Update app.py

app.py

@@ -1,27 +1,38 @@
 import gradio as gr
 from transformers import pipeline
+from PIL import Image
 
-pipeline = pipeline(task="image-classification", model="bortle/astrophotography-object-classifier-alpha5")
+# Load your model pipeline
+model_pipeline = pipeline(
+    task="image-classification", 
+    model="bortle/astrophotography-object-classifier-alpha5"
+)
 
 def predict(image):
-  predictions = pipeline(image)
-  return {p["label"]: p["score"] for p in predictions}
-
-
-def process_image(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))
-    return resized_image
+    
+    # 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(
-    predict,
-    fn=process_image,
+    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"],
+    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()
+).launch()