = json.dumps(str(word_exists)) + word_exists_full = json.loads(str(word_exists.text)) + + print(word_exists_full["hits"]["total"]["value"]) + + if "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {word} already exists in index {index_name}!") + if word_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = word_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["word"] += word + hit_source["description"] += definition + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["related_words"] += related_words + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["word"] = word + hit_source["description"] = definition + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["related_words"] = related_words + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] == 0 and update == "add": + print(f"Object {word} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {word} successfully added to index {index_name}!") + else: + print(f"Error adding object {word} to index {index_name}!") + else: + print(f"Error checking if object {word} exists in index {index_name}!") + + def format_object_data(self, name, description="", locations=[], related_objects=[], users=[]): + if type(name) is not str: + raise ValueError("The 'name' parameter MUST be of type String.") + if type(description) is not str: + raise ValueError("The 'description' parameter MUST be of type String.") + if type(locations) is not list: + raise ValueError("The 'locations' parameter MUST be of type List.") + if type(related_objects) is not list: + raise ValueError("The 'related_objects' parameter MUST be of type List.") + if type(users) is not list: + raise ValueError("The 'users' parameter MUST be of type List.") + + + + data = { + "name": name, + "description": description, + "locations": locations, + "related_objects": related_objects, + "users": users + } + + return data + + def format_word_data(self, word, definition="", locations=[], related_objects=[], users=[], related_words=[]): + if type(word) is not str: + raise ValueError("The 'word' parameter MUST be of type String.") + if type(definition) is not str: + raise ValueError("The 'definition' parameter MUST be of type String.") + if type(locations) is not list: + raise ValueError("The 'locations' parameter MUST be of type List.") + if type(related_objects) is not list: + raise ValueError("The 'related_objects' parameter MUST be of type List.") + if type(users) is not list: + raise ValueError("The 'users' parameter MUST be of type List.") + if type(related_words) is not list: + raise ValueError("The 'related_words' parameter MUST be of type List.") + + data = { + "word": word, + "definition": definition, + "locations": locations, + "related_objects": related_objects, + "related_words": related_words, + "users": users + } + + return data + + def search(self, index_name, index_type, keyword): + data_type_valid = ['object_db_schema', 'word_db_schema'] + if index_type not in data_type_valid: + raise ValueError(f"Data type {index_type} not found in available data types: {data_type_valid}") + + queries = self.queries_file_data + + self.log.info(f"Searching for '{keyword}' in '{index_name}' index (of type '{index_type}')...") + + if index_type == "object_db_schema": + queries["object_full"]["query"]["multi_match"]["query"] = keyword + + object_full = requests.post(self.es_uri + "/" + index_name + "/_search", json=queries["object_full"]) + object_full = json.loads(str(object_full.text)) + return object_full + elif index_type == "word_db_schema": + queries["word_full"]["query"]["multi_match"]["query"] = keyword + + word_full = requests.post(self.es_uri + "/" + index_name + "/_search", json=queries["word_full"]) + word_full = json.loads(str(word_full.text)) + return