Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 2,186 Bytes
ae9601e a225caf 2064a53 a225caf 08b3c07 a225caf ae9601e a225caf ae9601e 257403d a225caf 2064a53 a225caf 2064a53 a225caf 257403d a225caf 2064a53 a225caf 2064a53 a225caf 2064a53 a225caf ae9601e a225caf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
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.launch()
|