app.py

import gradio as gr
from transformers import pipeline
from PIL import Image

model_pipeline = pipeline(task="image-classification", model="bortle/moon-detector-v5.a")

def predict(image):
    # Resize the image to have width 1080 while keeping aspect ratio
    width = 1080
    ratio = width / image.width
    height = int(image.height * ratio)
    resized_image = image.resize((width, height))
    
    # Perform predictions
    predictions = model_pipeline(resized_image)
    
    # Return predictions as a dictionary
    return {p["label"]: p["score"] for p in predictions}

# Define the Gradio Interface
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload image"),
    outputs=gr.Label(num_top_classes=5),
    title="Moon Detector",
    allow_flagging="manual",
).launch()