word_full + + def delete_all_indices(self): + + indices_resp = requests.get(self.es_uri + "/_cat/indices/?format=json") + json_indicies = json.loads(indices_resp.text) + indicies = [] + for j in json_indicies: + indicies.append(j["index"]) + + for i in indicies: + del_resp = requests.delete(self.es_uri + "/" + str(i)) diff --git a/src/MAGIST/Utils/LogMaster/log_init.py b/src/MAGIST/Utils/LogMaster/log_init.py index 051eb3f..db4db52 100644 --- a/src/MAGIST/Utils/LogMaster/log_init.py +++ b/src/MAGIST/Utils/LogMaster/log_init.py @@ -7,6 +7,30 @@ import json import os, pathlib +class CustomFormatter(logging.Formatter): + + blue = '\033[34m' + green = '\033[92m' + grey = "\x1b[38;20m" + yellow = "\x1b[33;20m" + red = "\x1b[31;20m" + bold_red = "\x1b[31;1m" + reset = "\x1b[0m" + format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + + FORMATS = { + logging.DEBUG: blue + format + reset, + logging.INFO: green + format + reset, + logging.WARNING: yellow + format + reset, + logging.ERROR: red + format + reset, + logging.CRITICAL: bold_red + format + reset + } + + def format(self, record): + log_fmt = self.FORMATS.get(record.levelno) + formatter = logging.Formatter(log_fmt) + return formatter.format(record) + class MainLogger(): # Logging Class @@ -51,10 +75,13 @@ def StandardLogger(self, name): # create console handler with a higher log level error = logging.StreamHandler() + error.setFormatter(CustomFormatter()) + # create formatter and add it to the handlers formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') fh.setFormatter(formatter) - error.setFormatter(formatter) + # error.setFormatter(formatter) + # add the handlers to the logger logger.addHandler(fh) logger.addHandler(error) diff --git a/src/config/config.json b/src/config/config.json new file mode 100644 index 0000000..f61a2cd --- /dev/null +++ b/src/config/config.json @@ -0,0 +1,39 @@ +{ + "api_authentication": [ + {"google": [ + {"api_key": "AIzaSyBJchpTJ0-Xq2d8dF-x_HLqcVL8x5l2DlI"}, + {"project_cx": "47ab711e86cb1ca1b"}, + {"GIS_downloader_verbose": 0} + ]} + ], + "system_administration": [ + {"sudo_password": "Hanuman@77"} + ], + "task_management": [ + {"num_of_worker_threads": 4} + ], + "paths": [ + {"log_dir": "Logs"} + ], + "basic_variables": [ + {"verbose": 1}, + {"enable_matplot_display": 1} + ], + "tf_lite_detector": [ + {"data_path": "Data"}, + {"TensorBoard_log_dir": "Tensorflow/TensorBoard"}, + {"TF_ckpt_path": "Tensorflow/tf_ckpt"}, + {"input_image_size": [50, 50]}, + {"grayscale": 0}, + {"epochs": 10}, + {"batch_size": 32}, + {"seed": 42}, + {"validation_split": 0.2}, + {"export_full_model": "Tensorflow/Full_TF_Model"} + ], + "neural_db": [ + {"elastic_socket": "http://localhost:9200"}, + {"mongo_socket": "mongodb://localhost:27017/"}, + {"db_search_zone": ["vision", "nlp", "common"]} + ] +} \ No newline at end of file diff --git a/src/config/queries.json b/src/config/queries.json new file mode 100644 index 0000000..80f1086 --- /dev/null +++ b/src/config/queries.json @@ -0,0 +1,36 @@ +{ + "object_exists": { + "query": { + "query_string": { + "query" : "", + "fields": ["name"], + "analyzer": "keyword" + } + } + }, + "object_full": { + "query": { + "multi_match": { + "query" : "", + "analyzer": "main_analyzer" + } + } + }, + "word_exists": { + "query": { + "query_string": { + "query" : "", + "fields": ["name"], + "analyzer": "keyword" + } + } + }, + "word_full": { + "query": { + "multi_match": { + "query" : "", + "analyzer": "main_analyzer" + } + } + } +} \ No newline at end of file diff --git a/src/config/schema.json b/src/config/schema.json new file mode 100644 index 0000000..d614e01 --- /dev/null +++ b/src/config/schema.json @@ -0,0 +1,61 @@ +{ + "object_db_schema": { + "settings": { + "analysis": { + "analyzer": { + "main_analyzer": { + "type": "fingerprint", + "stopwords": "_english_" + } + } + }, + "similarity": { + "main_similarity": { + "type": "LMJelinekMercer", + "lambda": 0.5 + } + } + }, + + "mappings": { + "properties": { + "name": { "type": "keyword" }, + "description": { "type": "text" }, + "users": { "type": "text" }, + "related_objects": { "type": "text" }, + "location": { "type": "text" } + } + } + }, + "word_db_schema": { + "settings": { + "analysis": { + "analyzer": { + "main_analyzer": { + "type": "fingerprint", + "stopwords": "_english_" + } + } + }, + "similarity": { + "main_similarity": { + "type": "LMJelinekMercer", + "lambda": 0.5 + } + } + }, + + "mappings": { + "properties": { + "word": { "type": "text" }, + "definition": { "type": "text" }, + "users": { "type": "text" }, + "related_words": { "type": "text" }, + "related_objects": { "type": "text" }, + "location": { "type": "text" } + } + } + } +} + + diff --git a/src/config/schema_nested.json b/src/config/schema_nested.json new file mode 100644 index 0000000..303cb71 --- /dev/null +++ b/src/config/schema_nested.json @@ -0,0 +1,117 @@ +{ + "object_db_schema": { + "mappings": { + "properties": { + "Object": { + "type": "nested", + "properties": { + "name": { + "type": "nested", + "properties": { + "description": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "users": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "related_objects": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "location": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + } + } + } + } + } + } + } + }, + "word_db_schema": { + "mappings": { + "properties": { + "Word": { + "type": "nested", + "properties": { + "name": { + "type": "nested", + "properties": { + "definition": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "users": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "related_words": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "related_objects": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "location": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + } + } + } + } + } + } + } + } +} + + diff --git a/src/config/test.py b/src/config/test.py new file mode 100644 index 0000000..5c7fc2b --- /dev/null +++ b/src/config/test.py @@ -0,0 +1,57 @@ +import json + + +def tester(path, key): + f = open(path) + + config = json.load(f) + + item_checker = False + + for item in config: + if item == key: + print(item) + item_checker = True + for individual_value in config[item]: #Don't do this, you need to key the item itself. + if individual_value == key: + print(individual_value) + item_checker = True + for sub_key in individual_value: + x = individual_value[sub_key] + + if type(x) == list: + + my_list = [] + + for sub_val in individual_value[sub_key]: + if type(sub_val) == dict: + for x in sub_val: + if x == key: + print(sub_val[x]) + item_checker = True + else: + if sub_key == key: + my_list.append(sub_val) + + if len(my_list) > 0: + print(my_list[::1]) + item_checker = True + else: + pass + else: + if sub_key == key: + print(x) + item_checker = True + + return item_checker + + +dict_key = "project_cx" + +try: + if tester("config.json", dict_key): + pass + else: + print("Sorry, key not found :(") +except FileNotFoundError: + print("Error! Wrong file path / file not found :(") diff --git a/src/main.py b/src/main.py index 588d309..4296ace 100644 --- a/src/main.py +++ b/src/main.py @@ -1,119 +1,115 @@ from MAGIST.Vision.UnsupervisedModels.img_cluster import RoughCluster -import numpy as np - -cluster = RoughCluster("config.json") - -imgs = cluster.unsupervised_clusters(3, "2.jpg", (200, 200), "Clusters") - - from MAGIST.Utils.WebScraper.google import