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 - without any emojis in the history
        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 styling
css = """
body { 
    background-color: #f0f5fb; /* Light pastel blue background */
}
.gradio-container { 
    background-color: white;
    border-radius: 16px;
    box-shadow: 0 2px 10px rgba(0,0,0,0.05);
    max-width: 90%;
    margin: 15px auto;
    padding-bottom: 20px;
}
/* Header styling with diagonal shield */
.app-header {
    position: relative;
    overflow: hidden;
}
.app-header::before {
    content: "🛡️";
    position: absolute;
    font-size: 100px;
    opacity: 0.1;
    right: -20px;
    top: -30px;
    transform: rotate(15deg);
    pointer-events: none;
}
/* Simple styling for buttons */
#send-btn {
    background-color: white !important;
    color: #333 !important;
    border: 2px solid #e6c200 !important;
}
#send-btn:hover {
    background-color: #fff9e6 !important;
}
#clear-btn {
    background-color: white !important;
    color: #333 !important;
    border: 2px solid #e6c200 !important;
}
#clear-btn:hover {
    background-color: #fff9e6 !important;
}
/* Hide elements */
footer {
    display: none !important;
}
.footer {
    display: none !important;
}
"""

with gr.Blocks(css=css) as demo:
    # Custom header with branding
    gr.HTML("""
    <div class="app-header" style="background: linear-gradient(135deg, #4a90e2, #75c6ef); padding: 15px; border-radius: 16px 16px 0 0; color: white; border-bottom: 3px solid #e6c200;">
        <h1 style="font-size: 24px; font-weight: 600; margin: 0; display: flex; align-items: center; font-family: 'Palatino', serif;">
            <span style="margin-right: 10px; font-size: 22px;">🛡️</span>
            <span style="font-weight: 700; margin-right: 5px;">Safe</span>
            <span style="font-weight: 400; letter-spacing: 1px;">Playground</span>
        </h1>
        <p style="font-size: 14px; opacity: 0.9; margin-top: 5px;">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):
            # Using a custom chatbot renderer to add emojis
            chatbot = gr.Chatbot(
                label="Conversation", 
                show_label=True,
                height=400,
                render=False  # We'll render the messages manually
            )
            
            # Custom renderer for chat messages
            def render_chat(history):
                result = []
                for user_msg, bot_msg in history:
                    if user_msg is None:
                        user_msg = ""
                    if bot_msg is None:
                        bot_msg = ""
                    
                    # Add emojis to non-empty messages
                    if user_msg and not user_msg.startswith("👤"):
                        user_msg = f"👤 {user_msg}"
                    if bot_msg and not bot_msg.startswith("🛡️"):
                        bot_msg = f"🛡️ {bot_msg}"
                        
                    result.append((user_msg, bot_msg))
                return result
            
            # Custom chatbot with emoji rendering
            chatbot.render = render_chat
            
            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,
                    elem_id="send-btn"
                )
            
            with gr.Row():
                clear_button = gr.Button("Clear Chat", elem_id="clear-btn")
    
    # Define functions for chatbot interactions
    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):
        # Ensure there's history
        if not history or len(history) == 0:
            return history
            
        # Get the last user message from 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 - submit and send button work the same way
    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
    def clear_history():
        return []
    
    clear_button.click(clear_history, None, chatbot, queue=False)

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