import os
import gradio as gr
import logging
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain, LLMChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from PyPDF2 import PdfReader

class ContextAwareResponseGenerator:
    def __init__(self, llm):
        self.llm = llm
        self.response_prompt = PromptTemplate(
            input_variables=['context', 'query', 'chat_history'],
            template="""Analyze the context, query, and chat history to generate an optimal response:

Context: {context}
Query: {query}
Chat History: {chat_history}

Response Structure Selection Criteria:
1. Technical academic breakdown
2. Concise summary with key points
3. Markdown with hierarchical insights
4. Narrative explanation
5. Comparative analysis

Choose the most appropriate response structure (1-5) and generate the response accordingly:"""
        )
        self.response_chain = LLMChain(llm=self.llm, prompt=self.response_prompt)

    def generate_response(self, context, query, chat_history=''):
        try:
            # Generate structured response
            full_response = self.response_chain.run({
                'context': context,
                'query': query,
                'chat_history': chat_history or "No previous context"
            })
            
            # Extract only the actual response content (after the structure selection)
            response_content = full_response[1:].strip()
            
            return response_content
        except Exception as e:
            logging.error(f"Response generation error: {e}")
            return f"I couldn't generate a response for: {query}"

class AdvancedPdfChatbot:
    def __init__(self, openai_api_key):
        os.environ["OPENAI_API_KEY"] = openai_api_key
        self.llm = ChatOpenAI(temperature=0.2, model_name='gpt-4o')
        
        self.embeddings = OpenAIEmbeddings()
        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        
        self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
        self.response_generator = ContextAwareResponseGenerator(self.llm)
        
        self.db = None
        self.document_context = ""

    def load_and_process_pdf(self, pdf_path):
        try:
            reader = PdfReader(pdf_path)
            metadata = {
                "title": reader.metadata.get("/Title", "Untitled"),
                "author": reader.metadata.get("/Author", "Unknown")
            }
            
            loader = PyPDFLoader(pdf_path)
            documents = loader.load()
            texts = self.text_splitter.split_documents(documents)
            
            self.db = FAISS.from_documents(texts[:50], self.embeddings)
            self.document_context = f"Document: {metadata['title']} by {metadata['author']}"
            
            return True
        except Exception as e:
            logging.error(f"PDF processing error: {e}")
            return False

    def chat(self, query):
        if not self.db:
            return "Please upload a PDF first."
        
        # Retrieve chat history
        chat_history = self.memory.load_memory_variables({}).get('chat_history', [])
        
        # Generate context-aware response
        response = self.response_generator.generate_response(
            context=self.document_context, 
            query=query, 
            chat_history=str(chat_history)
        )
        
        # Store conversation in memory
        self.memory.save_context({"input": query}, {"output": response})
        
        return response

# Gradio Interface
pdf_chatbot = AdvancedPdfChatbot(os.environ.get("OPENAI_API_KEY"))

def upload_pdf(pdf_file):
    if not pdf_file:
        return "Upload a PDF file."
    file_path = pdf_file.name if hasattr(pdf_file, 'name') else pdf_file
    return "PDF processed successfully" if pdf_chatbot.load_and_process_pdf(file_path) else "Processing failed"

def respond(message, history):
    try:
        bot_message = pdf_chatbot.chat(message)
        history.append((message, bot_message))
        return "", history
    except Exception as e:
        return f"Error: {e}", history

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# Advanced 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])
    
    chatbot_interface = gr.Chatbot()
    msg = gr.Textbox(placeholder="Enter your query...")
    msg.submit(respond, inputs=[msg, chatbot_interface], outputs=[msg, chatbot_interface])

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