Create app.py

app.py

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+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import gradio as gr
+import io
+
+# Load model and feature extractor
+def load_model():
+    processor = AutoImageProcessor.from_pretrained("therealcyberlord/stanford-car-vit-patch16")
+    model = AutoModelForImageClassification.from_pretrained("therealcyberlord/stanford-car-vit-patch16")
+    return processor, model
+
+processor, model = load_model()
+
+# Function to classify image
+def classify_image(image):
+    # Convert image if necessary
+    if not isinstance(image, Image.Image):
+        image = Image.open(io.BytesIO(image)).convert("RGB")
+        
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        labels = model.config.id2label
+        predicted_class = labels[predicted_class_idx]
+    return predicted_class
+
+# Define Gradio Interface
+app = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Car Classification",
+    description="Upload a car image to classify its model."
+)
+
+# Launch the app
+app.launch()