Create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+preprocessor = AutoImageProcessor.from_pretrained("hjay/autotrain-z7ygf-g8xy8")
+model = AutoModelForImageClassification.from_pretrained("hjay/autotrain-z7ygf-g8xy8")
+
+def predict(img_url):
+    response = requests.get(img_url)
+    input_img = Image.open(BytesIO(response.content))
+    
+    inputs = preprocessor(images=input_img, return_tensors="pt")
+    outputs = model(**inputs)
+    
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    
+    return input_img, model.config.id2label[predicted_class_idx]
+
+gradio_app = gr.Interface(
+    predict,
+    inputs="textbox",
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Cat Or Dog?",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()