Spaces:

deenaik
/

hpmor

Build error

hpmor / src /chat_interface.py

Update README and chat interface to enhance user interaction with Harry Potter character. Improved prompts and example questions for better engagement. Refactored model chain to utilize Harry's personality in responses.

d659883 about 1 year ago

Raw History Blame Contribute Delete

8.92 kB

"""Gradio chat interface for HPMOR Q&A system."""

import gradio as gr
import json
from typing import List, Tuple, Optional
from datetime import datetime

from src.rag_engine import RAGEngine
from src.model_chain import ModelType
from src.config import config


class ChatInterface:
    """Gradio-based chat interface for HPMOR Q&A."""

    def __init__(self):
        """Initialize the chat interface."""
        print("Initializing HPMOR Q&A Chat Interface...")
        self.engine = RAGEngine(force_recreate=False)
        self.conversation_history = []

    def format_sources(self, sources: List[dict]) -> str:
        """Format sources for display."""
        if not sources:
            return "No sources found"

        formatted = []
        for i, source in enumerate(sources, 1):
            formatted.append(
                f"**Source {i}** - Chapter {source['chapter_number']}: {source['chapter_title']}\n"
                f"Relevance Score: {source['score']:.2f}\n"
                f"Preview: *{source['text_preview'][:150]}...*"
            )
        return "\n\n".join(formatted)

    def process_message(
        self,
        message: str,
        history: List[List[str]],
        model_choice: str,
        top_k: int,
        show_sources: bool
    ) -> Tuple[str, str, str]:
        """Process a chat message and return response."""
        if not message:
            return "", "", "Please enter a question."

        # Convert model choice to enum
        model_map = {
            "Auto (Intelligent Routing)": None,
            "Local Small (Fast)": ModelType.LOCAL_SMALL,
            "Local Large (Better)": ModelType.LOCAL_LARGE,
            "Groq API (Best)": ModelType.GROQ_API
        }
        force_model = model_map.get(model_choice)

        # Convert history to messages format
        messages = []
        for user_msg, assistant_msg in history:
            if user_msg:
                messages.append({"role": "user", "content": user_msg})
            if assistant_msg:
                messages.append({"role": "assistant", "content": assistant_msg})
        messages.append({"role": "user", "content": message})

        try:
            # Get response from engine
            response = self.engine.chat(messages, stream=False)

            # Extract answer
            if isinstance(response.get("answer"), str):
                answer = response["answer"]
            else:
                # Handle LlamaIndex response object
                answer = str(response.get("answer", "No response generated"))

            # Format model info
            model_info = f"**Model Used:** {response.get('model_used', 'Unknown')}"
            if response.get("fallback_used"):
                model_info += " (via fallback)"
            model_info += f"\n**Context Size:** {response.get('context_size', 0)} characters"

            # Format sources if requested
            sources_text = ""
            if show_sources and response.get("sources"):
                sources_text = self.format_sources(response["sources"])

            return answer, sources_text, model_info

        except Exception as e:
            error_msg = f"Error: {str(e)}"
            return error_msg, "", "Error occurred"

    def clear_conversation(self):
        """Clear conversation history and cache."""
        self.conversation_history = []
        self.engine.clear_cache()
        return None, "", "", "Conversation cleared"

    def create_interface(self) -> gr.Blocks:
        """Create the Gradio interface."""
        with gr.Blocks(title="HPMOR Q&A System", theme=gr.themes.Soft()) as interface:
            gr.Markdown(
                """
                # 🧙‍♂️ Chat with Harry James Potter-Evans-Verres
                Hello! I'm Harry Potter-Evans-Verres from "Harry Potter and the Methods of Rationality."
                Ask me anything about my adventures, experiments with magic, or my thoughts on rationality and science.
                I'll respond based on my experiences and the scientific method, of course!
                *Powered by RAG with Ollama (local) and Groq API for complex reasoning*
                """
            )

            with gr.Row():
                with gr.Column(scale=2):
                    chatbot = gr.Chatbot(
                        label="Chat",
                        height=500,
                        show_copy_button=True
                    )

                    with gr.Row():
                        msg_input = gr.Textbox(
                            label="Your Question",
                            placeholder="Ask me anything... For example: 'What do you think about magic?' or 'Tell me about your experiments'",
                            lines=2,
                            scale=4
                        )
                        submit_btn = gr.Button("Send", variant="primary", scale=1)

                with gr.Column(scale=1):
                    gr.Markdown("### Settings")

                    model_choice = gr.Radio(
                        choices=[
                            "Auto (Intelligent Routing)",
                            "Local Small (Fast)",
                            "Local Large (Better)",
                            "Groq API (Best)"
                        ],
                        value="Auto (Intelligent Routing)",
                        label="Model Selection"
                    )

                    top_k = gr.Slider(
                        minimum=1,
                        maximum=10,
                        value=5,
                        step=1,
                        label="Number of Context Chunks"
                    )

                    show_sources = gr.Checkbox(
                        value=True,
                        label="Show Sources"
                    )

                    clear_btn = gr.Button("Clear Conversation", variant="secondary")

                    gr.Markdown("### Model Info")
                    model_info = gr.Markdown(
                        value="Ready to answer questions",
                        elem_classes=["model-info"]
                    )

            with gr.Row():
                sources_display = gr.Markdown(
                    label="Retrieved Sources",
                    value="",
                    visible=True
                )

