Create app.py

app.py

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+import streamlit as st
+from image_classification import classify_image  # Import from image_classification.py
+
+# Title and description for your app
+st.title("Image Classifier")
+st.write("This app classifies uploaded images using a pre-trained TensorFlow model.")
+
+# Allow users to upload an image
+uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+  # Convert uploaded image to a format suitable for classification
+  image = cv2.imdecode(np.frombuffer(uploaded_image.read(), np.uint8), cv2.IMREAD_COLOR)  # Assuming OpenCV is used for conversion
+
+  # Perform classification using your function
+  try:
+      predicted_label, probability = classify_image(image)
+      st.write("Classified as:")
+      st.write(f"- {predicted_label}: {probability:.4f}")
+  except Exception as e:  # Catch potential errors during classification
+      st.error(f"Error during classification: {e}")
+else:
+  st.write("Upload an image to classify it.")