mr

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import os, random, logging, pickle, shutil
from dotenv import load_dotenv, find_dotenv
from typing import Optional
from pydantic import BaseModel, Field

from fastapi import FastAPI, HTTPException, File, UploadFile, status
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from app.engine.processing import ( # << creates the collection already
    process_pdf, 
    process_txt,
    index_data, 
    empty_collection, 
    vector_search,
    vector_search_raw,
)
from app.rag.rag import rag_it

from app.engine.logger import logger

from app.settings import datadir, datadir2

EXTENSIONS = ["pdf", "txt"]

app = FastAPI()

environment = os.getenv("ENVIRONMENT", "dev")  # created by dockerfile

if environment == "dev":
    logger("Running in development mode - allowing CORS for all origins")
    app.add_middleware(
        CORSMiddleware,
        allow_origins=["*"],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

try:
    load_dotenv(find_dotenv('env'))
    
except Exception as e:
    pass 

def read_root():
    logger("Title displayed on home page")
    return """
    <html>
        <body>
            <h1>Welcome to MultiRAG, a RAG system designed by JP Bianchi!</h1>
        </body>
    </html>
    """

# already provided by Reflex
# @app.get("/ping/")
def ping():
    """ Testing """
    logger("Someone is pinging the server")
    return {"answer": str(int(random.random() * 100))}


# @app.delete("/erase_data/")
def erase_data():
    """ Erase all files in the data directory at the first level only,
        (in case we would like to use it for something else) 
        but not the vector store or the parquet file.
        We can do it since the embeddings are in the parquet file already.
    """
    if len(os.listdir(datadir)) == 0:
        logger("No data to erase")
        return {"message": "No data to erase"}
    
    # if we try to rmtree datadir, it looks like /data can't be deleted on HF
    for f in os.listdir(datadir):
        if f == '.DS_Store' or f.split('.')[-1].lower() in EXTENSIONS:
            print(f"Removing {f}")
            os.remove(os.path.join(datadir, f))
            # we don't remove the parquet file, create_index does that
    
    logger("All data has been erased")
    return {"message": "All data has been erased"}


# @app.delete("/empty_collection/")
def delete_vectors():
    """ Empty the collection in the vector store """
    try:
        status = empty_collection()
        return {"message": f"Collection{'' if status else ' NOT'} erased!"}
    except Exception as e:
        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))


# @app.get("/list_files/")
def list_files():
    """ List all files in the data directory """
    print("Listing files")
    files = os.listdir(datadir)
    logger(f"Files in data directory: {files}")
    return {"files": files}


# @app.post("/upload/")
# @limiter.limit("5/minute") see 'slowapi' for rate limiting
async def upload_file(file: UploadFile = File(...)):
    """  Uploads a file in data directory, for later indexing """
    try:
        filepath = os.path.join(datadir, file.filename)
        logger(f"Fiename detected: {file.filename}")
        if os.path.exists(filepath):
            logger(f"File {file.filename} already exists: no processing done")
            return {"message": f"File {file.filename} already exists: no processing done"}    

        else:
            logger(f"Receiving file: {file.filename}")
            contents = await file.read()
            logger(f"File reception complete!")
            
    except Exception as e:
        logger(f"Error during file upload: {str(e)}")
        return {"message": f"Error during file upload:  {str(e)}"}
    
    if file.filename.endswith('.pdf'):
        
        # let's save the file in /data even if it's temp storage on HF
        with open(filepath, 'wb') as f:
            f.write(contents)
        
        # save it also in assets/data because data can be cleared
        filepath2 = os.path.join(datadir2, file.filename)
        with open(filepath2, 'wb') as f:
            f.write(contents)
                
        try:
            logger(f"Starting to process {file.filename}")
            new_content = process_pdf(filepath)
            success = {"message": f"Successfully uploaded {file.filename}"}
            success.update(new_content)
            return success
        
        except Exception as e:
            return {"message": f"Failed to extract text from PDF: {str(e)}"}
    
    elif file.filename.endswith('.txt'):
        
        with open(filepath, 'wb') as f:
            f.write(contents)
        
        filepath2 = os.path.join(datadir2, file.filename)
        with open(filepath2, 'wb') as f:
            f.write(contents)
            
        try:
            logger(f"Reading {file.filename}")
            new_content = process_txt(filepath)
            success = {"message": f"Successfully uploaded {file.filename}"}
            success.update(new_content)
            return success
        
        except Exception as e:
            return {"message": f"Failed to extract text from TXT: {str(e)}"}
        
    else:
        return {"message": "Only PDF & txt files are accepted"}


# @app.post("/create_index/")
async def create_index():
    """ Create an index for the uploaded files """
    
    logger("Creating index for uploaded files")
    try:
        msg = index_data()
        return {"message": msg}
    except Exception as e:
        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))


class Question(BaseModel):
    question: str

# @app.post("/ask/")
async def hybrid_search(question: Question):
    logger(f"Processing question: {question.question}")
    try:
        search_results = vector_search(question.question) 
        logger(f"Answer: {search_results}")
        return {"answer": search_results}
    except Exception as e:
        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
    

# @app.post("/ragit/")
async def ragit(question: Question):
    logger(f"Processing question: {question.question}")
    try:
        search_results = vector_search_raw(question.question) 
        logger(f"Search results generated: {search_results}")
        
        answer = rag_it(question.question, search_results)
        
        logger(f"Answer: {answer}")
        return {"answer": answer}
    except Exception as e:
        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
    

if __name__ == '__main__':
    import uvicorn
    from os import getenv
    port = int(getenv("PORT", 8000))
    print(f"Starting server on port {port}")
    reload = True if environment == "dev" else False
    uvicorn.run("main:app", host="0.0.0.0", port=port, reload=reload)


# Examples:
# curl -X POST "http://localhost:8001/upload" -F "[email protected]"
# curl -X DELETE "http://localhost:8001/erase_data/"
# curl -X GET "http://localhost:8001/list_files/" 

# hf space is at https://jpbianchi-mr.hf.space/ 
# code given by https://jpbianchi-mr.hf.space/docs
# Space must be public
# curl -X POST "https://jpbianchi-mr.hf.space/upload/" -F "[email protected]"

# curl -X POST http://localhost:8000/ask/ -H "Content-Type: application/json" -d '{"question": "what is Amazon loss"}' 
# curl -X POST http://localhost:8000/ragit/ -H "Content-Type: application/json" -d '{"question": "Does ATT have postpaid phone customers?"}'
# see more in notebook upload_index.ipynb