            # Example questions
            gr.Examples(
                examples=[
                    "Harry, how did you first react when you learned magic was real?",
                    "What's your opinion on the way Hogwarts teaches magic?",
                    "Can you explain your scientific experiments with magic?",
                    "What do you think about Hermione?",
                    "How do you apply rationality to magical problems?",
                    "What's your relationship with Professor Quirrell like?",
                ],
                inputs=msg_input,
                label="Example Questions for Harry"
            )

            # Event handlers
            def respond(message, history, model, topk, sources):
                """Handle message submission."""
                answer, sources_text, info = self.process_message(
                    message, history, model, topk, sources
                )
                history.append([message, answer])
                return "", history, sources_text, info

            msg_input.submit(
                respond,
                inputs=[msg_input, chatbot, model_choice, top_k, show_sources],
                outputs=[msg_input, chatbot, sources_display, model_info]
            )

            submit_btn.click(
                respond,
                inputs=[msg_input, chatbot, model_choice, top_k, show_sources],
                outputs=[msg_input, chatbot, sources_display, model_info]
            )

            clear_btn.click(
                lambda: self.clear_conversation(),
                outputs=[chatbot, sources_display, msg_input, model_info]
            )

            # Add custom CSS
            interface.css = """
                .model-info {
                    background-color: #f0f0f0;
                    padding: 10px;
                    border-radius: 5px;
                    font-size: 0.9em;
                }
            """

        return interface

    def launch(self):
        """Launch the Gradio interface."""
        interface = self.create_interface()

        print(f"\nLaunching HPMOR Q&A Chat Interface...")
        print(f"Server will be available at: http://localhost:{config.gradio_server_port}")

        interface.launch(
            server_name="0.0.0.0",
            server_port=config.gradio_server_port,
            share=config.gradio_share,
            favicon_path=None
        )


def main():
    """Launch the chat interface."""
    chat = ChatInterface()
    chat.launch()


if __name__ == "__main__":
    main()

	"""Gradio chat interface for HPMOR Q&A system."""

	import gradio as gr
	import json
	from typing import List, Tuple, Optional
	from datetime import datetime

	from src.rag_engine import RAGEngine
	from src.model_chain import ModelType
	from src.config import config


	class ChatInterface:
	"""Gradio-based chat interface for HPMOR Q&A."""

	def __init__(self):
	"""Initialize the chat interface."""
	print("Initializing HPMOR Q&A Chat Interface...")
	self.engine = RAGEngine(force_recreate=False)
	self.conversation_history = []

	def format_sources(self, sources: List[dict]) -> str:
	"""Format sources for display."""
	if not sources:
	return "No sources found"

	formatted = []
	for i, source in enumerate(sources, 1):
	formatted.append(
	f"Source {i} - Chapter {source['chapter_number']}: {source['chapter_title']}\n"
	f"Relevance Score: {source['score']:.2f}\n"
	f"Preview: {source['text_preview'][:150]}..."
	)
	return "\n\n".join(formatted)

	def process_message(
	self,
	message: str,
	history: List[List[str]],
	model_choice: str,
	top_k: int,
	show_sources: bool
	) -> Tuple[str, str, str]:
	"""Process a chat message and return response."""
	if not message:
	return "", "", "Please enter a question."

	# Convert model choice to enum
	model_map = {
	"Auto (Intelligent Routing)": None,
	"Local Small (Fast)": ModelType.LOCAL_SMALL,
	"Local Large (Better)": ModelType.LOCAL_LARGE,
	"Groq API (Best)": ModelType.GROQ_API
	}
	force_model = model_map.get(model_choice)

	# Convert history to messages format
	messages = []
	for user_msg, assistant_msg in history:
	if user_msg:
	messages.append({"role": "user", "content": user_msg})
	if assistant_msg:
	messages.append({"role": "assistant", "content": assistant_msg})
	messages.append({"role": "user", "content": message})

	try:
	# Get response from engine
	response = self.engine.chat(messages, stream=False)

	# Extract answer
	if isinstance(response.get("answer"), str):
	answer = response["answer"]
	else:
	# Handle LlamaIndex response object
	answer = str(response.get("answer", "No response generated"))

