Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# 1. Page Configuration
+st.set_page_config(page_title="Intel Scene Classifier", page_icon="🌲", layout="centered")
+
+# Custom layer loader to bypass the quantization_config error on Hugging Face
+class SafeDense(tf.keras.layers.Dense):
+    def __init__(self, *args, **kwargs):
+        # Remove the problematic argument if it exists in newer Keras versions
+        kwargs.pop('quantization_config', None)
+        super().__init__(*args, **kwargs)
+
+# 2. Cache and Load the Full Model Safely
+@st.cache_resource
+def load_my_model():
+    # We pass SafeDense to bypass version mismatch errors automatically
+    custom_objects = {'Dense': SafeDense}
+    try:
+        # Replace 'intel_scene_model.h5' with your exact model filename if different
+        return tf.keras.models.load_model('intel_scene_model.h5', custom_objects=custom_objects)
+    except Exception:
+        # Fallback if your model file uses the newer .keras format extension
+        return tf.keras.models.load_model('intel_scene_model.keras', custom_objects=custom_objects)
+
+with st.spinner("Loading CNN Model... Please wait"):
+    model = load_my_model()
+
+# Class names sorted exactly as in the dataset
+CLASS_NAMES = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']
+
+# 3. User Interface
+st.title("🌲 Landscape Classification using CNN")
+st.write("Upload any landscape image, and the model will instantly identify and classify it with high accuracy.")
+
+# Image Upload Tool
+uploaded_file = st.file_uploader("Choose an image (JPG, JPEG, PNG)...", 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_container_width=True)
+    
+    st.write("---")
+    with st.spinner("Analyzing image and predicting class..."):
+        # 4. Image Preprocessing
+        img_resized = image.convert('RGB').resize((150, 150))
+        img_array = np.array(img_resized) / 255.0  # Rescaling
+        img_array = np.expand_dims(img_array, axis=0)  # Add Batch dimension
+        
+        # 5. Model Inference & Prediction
+        predictions = model.predict(img_array)
+        highest_class_idx = np.argmax(predictions[0])
+        confidence = predictions[0][highest_class_idx] * 100
+        predicted_class = CLASS_NAMES[highest_class_idx]
+
+    # 6. Display Results Dynamically
+    st.success(f"**Final Prediction:** This landscape represents **{predicted_class.upper()}**")
+    st.metric(label="Confidence Level", value=f"{confidence:.2f}%")
+    
+    # Visualizing prediction distribution across all classes
+    st.subheader("Classification Probability Distribution:")
+    for name, pred in zip(CLASS_NAMES, predictions[0]):
+        st.write(f"**{name.capitalize()}:**")
+        st.progress(float(pred))