import gradio as gr from huggingface_hub import InferenceClient import os from dotenv import load_dotenv # Charger les variables d'environnement load_dotenv() # Vérifie si la clé API est définie dans l'environnement api_token = os.getenv("HF_API_TOKEN") model_name = "mistralai/Mistral-7B-Instruct-v0.3" if not api_token: raise ValueError("API token is required. Please set it in your .env file.") # Initialiser le client d'inférence client = InferenceClient(token=api_token, model=model_name) # Fonction de réponse def respond(message, history, system_message, max_tokens, temperature, top_p): messages = [{"role": "system", "content": system_message}] for user, assistant in history: if user: messages.append({"role": "user", "content": user}) if assistant: messages.append({"role": "assistant", "content": assistant}) messages.append({"role": "user", "content": message}) response = "" try: for message in client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p ): token = message.choices[0].delta.content response += token yield response except Exception as e: yield f"Error: {str(e)}" # Interface utilisateur avec Gradio demo = gr.Blocks() with demo: # Header avec le logo et le slogan with gr.Row(): gr.Image("logo.png", show_label=False, interactive=False, elem_id="logo", scale=1) gr.Markdown( """

SHAURI

Un test avec le modèle Mistral via l'API Hugging Face

""" ) # Composant Chatbot chatbot = gr.Chatbot(label="Chat History") # Zone de saisie utilisateur with gr.Row(): user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...", lines=1) # Réglages du système et paramètres with gr.Row(): system_message = gr.Textbox( value="You are a friendly Chatbot.", label="System Message", lines=2, ) with gr.Row(): max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens") temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature") top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") # Interaction entre l'utilisateur et le chatbot def handle_message(message, history, system_msg, max_toks, temp, top_p_val): if history is None: history = [] for response in respond(message, history, system_msg, max_toks, temp, top_p_val): history.append((message, response)) yield history, "" user_input.submit( fn=handle_message, inputs=[user_input, chatbot, system_message, max_tokens, temperature, top_p], outputs=[chatbot, user_input], ) if __name__ == "__main__": demo.launch()