GoogleScraper - -scraper = GoogleScraper("config.json") - -labels = [] - -for i in imgs: - label = scraper.reverse_image_search(i) - labels.append(label) - -labels = np.unique(np.array(labels)) - from MAGIST.TaskManagment.ThreadedQueue import MainPriorityQueue - -queue = MainPriorityQueue("config.json") -queue.detach_thread() - -priority = 1 - - -for l in labels: - queue.put_queue(scraper.download_raw_img_dataset, l, 10, "Data/", name=f"Downloading {l}", priority=priority) - priority += 1 - - -from MAGIST.Vision.DetectionDataManager.image_slicer import ImageSlicer - -slicer = ImageSlicer("config.json") - - - -for l in labels: - path = os.path.join("Data", l) - slicer.image_integrity_verification(path, delete_invalid=True) - slicer.resizer((500, 500), path) - coordinates = slicer.coordinate_compute((500, 500), (100, 100)) - slicer.crop_segments(coordinates, path, "Sliced", l) - - - - -from MAGIST.Vision.FullySupervisedModels.MAGIST_Lite_Detector import MAGIST_CNN - -cnn = MAGIST_CNN("config.json") - -queue.put_queue(cnn, name="MAGIST_CNN_Trainer", priority=10) - from MAGIST.Utils.WebScraper.wikipedia import WikipediaScraper -from MAGIST.NeuralDB.MongoUtils import AdminUtils - -mongo_admin = AdminUtils("config.json") -client = mongo_admin.initialize_neuraldb() - -from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB - -neural_db = NeuralDB("config.json", client) -neural_db.recreate_db() - -wiki = WikipediaScraper("config.json") - -for l in labels: - description = wiki.get_summary(l) - neural_db.insert_obj_desc(l, description) - -neural_db.remove_duplicates() - -from MAGIST.NLP.AudioTranscriber import GoogleAudioTranscriber -transcriber = GoogleAudioTranscriber("config.json") +use_esdb = True -text = transcriber.microphone_listener() +if use_esdb: + from MAGIST.NeuralDB.ElasticSearch import ESDB +elif not use_esdb: + from MAGIST.NeuralDB.MongoUtils import AdminUtils + from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB -from MAGIST.NLP.SelfAttention import TextPreprocessing - -selfattention = TextPreprocessing("config.json") - -selected = [] -for i in selfattention.__call__(text): - if i[2] == "Good": - selected.append(i[1]) - - -search_res = [] -unused_terms = [] -for i in selected: - res = neural_db.search_entire_db(i) - if res != []: - search_res.append(res) - else: - unused_terms.append(i) - -search_res = np.array(search_res) -search_res = np.squeeze(search_res) - -try: - print("=====================================================================================================") - print(search_res) - print("=====================================================================================================") - print(unused_terms) -except IndexError: - print("No results found") - -from MAGIST.NLP.WordScraper import FullDictionarySearch - -dict = FullDictionarySearch("config.json") - -for i in unused_terms: - definition = dict.define(i) - neural_db.insert_word_desc(i, definition) - -neural_db.remove_duplicates() \ No newline at end of file +import numpy as np +from os import walk +from tqdm import tqdm + +filenames = next(walk("inputs"), (None, None, []))[2] # [] if no file + +cluster = RoughCluster("config/config.json") +scraper = GoogleScraper("config/config.json") +queue = MainPriorityQueue("config/config.json") + +if use_esdb: + elastic = ESDB("config/config.json", "http://localhost:9200", "config/queries.json", "config/schema.json") + elastic.delete_all_indices() + elastic.create_index("object_db", "object_db_schema") + elastic.create_index("word_db", "word_db_schema") +elif not use_esdb: + mongo_admin = AdminUtils("config/config.json") + client = mongo_admin.initialize_neuraldb() + neural_db = NeuralDB("config/config.json", client) + + neural_db.recreate_db() + +wiki = WikipediaScraper("config/config.json") + +for f in tqdm(filenames): + try: + imgs = cluster.unsupervised_clusters( + 3, f"inputs/{f}", (200, 200), "Clusters") + + labels = [] + + for i in imgs: + label = scraper.reverse_image_search(i) + labels.append(label) + + labels = np.unique(np.array(labels)) + + queue.detach_thread() + + priority = 1 + + # for l in labels: + # scraper.download_raw_img_dataset(l, 10, "Data/") + + # from MAGIST.Vision.DetectionDataManager.image_slicer import ImageSlicer + # + # slicer = ImageSlicer("config/config.json") + # + # counter = 0 + # for l in labels: + # path = os.path.join("Data", l) + # file = os.listdir(path) + # + # for f in file: + # full_path = os.path.join(path, f) + # os.system(f"mv '{full_path}' '{os.path.join(path, f'Frame{str(counter).zfill(2)}.jpg')}'") + # counter += 1 + # + # counter = 0 + # for l in labels: + # path = os.path.join("Data", l) + # os.listdir(path) + # + # + # for l in labels: + # path = os.path.join("Data", l) + # + # slicer.image_integrity_verification(path, delete_invalid=True) + # slicer.resizer((500, 500), path) + # coordinates = slicer.coordinate_compute((500, 500), (100, 100)) + # slicer.crop_segments(coordinates, path, "Sliced", l) + # + # + # + # + # from MAGIST.Vision.FullySupervisedModels.MAGIST_Lite_Detector import MAGIST_CNN + # + # cnn = MAGIST_CNN("config/config.json") + # + # queue.put_queue(cnn, name="MAGIST_CNN_Trainer", priority=10) + + all_obj_data = [] + + for l in labels: + description = wiki.get_summary(l) + if use_esdb: + all_obj_data.append([l, description]) + elif not use_esdb: + neural_db.insert_obj_desc(l, description) + + if use_esdb: + for d in all_obj_data: + temp_data = elastic.format_object_data(str(d[0]), str(d[1])) + elastic.add_doc("object_db", "object_db_schema", temp_data) + if not use_esdb: + neural_db.remove_duplicates() + queue.join_thread() + + except Exception as e: + print("=============================BIG ERROR: SKIP IMAGE=============================") + print(e) + pass diff --git a/src/main.rs b/src/main.rs new file mode 100644 index 0000000..3b5ed75 --- /dev/null +++ b/src/main.rs @@ -0,0 +1,4 @@ +fn main() { + let out = MAGIST_Rust::add(2, 3); + println!("{}", out); +} \ No newline at end of file diff --git a/src/main_hmi.py b/src/main_hmi.py new file mode 100644 index 0000000..55a0506 --- /dev/null +++ b/src/main_hmi.py @@ -0,0 +1,139 @@ +from MAGIST.NLP.WordScraper import FullDictionarySearch +from MAGIST.NLP.SelfAttention import TextPreprocessing +from MAGIST.NLP.AudioTranscriber import GoogleAudioTranscriber +from MAGIST.Vision.UnsupervisedModels.img_cluster import RoughCluster +import numpy as np +import os +from os import walk +from tqdm import tqdm + +filenames = next(walk("inputs"), (None, None, []))[2] # [] if no file + +cluster = RoughCluster("config/config.json") + +# for f in tqdm(filenames): +# try: +# imgs = cluster.unsupervised_clusters( +# 3, f"inputs/{f}", (200, 200), "Clusters") +# +# from MAGIST.Utils.WebScraper.google import GoogleScraper +# +# scraper = GoogleScraper("config/config.json") +# +# labels = [] +# +# for i in imgs: +# label = scraper.reverse_image_search(i) +# labels.append(label) +# +# labels = np.unique(np.array(labels)) +# +# from MAGIST.TaskManagment.ThreadedQueue import MainPriorityQueue +# +# queue = MainPriorityQueue("config/config.json") +# queue.detach_thread() +# +# priority = 1 +# +# for l in labels: +# scraper.download_raw_img_dataset(l, 10, "Data/") +# +# from MAGIST.Vision.DetectionDataManager.image_slicer import ImageSlicer +# +# slicer = ImageSlicer("config/config.json") +# +# counter = 0 +# for l in labels: +# path = os.path.join("Data", l) +# file = os.listdir(path) +# +# for f in