Update app.py

app.py

@@ -3,20 +3,32 @@ import os
 import numpy as np
 import tensorflow as tf
 from PIL import Image
-import requests
+import zipfile
 
-# Path to your model file (corrected path)
-MODEL_PATH = '/Users/user/Desktop/a.links/your_trained_model.keras.zip'
+# Path to your model zip file in the Hugging Face Space
+MODEL_PATH = '/app/your_trained_model.keras.zip'  # Adjust this path for Hugging Face
 
-# Check if the model file exists and load it
+# Check if the model file exists and load the model
+model = None  # Initialize model variable outside the try block
 if os.path.exists(MODEL_PATH):
-    model = tf.keras.models.load_model(MODEL_PATH)
-    print("Model loaded successfully!")
+    try:
+        # Unzip the model file in the Hugging Face Space directory if needed
+        with zipfile.ZipFile(MODEL_PATH, 'r') as zip_ref:
+            zip_ref.extractall("/app")  # Adjust the extraction directory in Hugging Face Space
+        
+        # Now load the model
+        model = tf.keras.models.load_model("/app/your_trained_model.keras")  # Adjust to the unzipped path
+        print("Model loaded successfully!")
+    except Exception as e:
+        print(f"Error loading model: {e}")
 else:
     print(f"Model file not found at {MODEL_PATH}")
 
-# Define the function to predict decoration
+# If model is not loaded, return an error in the prediction function
 def predict_decoration(image: Image.Image):
+    if model is None:
+        raise ValueError("Model is not loaded, cannot make predictions.")
+
     # Preprocess the image to match the model input format
     image = image.resize((224, 224))  # Resize to match model's expected input size
     image_array = np.array(image) / 255.0  # Normalize the image to [0, 1]
@@ -84,8 +96,11 @@ with tab1:
         st.image(image, caption="Uploaded Tree Image", use_container_width=True)
 
         if st.button("Predict Decoration"):
-            prediction = predict_decoration(image)
-            st.write(f"Prediction: {prediction}")
+            try:
+                prediction = predict_decoration(image)
+                st.write(f"Prediction: {prediction}")
+            except ValueError as e:
+                st.error(str(e))
 
 # Tree Image URLs Tab
 with tab2: