Create app.py

app.py

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+from transformers import ViTImageProcessor, ViTForImageClassification
+import torch
+import gradio as gr
+from PIL import Image
+
+# Load general ViT model (ImageNet pretrained)
+model_name = "google/vit-base-patch16-224"
+processor = ViTImageProcessor.from_pretrained(model_name)
+model = ViTForImageClassification.from_pretrained(model_name)
+
+def predict(image):
+    if image is None:
+        return "Please upload an image."
+    
+    # Preprocess image
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+
+    probs = torch.nn.functional.softmax(logits, dim=1)
+    conf, predicted_class = torch.max(probs, dim=1)
+    label = model.config.id2label[predicted_class.item()]
+    confidence = conf.item() * 100
+
+    # This label will be a general ImageNet class, e.g. "banana", "bee", "daisy"
+    return f"Detected class: {label}\nConfidence: {confidence:.2f}%"
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="General Image Classification with ViT",
+    description="Upload an image to classify using ViT pretrained on ImageNet."
+).launch()
+