file: +# full_path = os.path.join(path, f) +# os.system( +# f"mv '{full_path}' '{os.path.join(path, f'Frame{str(counter).zfill(2)}.jpg')}'") +# counter += 1 +# +# counter = 0 +# for l in labels: +# path = os.path.join("Data", l) +# os.listdir(path) +# +# for l in labels: +# path = os.path.join("Data", l) +# +# slicer.image_integrity_verification(path, delete_invalid=True) +# slicer.resizer((500, 500), path) +# coordinates = slicer.coordinate_compute((500, 500), (100, 100)) +# slicer.crop_segments(coordinates, path, "Sliced", l) +# +# from MAGIST.Vision.FullySupervisedModels.MAGIST_Lite_Detector import MAGIST_CNN +# +# cnn = MAGIST_CNN("config/config.json") +# +# queue.put_queue(cnn, name="MAGIST_CNN_Trainer", priority=10) +# +# from MAGIST.Utils.WebScraper.wikipedia import WikipediaScraper +# from MAGIST.NeuralDB.MongoUtils import AdminUtils +# +# mongo_admin = AdminUtils("config/config.json") +# client = mongo_admin.initialize_neuraldb() +# +# from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB +# +# neural_db = NeuralDB("config/config.json", client) +# neural_db.recreate_db() +# +# wiki = WikipediaScraper("config/config.json") +# +# for l in labels: +# description = wiki.get_summary(l) +# neural_db.insert_obj_desc(l, description) +# +# neural_db.remove_duplicates() +# queue.join_thread() +# except BaseException: +# print("=============================BIG ERROR: SKIP IMAGE=============================") +# pass + + +transcriber = GoogleAudioTranscriber("config/config.json") + +text = transcriber.microphone_listener() + + +selfattention = TextPreprocessing("config/config.json") + +selected = [] +for i in selfattention.__call__(text): + if i[2] == "Good": + selected.append(i[1]) + + +search_res = [] +unused_terms = [] +for i in selected: + res = neural_db.search_entire_db(i) + if res != []: + search_res.append(res) + else: + unused_terms.append(i) + +search_res = np.array(search_res) +search_res = np.squeeze(search_res) + +try: + print("=====================================================================================================") + print(search_res) + print("=====================================================================================================") + print(unused_terms) +except IndexError: + print("No results found") + + +dict = FullDictionarySearch("config/config.json") + +for i in unused_terms: + definition = dict.define(i) + neural_db.insert_word_desc(i, definition) + +neural_db.remove_duplicates() diff --git a/src/query.py b/src/query.py new file mode 100644 index 0000000..f271eeb --- /dev/null +++ b/src/query.py @@ -0,0 +1,10 @@ +try: + from googlesearch import search +except ImportError: + print("No module named 'google' found") + +# to search +query = "A computer science portal" + +for j in search(query, num=10, stop=10, pause=2): + print(j) \ No newline at end of file diff --git a/src/test.py b/src/test.py index c772e4a..04c1501 100644 --- a/src/test.py +++ b/src/test.py @@ -1,14 +1,117 @@ -from MAGIST.NeuralDB.MongoUtils import AdminUtils -from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB +import os -a = AdminUtils("config.json") -client = a.initialize_neuraldb() +import numpy +import tensorflow as tf +import tensorflow_addons as tfa +print("TensorFlow version:", tf.__version__) -neural_db = NeuralDB("config.json", client) -neural_db.recreate_db() +from tensorflow.keras.layers import Dense, Flatten, Conv2D +import pathlib -a = neural_db.search_entire_db("blah") +# tf.compat.v1.disable_eager_execution() +AUTOTUNE = tf.data.AUTOTUNE -print(a) +dirs = os.listdir(str(pathlib.Path("Data").resolve())) -//testing123 + +train_ds_arr = [] +print(dirs) + + +BATCH_SIZE = 32 + +class SiameseNet(tf.keras.models.Model): + def __init__(self): + super(SiameseNet, self).