app.py

import gradio as gr
from transformers import OwlViTProcessor, OwlViTForObjectDetection
from PIL import Image

# Load the OWL-ViT model and processor
processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")

# Define the prediction function
def predict(image):
    image = Image.open(image)
    text_queries = ["A photo of a pokemon", "a photo of a human face", "a photo of a couch"]  # Example queries
    
    # Prepare inputs for the model
    inputs = processor(text=text_queries, images=image, return_tensors="pt")
    # Perform inference
    outputs = model(**inputs)
    
    # Format the response (dummy response as example)
    response = {"message": "Detection successful!"}
    return response

# Create a Gradio interface and enable the API mode
interface = gr.Interface(
    fn=predict,
    inputs="image",
    outputs="json",
    allow_flagging="never",
    live=True  # Allows the API endpoint to remain active
)

# Launch the interface with API mode enabled
interface.launch(server_name="0.0.0.0", server_port=7860, share=True)