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from gradio_client import Client
import gradio as gr
MODELS = {
"SmolVLM-Instruct": "akhaliq/SmolVLM-Instruct"
}
def create_chat_fn(client):
def chat(message, history):
response = client.predict(
message={"text": message, "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, 'request') and hasattr(request.request, 'headers'):
x_ip_token = request.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."
return create_chat_fn(client)(message, history)
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
def update_model(model_name, request):
return set_client_for_session(model_name, request)
model_dropdown.change(
fn=update_model,
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
demo.launch() |