import gradio as gr
import requests
import os

# Title and description
title = "🧠 Claude-3.7-Sonnet-Reasoning-Gemma3-12B Chat"
description = """
Chat with the Claude-3.7-Sonnet-Reasoning-Gemma3-12B model using Hugging Face Inference API.
"""

# Hugging Face API setup
API_URL = "https://router.huggingface.co/models/reedmayhew/claude-3.7-sonnet-reasoning-gemma3-12B"
headers = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN', '')}"}

def query_hf_api(payload):
    """Query Hugging Face Inference API"""
    try:
        response = requests.post(API_URL, headers=headers, json=payload)
        return response.json()
    except Exception as e:
        return {"error": str(e)}

def chat_with_claude(message, chat_history):
    """Chat function using HF Inference API"""
    
    # Build conversation context
    conversation = ""
    for msg in chat_history:
        if msg["role"] == "user":
            conversation += f"Human: {msg['content']}\n"
        else:
            conversation += f"Assistant: {msg['content']}\n"
    
    conversation += f"Human: {message}\nAssistant:"
    
    # Call the API
    payload = {
        "inputs": conversation,
        "parameters": {
            "max_new_tokens": 500,
            "temperature": 0.7,
            "top_p": 0.9,
            "do_sample": True
        }
    }
    
    response = query_hf_api(payload)
    
    if "error" in response:
        assistant_response = f"❌ API Error: {response['error']}"
    elif isinstance(response, list) and len(response) > 0:
        # Extract the generated text
        if "generated_text" in response[0]:
            full_text = response[0]["generated_text"]
            # Extract only the assistant's response
            assistant_response = full_text.split("Assistant:")[-1].strip()
        else:
            assistant_response = "❌ Unexpected response format from API"
    else:
        assistant_response = "❌ No response from API"
    
    # Update chat history
    chat_history.append({"role": "user", "content": message})
    chat_history.append({"role": "assistant", "content": assistant_response})
    
    return "", chat_history

def clear_chat():
    """Clear the chat history"""
    return []

# Create the Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(f"# {title}")
    gr.Markdown(description)
    
    # Add HF Token info
    gr.Markdown("""
    **Note:** To use this chat, you need to add your Hugging Face token in the Space settings:
    1. Go to Settings → Repository secrets
    2. Add `HF_TOKEN` with your Hugging Face token
    """)
    
    chatbot = gr.Chatbot(
        label="Chat with Claude",
        height=500,
        type="messages"  # Fixed the deprecation warning
    )
    
    with gr.Row():
        msg = gr.Textbox(
            label="Type your message here...",
            placeholder="Ask me anything...",
            lines=2,
            scale=4,
            container=False
        )
        submit_btn = gr.Button("Send", variant="primary", scale=1)
    
    with gr.Row():
        clear_btn = gr.Button("Clear Chat", variant="secondary")
    
    # Example questions
    gr.Examples(
        examples=[
            "Explain quantum computing in simple terms",
            "Write a Python function to calculate fibonacci sequence",
            "What are the benefits of renewable energy?",
            "How does machine learning work?"
        ],
        inputs=msg
    )
    
    # Event handlers
    def handle_submit(message, chat_history):
        if not message.strip():
            return "", chat_history
        return chat_with_claude(message, chat_history)
    
    submit_btn.click(
        fn=handle_submit,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot]
    )
    
    msg.submit(
        fn=handle_submit,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot]
    )
    
    clear_btn.click(
        fn=clear_chat,
        outputs=chatbot
    )
    
    gr.Markdown("""
    ### Tips:
    - Be specific with your questions for better responses
    - The model is good at reasoning and explanation tasks
    - Responses may take a few seconds to generate
    """)

# Launch the app
if __name__ == "__main__":
    demo.launch()