app/main.py

from fastapi import FastAPI, HTTPException, Header, Depends
from pydantic import BaseModel
import os
from pymongo import MongoClient
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
import uvicorn
from dotenv import load_dotenv
from fastapi.middleware.cors import CORSMiddleware

load_dotenv()

# MongoDB connection
MONGODB_ATLAS_CLUSTER_URI = os.getenv("MONGODB_ATLAS_CLUSTER_URI", None)
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
DB_NAME = "quran_db"
COLLECTION_NAME = "tafsir"
ATLAS_VECTOR_SEARCH_INDEX_NAME = "langchain_index"
MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]


embeddings = SentenceTransformerEmbeddings(model_name="BAAI/bge-m3")

vector_search = MongoDBAtlasVectorSearch.from_connection_string(
    MONGODB_ATLAS_CLUSTER_URI,
    DB_NAME + "." + COLLECTION_NAME,
    embeddings,
    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)


# FastAPI application setup
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


def index_file(filepath):
    """ Index each block in a file separated by double newlines for quick search. 
    Returns a dictionary with key as content and value as block number. """
    index = {}
    with open(filepath, 'r', encoding='utf-8') as file:
        content = file.read()  # Read the whole file at once
        blocks = content.split("\n\n")  # Split the content by double newlines

        for block_number, block in enumerate(blocks, 1):  # Starting block numbers at 1 for human readability
            # Replace single newlines within blocks with space and strip leading/trailing whitespace
            formatted_block = ' '.join(block.split('\n')).strip()
            index[formatted_block] = block_number
            # if(block_number == 100):
            #     print(formatted_block)  # Print the 5th block

    return index


def get_text_by_block_number(filepath, block_numbers):
    """ Retrieve specific blocks from a file based on block numbers, where each block is separated by '\n\n'. """
    blocks_text = []
    with open(filepath, 'r', encoding='utf-8') as file:
        content = file.read()  # Read the whole file at once
        blocks = content.split("\n\n")  # Split the content by double newlines

        for block_number, block in enumerate(blocks, 1):  # Starting block numbers at 1 for human readability
            if block_number in block_numbers:
                # Replace single newlines within blocks with space and strip leading/trailing whitespace
                formatted_block = ' '.join(block.split('\n')).strip()
                blocks_text.append(formatted_block)
                if len(blocks_text) == len(block_numbers):  # Stop reading once all required blocks are retrieved
                    break
    return blocks_text


# Existing API endpoints
@app.get("/")
async def read_root():
    return {"message": "Welcome to our app"}

# New Query model for the POST request body
class Item(BaseModel):
    question: str

EXPECTED_TOKEN = os.getenv("API_TOKEN")

def verify_token(authorization: str = Header(None)):
    """
    Dependency to verify the Authorization header contains the correct Bearer token.
    """
    # Prefix for bearer token in the Authorization header
    prefix = "Bearer "
    
    # Check if the Authorization header is present and correctly formatted
    if not authorization or not authorization.startswith(prefix):
        raise HTTPException(status_code=401, detail="Unauthorized: Missing or invalid token")

    # Extract the token from the Authorization header
    token = authorization[len(prefix):]

    # Compare the extracted token to the expected token value
    if token != EXPECTED_TOKEN:
        raise HTTPException(status_code=401, detail="Unauthorized: Incorrect token")

# New API endpoint to get an answer using the chain
@app.post("/get_answer")
async def get_answer(item: Item, token: str = Depends(verify_token)):
    try:
        # Perform the similarity search with the provided question
        matching_docs = vector_search.similarity_search(item.question, k=3)
        clean_answers = [doc.page_content.replace("\n", " ").strip() for doc in matching_docs]

        # Assuming 'search_file.txt' is where we want to search answers
        answers_index = index_file('app/quran_tafseer_formatted.txt')

        # Collect line numbers based on answers found
        line_numbers = [answers_index[answer] for answer in clean_answers if answer in answers_index]

        # Assuming 'retrieve_file.txt' is where we retrieve lines based on line numbers
        result_text = get_text_by_block_number('app/quran_tafseer.txt', line_numbers)

        return {"result_text": result_text}
    except Exception as e:
        # If there's an error, return a 500 error with the error's details
        raise HTTPException(status_code=500, detail=str(e))

# if __name__ == "__main__":
#     uvicorn.run("main:app", host="0.0.0.0", port=8080, reload=False)