app.py

import os
import gradio as gr
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory

from langchain.prompts import PromptTemplate



openai_api_key = os.environ.get("OPENAI_API_KEY")

class AdvancedPdfChatbot:
    def __init__(self, openai_api_key):
        os.environ["OPENAI_API_KEY"] = openai_api_key
        self.embeddings = OpenAIEmbeddings()
        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        self.llm =  ChatOpenAI(temperature=0,model_name='gpt-4o-mini')
        
        self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
        self.qa_chain = None
        self.pdf_path = None
        self.template = """
Imagine you are a chat assistant for knowledge retrieval, specializing in providing detailed information with a deep understanding of context.
Your goal is to generate responses in a structured format that is both informative and engaging.

        """
        self.prompt = PromptTemplate(template=self.template, input_variables=["context", "question"])

    def load_and_process_pdf(self, pdf_path):
        loader = PyPDFLoader(pdf_path)
        documents = loader.load()
        texts = self.text_splitter.split_documents(documents)
        self.db = FAISS.from_documents(texts, self.embeddings)
        self.pdf_path = pdf_path
        self.setup_conversation_chain()

    def setup_conversation_chain(self):
        self.qa_chain = ConversationalRetrievalChain.from_llm(
            self.llm,
            retriever=self.db.as_retriever(),
            memory=self.memory,
            combine_docs_chain_kwargs={"prompt": self.prompt}
        )

    def chat(self, query):
        if not self.qa_chain:
            return "Please upload a PDF first."
        result = self.qa_chain({"question": query})
        return result['answer']

    def get_pdf_path(self):
        # Return the stored PDF path
        if self.pdf_path:
            return self.pdf_path
        else:
            return "No PDF uploaded yet."

# Initialize the chatbot
pdf_chatbot = AdvancedPdfChatbot(openai_api_key)

def upload_pdf(pdf_file):
    if pdf_file is None:
        return "Please upload a PDF file."
    file_path = pdf_file.name
    pdf_chatbot.load_and_process_pdf(file_path)
    return file_path

def respond(message, history):
    bot_message = pdf_chatbot.chat(message)
    history.append((message, bot_message))
    return "", history

def clear_chatbot():
    pdf_chatbot.memory.clear()
    return []

def get_pdf_path():
    # Call the method to return the current PDF path
    return pdf_chatbot.get_pdf_path()

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# PDF Chatbot")
    
    with gr.Row():
        pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
        upload_button = gr.Button("Process PDF")

    upload_status = gr.Textbox(label="Upload Status")
    upload_button.click(upload_pdf, inputs=[pdf_upload], outputs=[upload_status])
    path_button = gr.Button("Get PDF Path")
    pdf_path_display = gr.Textbox(label="Current PDF Path")
    chatbot_interface = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")

    msg.submit(respond, inputs=[msg, chatbot_interface], outputs=[msg, chatbot_interface])
    clear.click(clear_chatbot, outputs=[chatbot_interface])
    path_button.click(get_pdf_path, outputs=[pdf_path_display])

if __name__ == "__main__":
    demo.launch()