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| import gradio as gr | |
| from functools import lru_cache | |
| # Cache model loading to optimize performance | |
| def load_hf_model(model_name): | |
| # Use the Gradio-built huggingface loader instead of transformers_gradio | |
| return gr.load( | |
| name=f"deepseek-ai/{model_name}", | |
| src="huggingface", # Changed from transformers_gradio.registry | |
| api_name="/chat" | |
| ) | |
| # Load all models at startup | |
| MODELS = { | |
| "DeepSeek-R1-Distill-Qwen-32B": load_hf_model("DeepSeek-R1-Distill-Qwen-32B"), | |
| "DeepSeek-R1": load_hf_model("DeepSeek-R1"), | |
| "DeepSeek-R1-Zero": load_hf_model("DeepSeek-R1-Zero") | |
| } | |
| # --- Chatbot function --- | |
| def chatbot(input_text, history, model_choice, system_message, max_new_tokens, temperature, top_p): | |
| history = history or [] | |
| # Get the selected model component | |
| model_component = MODELS[model_choice] | |
| # Create payload for the model | |
| payload = { | |
| "messages": [{"role": "user", "content": input_text}], | |
| "system": system_message, | |
| "max_tokens": max_new_tokens, | |
| "temperature": temperature, | |
| "top_p": top_p | |
| } | |
| # Run inference using the selected model | |
| try: | |
| response = model_component(payload) # The response is likely a dictionary | |
| if isinstance(response, dict) and "choices" in response: | |
| # Assuming the response structure is similar to OpenAI's API | |
| assistant_response = response["choices"][0]["message"]["content"] | |
| elif isinstance(response, dict) and "generated_text" in response: | |
| # If the response is in a different format, adjust accordingly | |
| assistant_response = response["generated_text"] | |
| else: | |
| assistant_response = "Unexpected model response format." | |
| except Exception as e: | |
| assistant_response = f"Error: {str(e)}" | |
| # Append user and assistant messages to history | |
| history.append((input_text, assistant_response)) | |
| return history, history, "" | |
| # --- Gradio Interface --- | |
| with gr.Blocks(theme=gr.themes.Soft(), title="DeepSeek Chatbot") as demo: | |
| gr.Markdown( | |
| """ | |
| # DeepSeek Chatbot | |
| Created by [ruslanmv.com](https://ruslanmv.com/) | |
| This is a demo of different DeepSeek models. Select a model, type your message, and click "Submit". | |
| You can also adjust optional parameters like system message, max new tokens, temperature, and top-p. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| chatbot_output = gr.Chatbot(label="DeepSeek Chatbot", height=500) | |
| msg = gr.Textbox(label="Your Message", placeholder="Type your message here...") | |
| with gr.Row(): | |
| submit_btn = gr.Button("Submit", variant="primary") | |
| clear_btn = gr.ClearButton([msg, chatbot_output]) | |
| with gr.Row(): | |
| with gr.Accordion("Options", open=True): | |
| model_choice = gr.Radio( | |
| choices=list(MODELS.keys()), | |
| label="Choose a Model", | |
| value="DeepSeek-R1" | |
| ) | |
| with gr.Accordion("Optional Parameters", open=False): | |
| system_message = gr.Textbox( | |
| label="System Message", | |
| value="You are a friendly Chatbot created by ruslanmv.com", | |
| lines=2, | |
| ) | |
| max_new_tokens = gr.Slider( | |
| minimum=1, maximum=4000, value=200, label="Max New Tokens" | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.10, maximum=4.00, value=0.70, label="Temperature" | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.10, maximum=1.00, value=0.90, label="Top-p (nucleus sampling)" | |
| ) | |
| chat_history = gr.State([]) | |
| # Event handling | |
| submit_btn.click( | |
| chatbot, | |
| [msg, chat_history, model_choice, system_message, max_new_tokens, temperature, top_p], | |
| [chatbot_output, chat_history, msg] | |
| ) | |
| msg.submit( | |
| chatbot, | |
| [msg, chat_history, model_choice, system_message, max_new_tokens, temperature, top_p], | |
| [chatbot_output, chat_history, msg] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |