app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Define available models (update with your actual model IDs)
model_list = {
    "Safe LM": "HuggingFaceH4/zephyr-7b-beta",  # Replace with your Safe LM model ID
    "Baseline 1": "HuggingFaceH4/zephyr-7b-beta",
    "Another Model": "HuggingFaceH4/zephyr-7b-beta"
}

def respond(message, history, system_message, max_tokens, temperature, top_p, selected_model):
    try:
        # Create an InferenceClient for the selected model
        client = InferenceClient(model_list.get(selected_model, "HuggingFaceH4/zephyr-7b-beta"))
        
        # Build conversation messages for the client
        messages = [{"role": "system", "content": system_message}]
        for user_msg, assistant_msg in history:
            if user_msg:  # Only add non-empty messages
                messages.append({"role": "user", "content": user_msg})
            if assistant_msg:  # Only add non-empty messages
                messages.append({"role": "assistant", "content": assistant_msg})
        messages.append({"role": "user", "content": message})
        
        response = ""
        
        # Stream the response from the client
        for token_message in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            # Safe extraction of token with error handling
            try:
                token = token_message.choices[0].delta.content
                if token is not None:  # Handle potential None values
                    response += token
                    yield response
            except (AttributeError, IndexError) as e:
                # Handle cases where token structure might be different
                print(f"Error extracting token: {e}")
                continue
    except Exception as e:
        # Return error message if the model call fails
        print(f"Error calling model API: {e}")
        yield f"Sorry, there was an error: {str(e)}"

# Custom CSS for branding with consistent styling and hiding footer
css = """
body {
    background-color: #f0f5fb !important; /* Light pastel blue background */
}

/* Gold button styling */
.gold-button {
    background: white !important;
    color: #333 !important;
    border: 2px solid #e6c200 !important;
}
.gold-button:hover {
    background: #fff9e6 !important;
    box-shadow: 0 2px 5px rgba(230, 194, 0, 0.2) !important;
}

/* Message styling - no backgrounds */
.message {
    background-color: transparent !important;
}
.message p {
    background-color: transparent !important;
}
.message-wrap {
    background-color: transparent !important;
}

/* Emoji for messages */
.user .message::before {
    content: "👤 ";
    font-size: 16px;
    margin-right: 5px;
}
.bot .message::before {
    content: "🛡️ ";
    font-size: 16px;
    margin-right: 5px;
}

/* Hide footer */
footer {
    display: none !important;
}
"""



with gr.Blocks(theme=gr.themes.Default(
    primary_hue="blue",
    secondary_hue="gold",
    font=["Crimson Pro", "Palatino", "serif"]
), css=css) as demo:
    # Custom header with branding - removed the hr element
    gr.HTML("""
    <div class="app-header">
        <h1 class="app-title">
            <span class="shield">🛡️</span>
            <span class="safe">Safe</span>
            <span class="lm">Playground</span>
        </h1>
        <p class="app-subtitle">Responsible AI for everyone</p>
    </div>
    """)
        
    with gr.Row():
        # Left sidebar: Model selector
        with gr.Column(scale=1):
            gr.Markdown("## Models")
            model_dropdown = gr.Dropdown(
                choices=list(model_list.keys()),
                label="Select Model",
                value="Safe LM",
                elem_classes=["model-select"]
            )
            
            # Settings
            gr.Markdown("### Settings")
            system_message = gr.Textbox(
                value="You are a friendly and safe assistant.",
                label="System Message",
                lines=2
            )
            max_tokens_slider = gr.Slider(
                minimum=1, maximum=2048, value=512, step=1, 
                label="Max New Tokens"
            )
            temperature_slider = gr.Slider(
                minimum=0.1, maximum=4.0, value=0.7, step=0.1, 
                label="Temperature"
            )
            top_p_slider = gr.Slider(
                minimum=0.1, maximum=1.0, value=0.95, step=0.05, 
                label="Top-p (nucleus sampling)"
            )
            
        # Main area: Chat interface
        with gr.Column(scale=3):
            chatbot = gr.Chatbot(
                label="Conversation", 
                show_label=True,
                # Removed avatar_images which may not be supported in some Gradio versions
            elem_id="chatbot",
                height=500
            )
            with gr.Row():
                user_input = gr.Textbox(
                    placeholder="Type your message here...", 
                    label="Your Message",
                    show_label=False,
                    scale=9
                )
                send_button = gr.Button(
                    "Send", 
                    scale=1,
                    variant="primary"
                )
            
            with gr.Row():
                clear_button = gr.Button("Clear Chat", variant="secondary", elem_classes=["gold-button"])
    
    # No footer - removed
    gr.HTML('<div style="height: 20px;"></div>')
    
    # Fix 1: Correct event handling for the chatbot interface
    def user(user_message, history):
        # Return the user's message and add it to history
        return "", history + [[user_message, None]]
    
    def bot(history, system_message, max_tokens, temperature, top_p, selected_model):
                    # Get the last user message from history (with error checking)
        if not history or len(history) == 0:
            return history
        user_message = history[-1][0]
        # Call respond function with the message
        response_generator = respond(
            user_message, 
            history[:-1],  # Pass history without the current message
            system_message, 
            max_tokens, 
            temperature, 
            top_p, 
            selected_model
        )
        # Update history as responses come in
        for response in response_generator:
            history[-1][1] = response
            yield history
    
    # Wire up the event chain - use queue=True for the bot responses
    user_input.submit(
        user,
        [user_input, chatbot],
        [user_input, chatbot],
        queue=False
    ).then(
        bot,
        [chatbot, system_message, max_tokens_slider, temperature_slider, top_p_slider, model_dropdown],
        [chatbot],
        queue=True
    )
    
    send_button.click(
        user,
        [user_input, chatbot],
        [user_input, chatbot],
        queue=False
    ).then(
        bot,
        [chatbot, system_message, max_tokens_slider, temperature_slider, top_p_slider, model_dropdown],
        [chatbot],
        queue=True
    )
    
    # Clear the chat history - use a proper function instead of lambda
    def clear_history():
        return []
    
    clear_button.click(clear_history, None, chatbot, queue=False)

if __name__ == "__main__":
    # Simple launch without parameters that might not be supported
    demo.launch()