__init__() + self.conv1 = Conv2D(64, 3, activation='relu') + self.conv2 = Conv2D(32, 3, activation='relu') + self.conv3 = Conv2D(64, 3, activation='relu') + self.flatten = Flatten() + self.d1 = Dense(128, activation='relu') + self.d2 = Dense(64, activation='relu') + self.d3 = Dense(32, ) + + def call(self, x1, x2): + # x1 = self.conv1(x1) + # x1 = self.conv2(x1) + x1 = self.conv3(x1) + x1 = self.flatten(x1) + x1 = self.d1(x1) + x1 = self.d2(x1) + x1 = self.d3(x1) + + # x2 = self.conv1(x2) + # x2 = self.conv2(x2) + x2 = self.conv3(x2) + x2 = self.flatten(x2) + x2 = self.d1(x2) + x2 = self.d2(x2) + x2 = self.d3(x2) + return x1, x2 + +snn = SiameseNet() +optimizer = tf.keras.optimizers.Adam() +# loss = tf.keras.losses.contrastive_loss() + +def get_label(file_path): + # Convert the path to a list of path components + parts = tf.strings.split(file_path, os.path.sep) + # The second to last is the class-directory + one_hot = parts[-2] == dirs + # Integer encode the label + return tf.argmax(one_hot) + +def decode_img(img): + # Convert the compressed string to a 3D uint8 tensor + img = tf.io.decode_jpeg(img, channels=3) + # Resize the image to the desired size + return tf.image.resize(img, [100, 100]) + +def process_path(file_path): + label = get_label(file_path) + # Load the raw data from the file as a string + img = tf.io.read_file(file_path) + img = decode_img(img) + return img, label + +for d in dirs: + data_dir = pathlib.Path(os.path.join("Data", d)).with_suffix('') + x = tf.data.Dataset.list_files(str(data_dir/'*'), shuffle=False) + + x = x.map(process_path, num_parallel_calls=AUTOTUNE) + train_ds_arr.append(x) + +class ContrastiveLoss(tf.keras.losses.Loss): + def __init__(self, margin=1.0): + super().__init__() + self.margin = margin + + def forward(self, x0, x1, y): + # euclidian distance + part1 = (1-y) * tf.pow(tf.subtract(x0, x1), 2) + part2 = y * tf.maximum(0.0, (self.margin-tf.pow(tf.subtract(x0, x1), 2))) + return part1 + part2 + +snn.compile(optimizer=optimizer, loss=ContrastiveLoss, metrics=["accuracy"]) +lossModel = tfa.losses.ContrastiveLoss()#ContrastiveLoss() + +train_loss = tf.keras.metrics.Mean(name='train_loss') +train_accuracy = 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--git a/testing/Rust-Python-MicroAPI/test_microapi/compute.rs b/testing/Rust-Python-MicroAPI/test_microapi/compute.rs new file mode 100644 index 0000000..53bd3d5 --- /dev/null +++ b/testing/Rust-Python-MicroAPI/test_microapi/compute.rs @@ -0,0 +1,26 @@ +use std::time::{Duration, Instant}; + + +fn compute(n: f32, scale: f32, scalar: f32) -> f32 { + let x = n * scale; + let part1 = (n * x).sin(); + let part2 = (1.0/x).sin(); + ((part1 * part2).abs()) * scalar +} + +fn main() { + let mut values = vec![]; + + for i in 1..30 { + let start = Instant::now(); + let out = compute(50.0, 0.1, 1.0); + let duration = start.elapsed(); + + values.push(duration); + + + println!("Time elapsed in expensive_function() is: {:.10?}", duration); + println!("Computed Values is: {:.50?}", out); + } + println!("{:.10?}", values) +} diff --git a/testing/Rust-Python-MicroAPI/test_microapi/src/main.rs b/testing/Rust-Python-MicroAPI/test_microapi/src/main.rs new file mode 100644 index 0000000..1b4432e --- /dev/null +++ b/testing/Rust-Python-MicroAPI/test_microapi/src/main.rs @@ -0,0 +1,80 @@ +extern crate xml_rpc; + +use xml_rpc::{Fault, Server}; +use std::net::{IpAddr, Ipv4Addr, SocketAddr}; +use serde::{Serialize, Deserialize}; + +// #[derive(Clone, Debug, Serialize, Deserialize)] +// struct MoveRightParams { +// pub point: Point, +// pub m: i64, +// } +// +// +// #[derive(Clone, Debug, Serialize, Deserialize)] +// struct Point { +// pub x: i64, +// pub y: