Delete app.py

app.py

@@ -1,48 +0,0 @@
-import os
-import streamlit as st
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-from huggingface_hub import login, hf_hub_download
-
-# Authenticate with Hugging Face token (if available)
-hf_token = os.environ.get("HF_TOKEN") 
-if hf_token:
-    login(token=hf_token)
-
-# Download and load the model from the Hugging Face Hub
-repo_id = os.environ.get("MODEL_ID", "willco-afk/tree-test-x")  # Get repo ID from secret or default
-filename = "your_trained_model.keras"  # Updated filename
-cache_dir = "./models"  # Local directory to cache the model
-os.makedirs(cache_dir, exist_ok=True)  
-model_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
-
-# Load the model
-model = tf.keras.models.load_model(model_path) 
-
-# Streamlit UI
-st.title("Christmas Tree Classifier")
-st.write("Upload an image of a Christmas tree to classify it:")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Display the uploaded image
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
-    st.write("")
-    st.write("Classifying...")
-
-    # Preprocess the image
-    image = image.resize((224, 224))  # Resize to match your model's input size
-    image_array = np.array(image) / 255.0  # Normalize pixel values
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
-
-    # Make prediction
-    prediction = model.predict(image_array)
-    
-    # Get predicted class
-    predicted_class = "Decorated" if prediction[0][0] >= 0.5 else "Undecorated"
-
-    # Display the prediction
-    st.write(f"Prediction: {predicted_class}")