Create app.py

app.py

@@ -0,0 +1,36 @@
+
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Load the saved model
+model2 = load_model('/content/shoplifting_model.h5')
+
+def predict_from_npy(npy_file):
+    try:
+        # Load the .npy file
+        data = np.load(npy_file.name, allow_pickle=True)  # Use npy_file.name
+
+        # Reshape the data to (30, 51)
+        reshaped_data = data.reshape(30, 51)  
+
+        # Make predictions
+        prediction = model2.predict(np.expand_dims(reshaped_data, axis=0))
+        threshold = 0.5
+        predicted_class = 'Shoplifting' if prediction[0][0] > threshold else 'Normal'
+
+        return predicted_class
+    except Exception as e:
+        return f"Error: {e}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_from_npy,
+    inputs=gr.File(label="Upload .npy File"),  # Input: File upload
+    outputs="text",  # Output: Text (prediction)
+    title="Shoplifting Prediction from .npy",
+    description="Upload an .npy file containing keypoint data to get a shoplifting prediction."
+)
+
+# Launch the interface
+iface.launch(debug=True)