Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+from PIL import Image
+import torch
+
+# Load your Hugging Face model
+model_id = "KabeerAmjad/food_classification_model"  # Replace with your actual model ID
+model = AutoModelForImageClassification.from_pretrained(model_id)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+# Define the prediction function
+def classify_image(img):
+    inputs = feature_extractor(images=img, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=-1)
+    
+    # Get the label with the highest probability
+    top_label = model.config.id2label[probs.argmax().item()]
+    return top_label
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Food Image Classification",
+    description="Upload an image to classify if it’s an apple pie, etc."
+)
+
+# Launch the app
+iface.launch()