Update app.py

app.py

@@ -1,15 +1,28 @@
-import zipfile
-import os
-import tensorflow as tf
+import streamlit as st
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
 
-# Path to the zipped model file in Hugging Face
-zip_model_path = '/app/your_trained_model.keras.zip'
-unzipped_model_path = '/app/your_trained_model.keras'
+# Load your trained model
+model = load_model('your_model.keras')
 
-# Unzip the model file if not already unzipped
-if not os.path.exists(unzipped_model_path):
-    with zipfile.ZipFile(zip_model_path, 'r') as zip_ref:
-        zip_ref.extractall('/app')
+# Streamlit UI
+st.title('Tree Decoration Prediction')
+st.write("Upload an image of a tree for decoration prediction:")
 
-# Load the model
-model = tf.keras.models.load_model(unzipped_model_path)
+uploaded_image = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_image is not None:
+    image = Image.open(uploaded_image)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Preprocess image (resize, normalize, etc. based on your model)
+    image = image.resize((224, 224))  # Example resizing
+    image = np.array(image) / 255.0  # Example normalization
+    image = np.expand_dims(image, axis=0)
+
+    # Get prediction
+    prediction = model.predict(image)
+    st.write(f"Prediction: {'Decorated' if prediction[0][0] > 0.5 else 'Undecorated'}")