main.py

from fastapi import FastAPI, Request, Form, Response, UploadFile
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.templating import Jinja2Templates
from fastapi.middleware.cors import CORSMiddleware
from pathlib import Path
import os
import json
from dotenv import load_dotenv
from typing import List
from langchain_community.embeddings import OllamaEmbeddings
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.vectorstores import FAISS
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_groq import ChatGroq
from pydantic import BaseModel
from langchain_huggingface import HuggingFaceEmbeddings


load_dotenv()

app = FastAPI()
templates = Jinja2Templates(directory="templates")

# Configure CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://unstructured-ai.vercel.app", "https://unstructured-ai.vercel.app/" ,"https://unstructured-git-master-kartikeya-mishras-projects.vercel.app/"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load embeddings
# embeddings = OllamaEmbeddings(model="all-minilm")
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

def convert_to_base_message(message_dict):
    return BaseMessage(
        content=message_dict['content'],
        role=message_dict['role'],
        metadata={}  # Include any metadata if necessary
    )

def format_chat_history(chat_history_list):
    return [convert_to_base_message(msg) for msg in chat_history_list]


def load_vector_store(document_name):
    index_path = f"./assets/{document_name}/index"
    return FAISS.load_local(index_path, embeddings, allow_dangerous_deserialization=True)


def load_all_vector_stores():
    vector_stores = {}
    assets_path = Path("./assets")
    for folder in assets_path.iterdir():
        if folder.is_dir():
            vector_stores[folder.name] = load_vector_store(folder.name)
    return vector_stores


def get_all_folder_names():
    assets_path = Path("./assets")
    folder_names = [folder.name for folder in assets_path.iterdir()
                    if folder.is_dir()]
    return folder_names


vector_stores = load_all_vector_stores()

prompt_template = ChatPromptTemplate.from_messages([
    ("system","""You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know.
Context: {context} 
Answer:"""),
    MessagesPlaceholder(variable_name="chat_history"),
    ("human", "{prompt}")
])


# ChatGroq LLM
qa_chain = LLMChain(llm=ChatGroq(model="llama3-70b-8192", api_key=os.getenv("GROQ_API_KEY")),
                    prompt=prompt_template)

class getAnswer(BaseModel):
    prompt: str
    selected_choice: List[str]
    chat_history: List[any]  # Ensure chat_history is a list
    class Config:
        arbitrary_types_allowed = True


@app.post("/get_answer")
async def get_answer(input: getAnswer):
    print(input)
    prompt = input.prompt
    chat_history = input.chat_history
    selected_choice = input.selected_choice
    selected_vector_stores = [vector_stores[doc] for doc in selected_choice if doc in vector_stores]
    relevant_docs = []
    for store in selected_vector_stores:
        relevant_docs.extend(store.similarity_search(prompt))

    context = ""
    relevant_images = []
    for d in relevant_docs:
        if d.metadata['type'] == 'text':
            context += '[text]' + d.page_content
        elif d.metadata['type'] == 'table':
            context += '[table]' + d.page_content
        elif d.metadata['type'] == 'image':
            context += '[image]' + d.page_content
            relevant_images.append(d.metadata['original'])
    
        # Convert chat_history to the correct format if needed
    # formatted_chat_history = [BaseMessage(**msg) if isinstance(msg, dict) else msg for msg in chat_history]

    result = qa_chain.run({'context': context, 'prompt': prompt, 'chat_history': chat_history})
    # try_images = relevant_docs
    # for d in try_images:
    #     if d.metadata['type'] == 'image':
    
    # print(relevant_images)
    print(result)
    return JSONResponse({"relevant_images": relevant_images, "result": result})


@app.get("/get_index")
async def get_index():
    folder_names = get_all_folder_names()
    return JSONResponse({"folders": folder_names})

# @app.post("/upload_doc")
# INSERT CODE TO STORE '.faiss' and '.pkl' files of uploaded documents in the index folder inside <document name> folder inside assets folder

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=10000)