Update app.py

app.py

@@ -1,12 +1,10 @@
 import gradio as gr
 from transformers import pipeline
-import os
 
-# Modelle laden
+# Load models
 vit_classifier = pipeline("image-classification", model="kuhs/vit-base-oxford-iiit-pets")
 clip_detector = pipeline(model="openai/clip-vit-large-patch14", task="zero-shot-image-classification")
 
-# Label-Liste für das CLIP-Modell
 labels_oxford_pets = [
     'Siamese', 'Birman', 'shiba inu', 'staffordshire bull terrier', 'basset hound', 'Bombay', 'japanese chin',
     'chihuahua', 'german shorthaired', 'pomeranian', 'beagle', 'english cocker spaniel', 'american pit bull terrier',
@@ -15,15 +13,6 @@ labels_oxford_pets = [
     'samoyed', 'British Shorthair', 'great pyrenees', 'Abyssinian', 'pug', 'saint bernard', 'Russian Blue', 'scottish terrier'
 ]
 
-# Beispielbilder vorbereiten
-example_images = []
-example_dir = "example_images"
-if os.path.exists(example_dir):
-    for img in os.listdir(example_dir):
-        if img.endswith(('.jpg', '.jpeg', '.png')):
-            example_images.append([os.path.join(example_dir, img)])
-
-# Klassifikationsfunktion
 def classify_pet(image):
     vit_results = vit_classifier(image)
     vit_output = {result['label']: result['score'] for result in vit_results}
@@ -31,21 +20,23 @@ def classify_pet(image):
     clip_results = clip_detector(image, candidate_labels=labels_oxford_pets)
     clip_output = {result['label']: result['score'] for result in clip_results}
     
-    return {
-        "ViT Classification": vit_output,
-        "CLIP Zero-Shot Classification": clip_output
-    }
+    return {"ViT Classification": vit_output, "CLIP Zero-Shot Classification": clip_output}
+
+example_images = [
+    ["example_images/dog1.jpeg"],
+    ["example_images/dog2.jpeg"],
+    ["example_images/leonberger.jpg"],
+    ["example_images/snow_leopard.jpeg"],
+    ["example_images/cat.jpg"]
+]
 
-# Gradio-Interface mit Beispielbildern
 iface = gr.Interface(
     fn=classify_pet,
     inputs=gr.Image(type="filepath"),
     outputs=gr.JSON(),
     title="Pet Classification Comparison",
     description="Upload an image of a pet, and compare results from a trained ViT model and a zero-shot CLIP model.",
-    examples=example_images if example_images else None,
-    cache_examples=False
+    examples=example_images
 )
 
-# App starten
 iface.launch()