Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+model_pipeline = pipeline(task="image-classification", model="ppicazo/allsky-stars-detected")
+
+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="Star Detector",
+    allow_flagging="manual",
+).launch()