app.py

import os
from typing import List
from chainlit.types import AskFileResponse
from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
from aimakerspace.openai_utils.prompts import (
    UserRolePrompt,
    SystemRolePrompt,
    AssistantRolePrompt,
)
from aimakerspace.openai_utils.embedding import EmbeddingModel
from aimakerspace.vectordatabase import VectorDatabase
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
import chainlit as cl
import tempfile
import pandas as pd
import pdfplumber

system_template = """\
Use the following context to answer the user's question. If you cannot find the answer in the context, 
say you don't know the answer. Additionally, if the user requests a summary or context overview, 
generate an engaging and concise summary that captures the main ideas with an interesting and appealing tone.

"""
system_role_prompt = SystemRolePrompt(system_template)

user_prompt_template = """\
Context:
{context}

Question:
{question}
"""
user_role_prompt = UserRolePrompt(user_prompt_template)

class RetrievalAugmentedQAPipeline:
    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
        self.llm = llm
        self.vector_db_retriever = vector_db_retriever

    async def arun_pipeline(self, user_query: str):
        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)

        context_prompt = ""
        for context in context_list:
            context_prompt += context[0] + "\n"

        formatted_system_prompt = system_role_prompt.create_message()

        formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)

        async def generate_response():
            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
                yield chunk

        return {"response": generate_response(), "context": context_list}

text_splitter = CharacterTextSplitter()

def process_text_file(file: AskFileResponse):
    with tempfile.NamedTemporaryFile(mode="wb", delete=False, suffix=".txt") as temp_file:
        temp_file_path = temp_file.name
        temp_file.write(file.read)

    text_loader = TextFileLoader(temp_file_path)
    documents = text_loader.load_documents()
    texts = text_splitter.split_texts(documents)
    return texts

def process_pdf_file(file: AskFileResponse):
    # Use the path attribute to read the file directly
    temp_file_path = file.path  # Get the path of the uploaded file

    extracted_text = ""
    with pdfplumber.open(temp_file_path) as pdf:
        for page in pdf.pages:
            extracted_text += page.extract_text()

    texts = text_splitter.split_texts([extracted_text])
    return texts



def process_csv_file(file: AskFileResponse):
    with tempfile.NamedTemporaryFile(mode="wb", delete=False, suffix=".csv") as temp_file:
        temp_file_path = temp_file.name
        temp_file.write(file.content)

    df = pd.read_csv(temp_file_path)
    texts = df.apply(lambda row: ' '.join(row.astype(str)), axis=1).tolist()
    return text_splitter.split_texts(texts)

@cl.on_chat_start
async def on_chat_start():
    cl.user_session.set("all_texts", [])

    files = await cl.AskFileMessage(
        content="Please upload one or more Text, PDF, or CSV files to begin!",
        accept=["text/plain", "application/pdf", "text/csv"],
        max_size_mb=20,
        timeout=180,
    ).send()

    if not files:
        await cl.Message(content="No files were uploaded. Please upload at least one file to proceed.").send()
        return

    all_texts = cl.user_session.get("all_texts", [])

    for file in files:
        file_type = file.name.split(".")[-1].lower()

        msg = cl.Message(content=f"Processing `{file.name}`...")
        await msg.send()


        # Process each file based on its type
        if file_type == "txt":
            texts = process_text_file(file)
        elif file_type == "pdf":
            texts = process_pdf_file(file)
        elif file_type == "csv":
            texts = process_csv_file(file)
        else:
            await cl.Message(content=f"Unsupported file type: `{file.name}`. Please upload text, PDF, or CSV files.").send()
            continue

        all_texts.extend(texts)  # Combine texts from all uploaded files

    cl.user_session.set("all_texts", all_texts)

    await cl.Message(content="Files processed! You can now start asking questions.").send()

@cl.on_message
async def main(message):
    chain = cl.user_session.get("chain")

    if not chain:
        all_texts = cl.user_session.get("all_texts")
        if not all_texts:
            await cl.Message(content="Please upload at least one file before asking questions.").send()
            return

        # Create a dict vector store
        vector_db = VectorDatabase()
        vector_db = await vector_db.abuild_from_list(all_texts)
        
        chat_openai = ChatOpenAI()

        # Create a chain
        retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
            vector_db_retriever=vector_db,
            llm=chat_openai
        )

        cl.user_session.set("chain", retrieval_augmented_qa_pipeline)
        chain = retrieval_augmented_qa_pipeline

    msg = cl.Message(content="")
    result = await chain.arun_pipeline(message.content)

    async for stream_resp in result["response"]:
        await msg.stream_token(stream_resp)

    await msg.send()