from huggingface_hub import InferenceClient import gradio as gr def hf_chat(api_key, system_prompt, user_prompt, temperature, max_tokens, top_p): try: # Initialize the Hugging Face Inference Client client = InferenceClient(api_key=api_key) # Prepare the messages messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ] # Stream the response from the Hugging Face model stream = client.chat.completions.create( model="Qwen/Qwen2.5-72B-Instruct", messages=messages, temperature=temperature, max_tokens=max_tokens, top_p=top_p, stream=True ) # Concatenate the streamed content output = "" for chunk in stream: output += chunk.choices[0].delta.content return output except Exception as e: return f"Error: {str(e)}" # Define the Gradio interface with gr.Blocks() as demo: gr.Markdown("""# history prof it is my first appli. i am bad in history but to help me for my homework i makeing a ai.""") api_key_input = gr.Textbox(label="Hugging Face API Key", placeholder="Enter your Hugging Face API key", type="password") system_prompt_input = gr.Textbox(label="ethan's history prof", value="You are a history professor 5e in FRENCH.", placeholder="you are a history professor 5e in FRENCH.") user_prompt_input = gr.Textbox(label="user chat", placeholder="metez votre question ici.") temperature_slider = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.7, step=0.1) max_tokens_slider = gr.Slider(label="Max Tokens", minimum=10, maximum=2048, value=100, step=10) top_p_slider = gr.Slider(label="Top P", minimum=0.1, maximum=1.0, value=0.7, step=0.1) output = gr.Textbox(label="history prof 👨‍🏫") generate_button = gr.Button("📖") generate_button.click( hf_chat, inputs=[ api_key_input, system_prompt_input, user_prompt_input, temperature_slider, max_tokens_slider, top_p_slider ], outputs=[output] ) if __name__ == "__main__": demo.launch() #Merci papa de m'avoir offert cet ordi ❤️❤️❤️