app.py

import gradio as gr
import os
import requests
import threading
from datetime import datetime
from typing import List, Dict, Any, Generator
from session_manager import SessionManager

# Initialize session manager and get HF API key
session_manager = SessionManager()
HF_API_KEY = os.getenv("HF_API_KEY")

# Model endpoints configuration
MODEL_ENDPOINTS = {
    "Qwen2.5-72B-Instruct": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct",
    "Llama3.3-70B-Instruct": "https://api-inference.huggingface.co/models/meta-llama/Llama-3.3-70B-Instruct",
    "Qwen2.5-Coder-32B-Instruct": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-32B-Instruct",
}

def query_model(model_name: str, messages: List[Dict[str, str]]) -> str:
    """Query a single model with the chat history"""
    endpoint = MODEL_ENDPOINTS[model_name]
    headers = {
        "Authorization": f"Bearer {HF_API_KEY}",
        "Content-Type": "application/json"
    }
    
    # Build full conversation history for context
    conversation = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
    
    # Model-specific prompt formatting with full history
    model_prompts = {
        "Qwen2.5-72B-Instruct": (
            f"<|im_start|>system\nCollaborate with other experts. Previous discussion:\n{conversation}<|im_end|>\n"
            "<|im_start|>assistant\nMy analysis:"
        ),
        "Llama3.3-70B-Instruct": (
            "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n"
            f"Build upon this discussion:\n{conversation}<|eot_id|>\n"
            "<|start_header_id|>assistant<|end_header_id|>\nMy contribution:"
        ),
        "Qwen2.5-Coder-32B-Instruct": (
            f"<|im_start|>system\nTechnical discussion context:\n{conversation}<|im_end|>\n"
            "<|im_start|>assistant\nTechnical perspective:"
        )
    }

    # Model-specific stop sequences
    stop_sequences = {
        "Qwen2.5-72B-Instruct": ["<|im_end|>", "<|endoftext|>"],
        "Llama3.3-70B-Instruct": ["<|eot_id|>", "\nuser:"],
        "Qwen2.5-Coder-32B-Instruct": ["<|im_end|>", "<|endoftext|>"]
    }

    payload = {
        "inputs": model_prompts[model_name],
        "parameters": {
            "max_tokens": 2048,
            "temperature": 0.7,
            "stop_sequences": stop_sequences[model_name],
            "return_full_text": False
        }
    }
    
    try:
        response = requests.post(endpoint, json=payload, headers=headers)
        response.raise_for_status()
        result = response.json()[0]['generated_text']
        # Clean up response formatting
        result = result.split('<|')[0]  # Remove any remaining special tokens
        result = result.replace('**', '').replace('##', '')  # Remove markdown
        result = result.strip()  # Remove leading/trailing whitespace
        return result  # Return complete response
    except Exception as e:
        return f"{model_name} error: {str(e)}"

def respond(message: str, history: List[List[str]], session_id: str) -> str:
    """Handle sequential model responses with context preservation"""
    # Load or initialize session
    session = session_manager.load_session(session_id)
    if not isinstance(session, dict) or "history" not in session:
        session = {"history": []}
    
    # Build context from session history
    messages = []
    for entry in session["history"]:
        if entry["type"] == "user":
            messages.append({"role": "user", "content": entry["content"]})
        else:
            messages.append({"role": "assistant", "content": f"{entry['model']}: {entry['content']}"})
    
    # Add current message
    messages.append({"role": "user", "content": message})
    session["history"].append({
        "timestamp": datetime.now().isoformat(),
        "type": "user",
        "content": message
    })
    
    responses = []
    
    # First model response
    response1 = query_model("Qwen2.5-Coder-32B-Instruct", messages)
    session["history"].append({
        "timestamp": datetime.now().isoformat(),
        "type": "assistant",
        "model": "Qwen2.5-Coder-32B-Instruct",
        "content": response1
    })
    messages.append({"role": "assistant", "content": f"Qwen2.5-Coder-32B-Instruct: {response1}"})
    responses.append(f"🔵 **Qwen2.5-Coder-32B-Instruct**\n{response1}")
    
    # Second model response
    response2 = query_model("Qwen2.5-72B-Instruct", messages)
    session["history"].append({
        "timestamp": datetime.now().isoformat(),
        "type": "assistant",
        "model": "Qwen2.5-72B-Instruct",
        "content": response2
    })
    messages.append({"role": "assistant", "content": f"Qwen2.5-72B-Instruct: {response2}"})
    responses.append(f"🟣 **Qwen2.5-72B-Instruct**\n{response2}")
    
    # Final model response
    response3 = query_model("Llama3.3-70B-Instruct", messages)
    session["history"].append({
        "timestamp": datetime.now().isoformat(),
        "type": "assistant",
        "model": "Llama3.3-70B-Instruct",
        "content": response3
    })
    messages.append({"role": "assistant", "content": f"Llama3.3-70B-Instruct: {response3}"})
    responses.append(f"🟡 **Llama3.3-70B-Instruct**\n{response3}")
    
    # Save final session state
    session_manager.save_session(session_id, session)
    
    # Return responses
    return "\n\n".join(responses)

# Custom CSS for styling
css = """
.message { padding: 15px; margin: 10px 0; border-radius: 10px; }
.assistant { background: #f8fafc; border-left: 4px solid #3b82f6; }
.user { background: #eff6ff; border-left: 4px solid #60a5fa; }
.model-name { font-weight: 600; color: #1e40af; margin-bottom: 8px; }
.thinking { color: #6b7280; font-style: italic; }
"""

# Create the Gradio interface
demo = gr.ChatInterface(
    fn=respond,
    title="Multi-LLM Collaboration Chat",
    description="Experience collaborative AI thinking with three powerful language models",
    examples=[
        ["Explain how quantum computing works"],
        ["Write a Python function to find prime numbers"],
    ],
    additional_inputs=[gr.State(session_manager.create_session)],
    chatbot=gr.Chatbot(
        height=600,
        show_label=False,
        bubble_full_width=False,
        show_copy_button=True,
        container=True,
        sanitize_html=False,
        render_markdown=True
    ),
    theme=gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="indigo",
        neutral_hue="slate",
        font=("Inter", "sans-serif"),
    ),
    css=css,
)

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