Update app.py

app.py

@@ -1,28 +1,16 @@
+import tensorflow as tf
 import streamlit as st
-from tensorflow.keras.models import load_model
-import numpy as np
-from PIL import Image
+import os
 
-# Load your trained model
-model = load_model('your_model.keras')
+# Load the model
+model = tf.keras.models.load_model('your_model.keras')
 
-# Streamlit UI
+# Streamlit app interface
 st.title('Tree Decoration Prediction')
-st.write("Upload an image of a tree for decoration prediction:")
 
-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'}")
+# Example usage in your Streamlit app
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png"])
+if uploaded_image:
+    st.image(uploaded_image, caption="Uploaded Image.", use_column_width=True)
+    prediction = model.predict(uploaded_image)
+    st.write(f"Prediction: {prediction}")