Update app.py

app.py

@@ -21,28 +21,46 @@ model_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache
 model = tf.keras.models.load_model(model_path) 
 
 # Streamlit UI
-st.title("Christmas Tree Classifier")
-st.write("Upload an image of a Christmas tree to classify it:")
+tab1, tab2 = st.tabs(["Christmas Tree Classifier", "Sample Image Links"])
 
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+# Tab 1: Christmas Tree Classifier
+with tab1:
+    st.title("Christmas Tree Classifier")
+    st.write("Upload an image of a Christmas tree to classify it:")
 
-if uploaded_file is not None:
-    # Display the uploaded image
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
-    st.write("")
-    st.write("Classifying...")
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-    # Preprocess the image
-    image = image.resize((224, 224))  # Resize to match your model's input size
-    image_array = np.array(image) / 255.0  # Normalize pixel values
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image.", use_column_width=True)
+        st.write("")
+        st.write("Classifying...")
 
-    # Make prediction
-    prediction = model.predict(image_array)
+        # Preprocess the image
+        image = image.resize((224, 224))  # Resize to match your model's input size
+        image_array = np.array(image) / 255.0  # Normalize pixel values
+        image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+
+        # Make prediction
+        prediction = model.predict(image_array)
+        
+        # Get predicted class
+        predicted_class = "Decorated" if prediction[0][0] >= 0.5 else "Undecorated"
+
+        # Display the prediction
+        st.write(f"Prediction: {predicted_class}")
+
+# Tab 2: Sample Image Links
+with tab2:
+    st.title("Sample Image Links and Placeholder Texts")
     
-    # Get predicted class
-    predicted_class = "Decorated" if prediction[0][0] >= 0.5 else "Undecorated"
+    # Display 50 placeholder texts (as links) for sample tree images
+    st.write("Here are 50 placeholder links for sample tree images:")
+
+    for i in range(1, 51):
+        st.write(f"[Sample Tree Image {i}](https://www.example.com/sample-tree-image-{i})")
 
-    # Display the prediction
-    st.write(f"Prediction: {predicted_class}")
+    st.write("\nAdditional Links:")
+    st.write("[View sample images here](https://www.example.com/sample-images)")
+    st.write("[Download sample images here](https://www.example.com/download-sample-images)")