	# Format model info
	model_info = f"Model Used: {response.get('model_used', 'Unknown')}"
	if response.get("fallback_used"):
	model_info += " (via fallback)"
	model_info += f"\nContext Size: {response.get('context_size', 0)} characters"

	# Format sources if requested
	sources_text = ""
	if show_sources and response.get("sources"):
	sources_text = self.format_sources(response["sources"])

	return answer, sources_text, model_info

	except Exception as e:
	error_msg = f"Error: {str(e)}"
	return error_msg, "", "Error occurred"

	def clear_conversation(self):
	"""Clear conversation history and cache."""
	self.conversation_history = []
	self.engine.clear_cache()
	return None, "", "", "Conversation cleared"

	def create_interface(self) -> gr.Blocks:
	"""Create the Gradio interface."""
	with gr.Blocks(title="HPMOR Q&A System", theme=gr.themes.Soft()) as interface:
	gr.Markdown(
	"""
	# 🧙‍♂️ Chat with Harry James Potter-Evans-Verres

	Hello! I'm Harry Potter-Evans-Verres from "Harry Potter and the Methods of Rationality."
	Ask me anything about my adventures, experiments with magic, or my thoughts on rationality and science.
	I'll respond based on my experiences and the scientific method, of course!

	Powered by RAG with Ollama (local) and Groq API for complex reasoning
	"""
	)

	with gr.Row():
	with gr.Column(scale=2):
	chatbot = gr.Chatbot(
	label="Chat",
	height=500,
	show_copy_button=True
	)

	with gr.Row():
	msg_input = gr.Textbox(
	label="Your Question",
	placeholder="Ask me anything... For example: 'What do you think about magic?' or 'Tell me about your experiments'",
	lines=2,
	scale=4
	)
	submit_btn = gr.Button("Send", variant="primary", scale=1)

	with gr.Column(scale=1):
	gr.Markdown("### Settings")

	model_choice = gr.Radio(
	choices=[
	"Auto (Intelligent Routing)",
	"Local Small (Fast)",
	"Local Large (Better)",
	"Groq API (Best)"
	],
	value="Auto (Intelligent Routing)",
	label="Model Selection"
	)

	top_k = gr.Slider(
	minimum=1,
	maximum=10,
	value=5,
	step=1,
	label="Number of Context Chunks"
	)

	show_sources = gr.Checkbox(
	value=True,
	label="Show Sources"
	)

	clear_btn = gr.Button("Clear Conversation", variant="secondary")

	gr.Markdown("### Model Info")
	model_info = gr.Markdown(
	value="Ready to answer questions",
	elem_classes=["model-info"]
	)

	with gr.Row():
	sources_display = gr.Markdown(
	label="Retrieved Sources",
	value="",
	visible=True
	)

	# Example questions
	gr.Examples(
	examples=[
	"Harry, how did you first react when you learned magic was real?",
	"What's your opinion on the way Hogwarts teaches magic?",
	"Can you explain your scientific experiments with magic?",
	"What do you think about Hermione?",
	"How do you apply rationality to magical problems?",
	"What's your relationship with Professor Quirrell like?",
	],
	inputs=msg_input,
	label="Example Questions for Harry"
	)

	# Event handlers
	def respond(message, history, model, topk, sources):
	"""Handle message submission."""
	answer, sources_text, info = self.process_message(
	message, history, model, topk, sources
	)
	history.append([message, answer])
	return "", history, sources_text, info

	msg_input.submit(
	respond,
	inputs=[msg_input, chatbot, model_choice, top_k, show_sources],
	outputs=[msg_input, chatbot, sources_display, model_info]
	)

	submit_btn.click(
	respond,
	inputs=[msg_input, chatbot, model_choice, top_k, show_sources],
	outputs=[msg_input, chatbot, sources_display, model_info]
	)

	clear_btn.click(
	lambda: self.clear_conversation(),
	outputs=[chatbot, sources_display, msg_input, model_info]
	)

	# Add custom CSS
	interface.css = """
	.model-info {
	background-color: #f0f0f0;
	padding: 10px;
	border-radius: 5px;
	font-size: 0.9em;
	}
	"""

	return interface

	def launch(self):
	"""Launch the Gradio interface."""
	interface = self.create_interface()

	print(f"\nLaunching HPMOR Q&A Chat Interface...")
	print(f"Server will be available at: http://localhost:{config.gradio_server_port}")

	interface.launch(
	server_name="0.0.0.0",
	server_port=config.gradio_server_port,
	share=config.gradio_share,
	favicon_path=None
	)


	def main():
	"""Launch the chat interface."""
	chat = ChatInterface()
	chat.launch()


	if __name__ == "__main__":
	main()