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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from typing import List, Tuple | |
| # Initialize the InferenceClient with the model you want to use | |
| client = InferenceClient("microsoft/phi-4") | |
| # Define the system message (non-editable) | |
| SYSTEM_MESSAGE = "You're an advanced AI assistant designed to engage in friendly and informative conversations. Your role is to respond to user queries with helpful, clear, and concise answers, while maintaining a conversational tone. You can provide advice, explanations, and solutions based on user input." | |
| def generate_response( | |
| user_input: str, | |
| history: List[Tuple[str, str]], | |
| max_tokens: int, | |
| temperature: float, | |
| top_p: float | |
| ) -> str: | |
| """ | |
| Generates a response from the AI model. | |
| Args: | |
| user_input: The user's input message. | |
| history: A list of tuples containing the conversation history | |
| (user input, AI response). | |
| max_tokens: The maximum number of tokens in the generated response. | |
| temperature: Controls the randomness of the generated response. | |
| top_p: Controls the nucleus sampling probability. | |
| Returns: | |
| str: The generated response from the AI model. | |
| """ | |
| try: | |
| # Build the message list with system message and history | |
| messages = [{"role": "system", "content": SYSTEM_MESSAGE}] | |
| messages.extend([{"role": "user" if i % 2 == 0 else "assistant", "content": val} | |
| for i, val in enumerate(sum(history, ()))]) | |
| messages.append({"role": "user", "content": user_input}) | |
| # Generate response from the model | |
| response = "" | |
| for msg in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| if 'choices' in msg and len(msg['choices']) > 0: | |
| token = msg['choices'][0].get('delta', {}).get('content', '') | |
| if token: | |
| response += token | |
| return response | |
| except Exception as e: | |
| print(f"An error occurred: {e}") | |
| return "Error: An unexpected error occurred while processing your request." | |
| # Define the Gradio Interface | |
| iface = gr.Interface( | |
| fn=generate_response, | |
| inputs=[ | |
| gr.Textbox(lines=2, label="Your Message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"), | |
| gr.Chatbot(label="Conversation") | |
| ], | |
| outputs=[gr.Textbox(label="AI Response")], | |
| title="Chat with AI", | |
| description="Interact with an AI assistant that engages in friendly and informative conversations.", | |
| ) | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| iface.launch() | |