Update app.py

app.py

@@ -2,23 +2,20 @@ import streamlit as st
 import tensorflow as tf
 from PIL import Image
 import numpy as np
-import tempfile
 import zipfile
 import os
 
-# Function to load model from a zip file
+
+# Function to load the model from the zip file
 def load_model_from_zip(zip_file_path):
-    with tempfile.TemporaryDirectory() as temp_dir:
-        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
-            zip_ref.extractall(temp_dir)
-        
-        # Load the model (assuming your model is 'your_trained_model.keras' inside the zip)
-        model_path = os.path.join(temp_dir, 'your_trained_model.keras')
-        model = tf.keras.models.load_model(model_path)  
+    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+        zip_ref.extractall('.')  # Extract to the current directory
+    # Load the SavedModel (no need for model_path anymore)
+    model = tf.keras.models.load_model('your_trained_model')  # Replace 'your_trained_model' with your model folder name, if any
     return model
 
-# Load the model from the zip file
-model = load_model_from_zip('/content/drive/MyDrive/my_christmas_tree_model.zip') # Replace 'my_christmas_tree_model.zip' with the actual name of the zip file
+# Load the model
+model = load_model_from_zip('my_christmas_tree_model.zip')  # Update with your actual zip file name
 
 # Streamlit UI
 st.title("Christmas Tree Classifier")
@@ -40,7 +37,7 @@ if uploaded_file is not None:
     prediction = model.predict(image_array)
 
     # Interpret prediction (assuming binary classification)
-    class_names = ['Undecorated', 'Decorated'] #Update your class names here
+    class_names = ['Undecorated', 'Decorated']  # Update with your actual class names
     predicted_class_index = 1 if prediction[0][0] >= 0.5 else 0  # Adjust threshold if needed
     predicted_class = class_names[predicted_class_index]