Spaces:
Running
Running
| import os | |
| from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| # Load Hugging Face token from .env | |
| hf_token = os.getenv("HF_TOKEN") | |
| # Define available models | |
| models = { | |
| "Llama-3.3-70B-Instruct": "meta-llama/llama-3.3-70B-instruct", | |
| "QwQ-32B-Preview":"Qwen/QwQ-32B-Preview", | |
| "Qwen2.5-Coder-32B-Instruct": "qwen/qwen2.5-coder-32B-instruct", | |
| "Mistral-Nemo-Instruct-2407": "mistralai/Mistral-Nemo-Instruct-2407", | |
| "Hermes-3-Llama-3.2-3B":"NousResearch/Hermes-3-Llama-3.2-3B", | |
| "Phi-3-mini-4k-instruct": "microsoft/phi-3-mini-4k-instruct", | |
| } | |
| # Initialize the InferenceClient with a selected model | |
| def get_inference_client(selected_model): | |
| return InferenceClient( | |
| models[selected_model], | |
| token=hf_token, | |
| ) | |
| # Function to get a response from the chatbot | |
| def get_response(user_input, history, selected_model, system_prompt, temperature, max_tokens, top_p): | |
| client = get_inference_client(selected_model) | |
| # messaages | |
| messages = [] | |
| # Add system message, if not empty | |
| if (len(system_prompt)) > 0: | |
| messages = [{"role": "system", "content": system_prompt}] | |
| # Include previous conversation history | |
| for h in history: | |
| messages.append({"role": h['role'], "content": h['content']}) | |
| # Add the current user input to the messages | |
| messages.append({"role": "user", "content": user_input}) | |
| # Get response from the model | |
| response = client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ) | |
| bot_response = response.choices[0].message.content | |
| history.append({"role": "user", "content": user_input}) | |
| history.append({"role": "assistant", "content": bot_response}) | |
| return history | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| # Set the type to 'messages' to avoid the deprecation warning | |
| chatbot = gr.Chatbot(type="messages") | |
| with gr.Row(): | |
| user_input = gr.Textbox(show_label=False, placeholder="Enter your message...") | |
| send_button = gr.Button("Send") | |
| with gr.Column(scale=1): | |
| with gr.Accordion("Settings", open=False): | |
| # Model selection | |
| selected_model = gr.Dropdown(choices=list(models.keys()), label="Select Model", value="Llama-3.3-70B-Instruct") | |
| # Chat settings | |
| system_prompt = gr.Textbox(value="You are a friendly and open-minded chatbot.", label="System Prompt (Optional)", lines=5) | |
| temperature = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Temperature") | |
| max_tokens = gr.Slider(minimum=10, maximum=8192, step=10, value=250, label="Max Tokens") | |
| top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.9, label="Top-p") | |
| # Chatbot interaction | |
| def submit_message(user_input, history, selected_model, system_prompt, temperature, max_tokens, top_p): | |
| # Get updated history including user input and bot response | |
| history = get_response(user_input, history, selected_model, system_prompt, temperature, max_tokens, top_p) | |
| return "", history | |
| # Set up the send button click functionality | |
| send_button.click( | |
| submit_message, | |
| [user_input, chatbot, selected_model, system_prompt, temperature, max_tokens, top_p], | |
| [user_input, chatbot] | |
| ) | |
| # Trigger sending message when Enter key is pressed | |
| user_input.submit( | |
| submit_message, | |
| [user_input, chatbot, selected_model, system_prompt, temperature, max_tokens, top_p], | |
| [user_input, chatbot] | |
| ) | |
| # Launch the Gradio interface | |
| demo.launch() | |