app.py

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

# Load your model pipeline
model_pipeline = pipeline(
    task="image-classification", 
    model="bortle/astrophotography-object-classifier-alpha5"
)

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 Astrophotography image"),
    outputs=gr.Label(num_top_classes=5),
    title="Astrophotography Object Classifier",
    allow_flagging="manual",
    examples=[
        "examples/Andromeda.jpg", "examples/Heart.jpg", "examples/Pleiades.jpg",
        "examples/Rosette.jpg", "examples/Moon.jpg", "examples/GreatHercules.jpg",
        "examples/Leo-Triplet.jpg", "examples/Crab.jpg", "examples/North-America.jpg",
        "examples/Horsehead-Flame.jpg", "examples/Pinwheel.jpg", "examples/Saturn.jpg"
    ],
    cache_examples=True
).launch()