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| 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 | |
| "Zephyr Beta": "HuggingFaceH4/zephyr-7b-beta", | |
| "Another Model": "HuggingFaceH4/zephyr-7b-beta" | |
| } | |
| def respond(message, history, system_message, max_tokens, temperature, top_p, selected_model): | |
| # Look up the model ID from our list based on the dropdown selection | |
| model_id = model_list.get(selected_model, "HuggingFaceH4/zephyr-7b-beta") | |
| # Create an InferenceClient for the selected model | |
| client = InferenceClient(model_id) | |
| # Build the conversation history into the message list | |
| messages = [{"role": "system", "content": system_message}] | |
| for user_msg, assistant_msg in history or []: | |
| 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}) | |
| 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, | |
| ): | |
| token = token_message.choices[0].delta.content | |
| response += token | |
| yield response | |
| # CSS styling: pastel backgrounds, gentle light colors, and rounded corners for a safe vibe | |
| css = """ | |
| body { background-color: #FAF3E0; } | |
| .gradio-container { background-color: #FFFFFF; border-radius: 16px; padding: 20px; } | |
| button, input, .gradio-dropdown, .gradio-slider, textarea { border-radius: 16px; } | |
| .gradio-chat { border-radius: 16px; } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| 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" | |
| ) | |
| # Main area: Chat interface and settings | |
| with gr.Column(scale=3): | |
| gr.Markdown("## Chat Interface") | |
| chatbot = gr.Chatbot(label="Chat with your Model") | |
| user_input = gr.Textbox(placeholder="Enter your message...", label="Your Message") | |
| with gr.Row(): | |
| send_button = gr.Button("Send") | |
| clear_button = gr.Button("Clear Chat") | |
| gr.Markdown("### Chat Settings") | |
| system_message = gr.Textbox(value="You are a friendly Chatbot.", label="System Message") | |
| 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)") | |
| # When "Send" is clicked, run the respond() function and update the chat interface. | |
| send_button.click( | |
| fn=respond, | |
| inputs=[user_input, chatbot, system_message, max_tokens_slider, temperature_slider, top_p_slider, model_dropdown], | |
| outputs=[user_input, chatbot], | |
| ) | |
| # Clear the chat history when "Clear Chat" is clicked. | |
| clear_button.click(lambda: None, None, chatbot, queue=False) | |
| if __name__ == "__main__": | |
| demo.launch() | |