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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example_images/burger.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,49 @@
+#! pip install gradio
+import gradio as gr
+from transformers import pipeline
+
+# Lade beide Modelle
+vit_classifier = pipeline("image-classification", model="chrisis2/vit-food-classification-chrisis2")  # Ersetze mit deinem Modell
+clip_detector = pipeline(model="openai/clip-vit-large-patch14", task="zero-shot-image-classification")
+
+# Verwende deine Food-Labels
+food_labels = [
+    "Baked Potato", "Crispy Chicken", "Donut", "Fries", "Hot Dog", "Sandwich", "Taco", "Taquito",
+    "apple_pie", "burger", "butter_naan", "chai", "chapati", "cheesecake", "chicken_curry",
+    "chole_bhature", "dal_makhani", "dhokla", "fried_rice", "ice_cream", "idli", "jalebi",
+    "kaathi_rolls", "kadai_paneer", "kulfi", "masala_dosa", "momos", "omelette", "paani_puri",
+    "pakode", "pav_bhaji", "pizza", "samosa", "sushi"
+]
+
+def classify_food(image):
+    # Klassifiziere mit deinem trainierten Modell
+    vit_results = vit_classifier(image)
+    vit_output = {result['label']: result['score'] for result in vit_results}
+    
+    # Klassifiziere mit CLIP Zero-Shot
+    clip_results = clip_detector(image, candidate_labels=food_labels)
+    clip_output = {result['label']: result['score'] for result in clip_results}
+    
+    return {"Trainiertes ViT-Modell": vit_output, "CLIP Zero-Shot": clip_output}
+
+# Füge Beispielbilder aus deinem Dataset hinzu
+example_images = [
+    ["gradio-food-app/example_images/burger.jpg.jpg"],
+    ["gradio-food-app/example_images/burger.jpg.jpg"],
+    ["gradio-food-app/example_images/burger.jpg.jpg"],
+    ["gradio-food-app/example_images/pizza.jpg.jpg"],
+    ["gradio-food-app/example_images/sushi.jpg.jpg"],
+    # Füge weitere Beispiele hinzu
+]
+
+# Erstelle die Gradio-Oberfläche
+iface = gr.Interface(
+    fn=classify_food,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.JSON(),
+    title="Food Classification Vergleich",
+    description="Lade ein Bild eines Lebensmittels hoch und vergleiche die Ergebnisse eines trainierten ViT-Modells mit einem Zero-Shot CLIP-Modell.",
+    examples=example_images
+)
+
+iface.launch()

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch