Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+
+# Load the pre-trained model
+model = load_model('your_trained_model_resnet50.keras')
+
+# Streamlit app title
+st.title("Tree Decoration Prediction")
+
+# Upload image for prediction
+uploaded_file = st.file_uploader("Choose a tree image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display uploaded image
+    img = Image.open(uploaded_file)
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    # Prepare the image for prediction
+    img = img.resize((224, 224))  # Resizing image for ResNet50 input size
+    img_array = np.array(img) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    # Predict the class
+    prediction = model.predict(img_array)
+
+    # Show the prediction result
+    if prediction[0] > 0.5:
+        st.write("The tree is decorated!")
+    else:
+        st.write("The tree is undecorated!")