from fastapi import FastAPI, HTTPException

import uvicorn

import os

from dotenv import load_dotenv

from fastapi.middleware.cors import CORSMiddleware

from app.api.routes.AIChatbot import chatbot

from app.api.routes.SentimentAnalysis import sentiment

from app.api.routes.StockData import stock_data

from chromadb import PersistentClient

from chromadb.config import Settings

import google.generativeai as genai

load_dotenv()

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

chroma_client = PersistentClient(path="./db")

collection = chroma_client.get_or_create_collection(name="fintech_docs")

app = FastAPI()

origins = [

os.getenv("FRONTEND_URL"),

os.getenv("TYPESCRIPT_BACKEND_URL")

]

app.add_middleware(

CORSMiddleware,

allow_origins=origins,

allow_credentials=True,

allow_methods=["*"],

allow_headers=["*"],

)

app.include_router(chatbot.router, prefix="/api/v1/chatbot", tags=["chatbot"])

app.include_router(sentiment.router, prefix="/api/v1/news", tags=["sentiment"])

app.include_router(stock_data.router, prefix="/api/v1/stocks", tags=["stocks"])

@app.get("/")

def root():

return {"message": "Welcome to FinTechFore Python Backend", "status": "Ok"}

# 🔍 Chroma vector search using Gemini embeddings

@app.get("/chroma-search")

def chroma_search(q: str):

try:

# Try to generate embedding using Google API

embedding_result = genai.embed_content(

model="models/embedding-001",

content=q

)

if "embedding" not in embedding_result:

raise HTTPException(status_code=500, detail="Failed to generate embedding")

embedding = embedding_result["embedding"]

result = collection.query(query_embeddings=[embedding], n_results=3)

# Return documents if they exist, otherwise return empty list

if "documents" in result and result["documents"]:

return result["documents"][0] if len(result["documents"]) > 0 else []

else:

return []

except Exception as e:

error_msg = str(e)

print(f"Chroma search error: {error_msg}") # Log the error for debugging

# Check if it's a quota exceeded error

if "quota" in error_msg.lower() or "429" in error_msg:

# Return a default response when quota is exceeded

return ["We're currently experiencing high demand. Please try again later or check back soon."]

else:

# Provide a more helpful message for other errors

return [f"Search temporarily unavailable: {str(e)[:100]}..."]

if __name__ == "__main__":

uvicorn.run(app, host="0.0.0.0", port=8000)