Create app.py

app.py

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+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+from PIL import Image
+import gradio as gr
+
+# Load the pretrained model and feature extractor
+feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224")
+
+# Define the function to classify images
+def classify_image(image):
+    image = Image.fromarray(image).convert("RGB")  # Convert input image to RGB
+    inputs = feature_extractor(images=image, return_tensors="pt")  # Preprocess image
+    outputs = model(**inputs)  # Get model predictions
+    predicted_class_idx = outputs.logits.argmax(-1).item()  # Get predicted class index
+    return model.config.id2label[predicted_class_idx]  # Return class label
+
+# Create a Gradio app interface
+app = gr.Interface(
+    fn=classify_image,  # Function to run
+    inputs=gr.Image(type="numpy"),  # Input: Image
+    outputs="text",  # Output: Predicted class label
+    title="Image Classification App"  # App title
+)
+
+# Launch the app
+app.launch()