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app.py

@@ -3,11 +3,7 @@ import os
 import numpy as np
 import tensorflow as tf
 from PIL import Image
-import io
-import warnings
-
-# Filter warnings (done before loading model and processing)
-warnings.filterwarnings("ignore", category=DeprecationWarning)
+import requests
 
 # Load your pre-trained Keras model (adjust the path as needed)
 MODEL_PATH = "your_trained_model.keras"  # Model file location in Hugging Face Space
@@ -22,21 +18,40 @@ def predict_decoration(image: Image.Image):
 
     # Make prediction
     prediction = model.predict(image_array)
-    # Assuming the model outputs a binary result (decorated or undecorated)
     return "Decorated" if prediction[0] > 0.5 else "Undecorated"
 
 # Set up Streamlit interface
 st.title("Tree Decoration Predictor")
 
-# Upload image through Streamlit
-uploaded_image = st.file_uploader("Upload an image of a tree", type=["jpg", "jpeg", "png"])
-
-if uploaded_image:
-    # Open and display the uploaded image
-    image = Image.open(uploaded_image)
-    st.image(image, caption="Uploaded Tree Image", use_column_width=True)
-
-    # Predict decoration when the button is clicked
-    if st.button("Predict Decoration"):
-        prediction = predict_decoration(image)
-        st.write(f"Prediction: {prediction}")
+# Create tabs for better organization
+tab1, tab2 = st.tabs(["Upload Image", "Tree Image URLs"])
+
+# Upload Image Tab
+with tab1:
+    # Upload image and make prediction
+    uploaded_image = st.file_uploader("Upload an image of a tree", type=["jpg", "jpeg", "png"])
+
+    if uploaded_image:
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Uploaded Tree Image", use_column_width=True)
+
+        if st.button("Predict Decoration"):
+            prediction = predict_decoration(image)
+            st.write(f"Prediction: {prediction}")
+
+# Tree Image URLs Tab
+with tab2:
+    # Fetch tree image URLs
+    st.write("Below is a list of tree image URLs available for testing:")
+    url = 'https://raw.githubusercontent.com/willco-afk/tree-samples/main/tree_images.txt'
+    response = requests.get(url)
+
+    if response.status_code == 200:
+        urls = response.text.splitlines()
+        for url in urls:
+            st.write(url)
+    else:
+        st.write("Failed to fetch the file.")
+
+    # Add download link here in the Tree Image URLs Tab for better context
+    st.markdown("[Download tree_images.txt](https://raw.githubusercontent.com/willco-afk/tree-samples/main/tree_images.txt)")