from gradio_client import Client, handle_file import gradio as gr import os MODELS = { "SmolVLM-Instruct": "akhaliq/SmolVLM-Instruct" } def create_chat_fn(client): def chat(message, history): # Extract text and files from the message text = message.get("text", "") files = message.get("files", []) # Handle file uploads if present processed_files = [handle_file(f) for f in files] response = client.predict( message={"text": text, "files": processed_files}, system_prompt="You are a helpful AI assistant.", temperature=0.7, max_new_tokens=1024, top_k=40, repetition_penalty=1.1, top_p=0.95, api_name="/chat" ) return response return chat def set_client_for_session(model_name, request: gr.Request): headers = {} if request and hasattr(request, 'headers'): x_ip_token = request.headers.get('x-ip-token') if x_ip_token: headers["X-IP-Token"] = x_ip_token return Client(MODELS[model_name], headers=headers) def safe_chat_fn(message, history, client): if client is None: return "Error: Client not initialized. Please refresh the page." try: return create_chat_fn(client)(message, history) except Exception as e: print(f"Error during chat: {str(e)}") return f"Error during chat: {str(e)}" with gr.Blocks() as demo: client = gr.State() model_dropdown = gr.Dropdown( choices=list(MODELS.keys()), value="SmolVLM-Instruct", label="Select Model", interactive=True ) chat_interface = gr.ChatInterface( fn=safe_chat_fn, additional_inputs=[client], multimodal=True ) # Update client when model changes model_dropdown.change( fn=set_client_for_session, inputs=[model_dropdown], outputs=[client] ) # Initialize client on page load demo.load( fn=set_client_for_session, inputs=[gr.State("SmolVLM-Instruct")], outputs=[client] ) demo = demo