Create app.py

app.py

@@ -0,0 +1,22 @@
+from transformers import pipeline
+import gradio as gr
+
+# Step 1: Load a pre-trained model for image classification
+model = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+# Step 2: Define a function for classifying images
+def classify_image(image):
+    predictions = model(image)
+    return predictions
+
+# Step 3: Create a Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs="image",  # Input is an image
+    outputs="label",  # Output is a label (e.g., "sitting", "standing")
+    title="Pose Detection: Sitting or Standing"
+)
+
+# Step 4: Launch the app
+if __name__ == "__main__":
+    interface.launch()