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| import gradio as gr | |
| import uuid | |
| import json | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # Load the DeepSeek-R1 model | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "deepseek-ai/DeepSeek-R1", | |
| trust_remote_code=True, | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1") | |
| def format_messages_for_model(messages): | |
| """ | |
| Format the conversation history into a single string compatible with the DeepSeek-R1 model. | |
| """ | |
| formatted_messages = "" | |
| for msg in messages: | |
| if msg["role"] == "user": | |
| formatted_messages += f"User: {msg['content']}\n" | |
| elif msg["role"] == "assistant": | |
| formatted_messages += f"DeepSeek-R1: {msg['content']}\n" | |
| return formatted_messages | |
| def generate_response(user_input, messages): | |
| """ | |
| Generate a response using DeepSeek-R1 and return it as a string. | |
| """ | |
| formatted_history = format_messages_for_model(messages) | |
| full_input = f"{formatted_history}User: {user_input}\nDeepSeek-R1: " | |
| inputs = tokenizer(full_input, return_tensors="np") | |
| outputs = model.generate( | |
| **inputs, | |
| max_length=2048, | |
| temperature=0.7, | |
| top_p=0.9, | |
| do_sample=True, | |
| return_dict_in_generate=True, | |
| ) | |
| response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) | |
| return response.split("DeepSeek-R1: ")[-1].strip() | |
| def chatbot( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message: str, | |
| max_tokens: int, | |
| temperature: float, | |
| top_p: float, | |
| ): | |
| """ | |
| Main function for generating responses and saving chat history. | |
| """ | |
| # Generate a unique session ID for this chat session | |
| session_id = str(uuid.uuid4()) | |
| # Initialize the messages with the system message | |
| messages = [] | |
| if system_message: | |
| messages.append({"role": "system", "content": system_message}) | |
| # Add previous chat history | |
| for user_msg, bot_msg in history: | |
| messages.append({"role": "user", "content": user_msg}) | |
| if bot_msg: | |
| messages.append({"role": "assistant", "content": bot_msg}) | |
| # Add the new user message | |
| messages.append({"role": "user", "content": message}) | |
| # Generate response | |
| response = generate_response(message, messages) | |
| # Add the bot's response to messages | |
| messages.append({"role": "assistant", "content": response}) | |
| # Update chat history in the interface | |
| new_history = history + [(message, response)] | |
| # Save chat history to Hugging Face storage | |
| storage = gr.HuggingFace() | |
| storage.set(key=session_id, value={"messages": messages}) | |
| return response, new_history | |
| def create_chat_interface(): | |
| """ | |
| Create the Gradio chat interface. | |
| """ | |
| with gr.Blocks() as app: | |
| session_id = str(uuid.uuid4()) # Generate session ID at startup | |
| with gr.Row(): | |
| chatbot_interface = gr.Chatbot(label="DeepSeek-R1 Chatbot") | |
| input_box = gr.Textbox( | |
| label="Type your message here", | |
| placeholder="Type your message here...", | |
| show_label=True, | |
| ) | |
| # Button to submit the message | |
| submit_button = gr.Button("Send") | |
| # Store chat history in the interface | |
| history = gr.State([]) | |
| # Define the system message | |
| system_message = gr.Textbox( | |
| value="You are DeepSeek-R1, an AI assistant.", | |
| label="System Message", | |
| show_label=True, | |
| visible=True, | |
| ) | |
| # Define inference parameters | |
| with gr.Accordion("Parameters", open=True): | |
| max_tokens = gr.Slider( | |
| minimum=1, | |
| maximum=2048, | |
| value=512, | |
| step=1, | |
| label="Max Tokens", | |
| info="Maximum length of the generated response.", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.0, | |
| maximum=2.0, | |
| value=1.0, | |
| step=0.1, | |
| label="Temperature", | |
| info="Higher values produce more diverse outputs.", | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.9, | |
| step=0.05, | |
| label="Top-p", | |
| info="Controls diversity through nucleus sampling.", | |
| ) | |
| # Define the submit function | |
| submit_button.click( | |
| fn=chatbot, | |
| inputs=[ | |
| input_box, | |
| history, | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ], | |
| outputs=[gr.Textbox(), chatbot_interface], | |
| api_name="chatbot", | |
| ) | |
| return app | |
| if __name__ == "__main__": | |
| app = create_chat_interface() | |
| app.launch() |