app.py

import gradio as gr
from gradio_client import Client, handle_file
import requests
from PIL import Image
import io

def predict(image, question, seed, top_p, temperature):
    # Inicializa o cliente do Gradio
    client = Client("deepseek-ai/Janus-Pro-7B")
    
    # Prepara a imagem para envio
    if image.startswith('http'):
        response = requests.get(image)
        img_path = handle_file(io.BytesIO(response.content))
    else:
        img_path = handle_file(image)
    
    # Faz a predição
    result = client.predict(
        image=img_path,
        question=question,
        seed=seed,
        top_p=top_p,
        temperature=temperature,
        api_name="/multimodal_understanding"
    )
    
    return result

# Componentes da interface
image_input = gr.Image(label="Upload Image", type="filepath")
question_input = gr.Textbox(label="Question", placeholder="Ask something about the image...")
seed_slider = gr.Slider(0, 100, value=42, label="Seed")
top_p_slider = gr.Slider(0, 1, value=0.95, label="Top-p")
temp_slider = gr.Slider(0, 1, value=0.1, label="Temperature")

# Cria a interface
demo = gr.Interface(
    fn=predict,
    inputs=[
        image_input,
        question_input,
        seed_slider,
        top_p_slider,
        temp_slider
    ],
    outputs=gr.Textbox(label="Answer"),
    title="Janus-Pro-7B Multimodal Demo",
    description="Ask questions about images using the Janus-Pro-7B model",
    examples=[
        ["https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", "What's in this image?", 42, 0.95, 0.1]
    ]
)

if __name__ == "__main__":
    demo.launch()