app.py

import gradio as gr
import PyPDF2
import openai
from config import OPENAI_API_KEY
import os
openai.api_key = os.getenv("OPENAI_API_KEY")



class PDFChat:
    def __init__(self):
        self.pdf_text = ""
    
    def extract_text_from_pdf(self, pdf_file):
        """Extract text from PDF file and store it"""
        if not pdf_file:
            return "Please upload a PDF file first."
        
        try:
            self.pdf_text = ""  # Clear previous content
            with open(pdf_file.name, "rb") as file:
                reader = PyPDF2.PdfReader(file)
                for page in reader.pages:
                    self.pdf_text += page.extract_text() + "\n"
            return "PDF loaded successfully! You can now ask questions."
        except Exception as e:
            return f"Error loading PDF: {str(e)}"

    def answer_question(self, question, chat_history):
        """Generate answer based on PDF content and conversation history"""
        if not self.pdf_text:
            return [[question, "Please upload and load a PDF file first."]]

        if not question:
            return chat_history

        # Construct the conversation context
        messages = [
            {"role": "system", "content": "You are a helpful assistant that answers questions based on the PDF content."},
            {"role": "system", "content": f"PDF Content: {self.pdf_text}"}
        ]
        
        # Add conversation history
        for human, assistant in chat_history:
            messages.append({"role": "user", "content": human})
            messages.append({"role": "assistant", "content": assistant})
        
        # Add current question
        messages.append({"role": "user", "content": question})
        
        try:
            response = openai.ChatCompletion.create(
                model="gpt-4-turbo",
                messages=messages
            )
            answer = response.choices[0].message['content']
            
            # Update chat history with new question and answer
            chat_history.append((question, answer))
            return chat_history
        except Exception as e:
            error_message = f"Error generating response: {str(e)}"
            chat_history.append((question, error_message))
            return chat_history

    def clear_history(self):
        """Clear conversation history"""
        return []

css = """
.container {
    max-width: 800px;
    margin: auto;
}
.chat-window {
    height: 600px;
    overflow-y: auto;
}
"""

# Create PDF Chat instance
pdf_chat = PDFChat()

# Create the Gradio interface
with gr.Blocks(css=css, theme='Taithrah/Minimal') as demo:
    gr.Markdown("# Renesas PDF Chatbot")
    
    with gr.Row():
        with gr.Column(scale=2):
            pdf_input = gr.File(
                label="Upload PDF",
                file_types=[".pdf"]
            )
            load_button = gr.Button("Load PDF")
            status_text = gr.Textbox(
                label="Status",
                interactive=False
            )
    
    with gr.Row():
        chatbot = gr.Chatbot(
            [],
            elem_id="chatbot",
            label="Chat History",
            height=400
        )
    
    with gr.Row():
        question_input = gr.Textbox(
            label="Ask a question",
            placeholder="What would you like to know about the PDF?",
            scale=4
        )
        submit_button = gr.Button("Send", scale=1)
        clear_button = gr.Button("Clear History", scale=1)
    
    # Example queries
    gr.Examples(
        examples=[
            ["renesas-ra6m1-group-datasheet.pdf", "Which Renesas products are mentioned in this PDF?"],
            ["renesas-ra6m1-group-datasheet.pdf", "What are the key features of the microcontroller?"],
            ["renesas-ra6m1-group-datasheet.pdf", "Explain the power consumption specifications."]
        ],
        inputs=[pdf_input, question_input],
        label="Example Queries"
    )
    
    # Event handlers
    load_button.click(
        pdf_chat.extract_text_from_pdf,
        inputs=[pdf_input],
        outputs=[status_text]
    )
    
    # Function to clear input after sending
    def clear_input():
        return ""
    
    question_input.submit(
        pdf_chat.answer_question,
        inputs=[question_input, chatbot],
        outputs=[chatbot]
    ).then(
        clear_input,
        outputs=[question_input]
    )
    
    submit_button.click(
        pdf_chat.answer_question,
        inputs=[question_input, chatbot],
        outputs=[chatbot]
    ).then(
        clear_input,
        outputs=[question_input]
    )
    
    clear_button.click(
        pdf_chat.clear_history,
        outputs=[chatbot]
    )

# Launch the interface with sharing enabled
if __name__ == "__main__":
    demo.launch(debug=True)