i64, +// } +// +// fn move_right(mut p: MoveRightParams) -> Result { +// p.point.x += p.m; +// +// Ok(p.point) +// } + +#[derive(Clone, Debug, Serialize, Deserialize)] +struct PosEmbeddingInputs { + pub n: f32, + pub scale: f32, + pub scalar: f32 +} + +fn compute(inputs: PosEmbeddingInputs) -> Result { + let n = inputs.n; + let scale = inputs.scale; + let scalar = inputs.scalar; + let x = n * scale; + let part1 = (n * x).sin(); + let part2 = (1.0/x).sin(); + Ok(((part1 * part2).abs()) * scalar) +} + +fn main() { + let socket = SocketAddr::new(IpAddr::V4(Ipv4Addr::new(127, 0, 0, 1)), 8080); + let mut server = Server::new(); + + server.register_simple("pos_embedding", &compute); + + let bound_server = server.bind(&socket).unwrap(); + + println!("Running server"); + bound_server.run(); +} +// +// use std::time::{Duration, Instant}; +// use rust_decimal::Decimal; +// +// fn compute(n: Decimal, scale: Decimal, scalar: Decimal) -> Decimal { +// let x = n * scale; +// let part1 = (n * x).sin(); +// let part2 = (Decimal::new(10, 1)/x).sin(); +// ((part1 * part2).abs()) * scalar +// +// } +// +// fn main() { +// let mut values = vec![]; +// +// for i in 1..30 { +// let start = Instant::now(); +// let out = compute(Decimal::new(50, 0), Decimal::new(01, 1), Decimal::new(1, 0)); +// let duration = start.elapsed(); +// +// values.push(duration); +// +// +// println!("Time elapsed in expensive_function() is: {:?}", duration); +// } +// println!("{:?}", values) +// } diff --git a/testing/Rust-Python-MicroAPI/test_microapi/src/slithery.py b/testing/Rust-Python-MicroAPI/test_microapi/src/slithery.py new file mode 100644 index 0000000..de380c2 --- /dev/null +++ b/testing/Rust-Python-MicroAPI/test_microapi/src/slithery.py @@ -0,0 +1,49 @@ +# import xmlrpc.client +# import time +# +# proxy = xmlrpc.client.ServerProxy("http://127.0.0.1:8080") +# +# # print(proxy.system.listMethods()) +# +# +# params = {"point": {"x": 5, "y": 5}, "m": 2} +# +# params = {"n": 5.0, "scale": 10.0, "scalar": 1.0} +# +# start = time.time() +# move_right = proxy.pos_embedding(params) +# end = time.time() +# +# print(move_right) +# print("Time consumed in working: ",(end - start)) + + + +import time +import numpy as np + +def __positional_embedding_function(n, scale=0.1, scalar=1): + """Computes the positional embedding. + + :param n: The position to be computed. + :param scale: The scale of the positional embedding function. + :param scalar: The scalar multiplier to the output of the positional embedding. + + :return: The positional embedding. + """ + x = np.multiply(n, scale) + part1 = np.sin(np.multiply(n, x)) + part2 = np.sin(np.divide(1, x)) + return np.multiply(np.abs(np.multiply(part1, part2)), scalar) + +values = [] + +for i in range(10): + start = time.time() + out = __positional_embedding_function(50) + end = time.time() + print("Time consumed in working: ",(end - start)*1000000000) + print(out) + values.append((end - start)*1000000000) + +print(values) \ No newline at end of file diff --git a/testing/StereoClustering/main.py b/testing/StereoClustering/main.py new file mode 100644 index 0000000..c58e4de --- /dev/null +++ b/testing/StereoClustering/main.py @@ -0,0 +1,27 @@ +import cv2 +import numpy as np + +image = cv2.imread('disp0.jpg') + +kernel1 = np.array([[-1, 0, 1], + [-1, 0, 1], + [-1, 0, 1]]) + +# kernel1 = np.array([[2, 4, 5, 4, 2], +# [4, 9, 12, 9, 4], +# [5, 12, 15, 12, 5], +# [4, 9, 12, 9, 4], +# [2, 4, 5, 4, 2]]) + + + +image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +identity = cv2.filter2D(src=image, ddepth=-1, kernel=kernel1) + +cv2.imshow('Original', image) +cv2.imshow('Identity', identity) + +cv2.waitKey() +# cv2.imwrite('identity.jpg', identity) +cv2.destroyAllWindows()