Create app.py

app.py

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+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+from PIL import Image
+import gradio as gr
+
+# Step 1: Load the model and feature extractor
+feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224")
+
+# Step 2: Define the function for prediction
+def recognize_image(image):
+    # Convert the input image to RGB
+    image = Image.fromarray(image).convert("RGB")
+    
+    # Preprocess the image
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    
+    # Make predictions
+    outputs = model(**inputs)
+    predicted_class_idx = outputs.logits.argmax(-1).item()
+    
+    # Get the predicted class label
+    return model.config.id2label[predicted_class_idx]
+
+# Step 3: Create a Gradio interface
+app = gr.Interface(
+    fn=recognize_image,                 # Prediction function
+    inputs=gr.Image(type="numpy"),      # Input: Image
+    outputs="text",                     # Output: Predicted label
+    title="Image Recognition App"       # App title
+)
+
+# Launch the app
+app.launch()