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import streamlit as st
import tensorflow as tf
from tensorflow.keras.applications.efficientnet import preprocess_input
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image
# Sidebar: show TensorFlow version
st.sidebar.write(f"TensorFlow version: {tf.__version__}")
# Load model with error handling
@st.cache_resource
def load_model():
try:
model = tf.keras.models.load_model("garbage_classifier_efficientnetv2b2.keras")
st.sidebar.success("✅ Model loaded successfully!")
return model
except Exception as e:
st.sidebar.error(f"❌ Error loading model: {str(e)}")
return None
model = load_model()
class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
# Title and instructions
st.title("🗑️ Garbage Classifier with Camera")
st.write("Upload an image or take a photo to classify the type of garbage.")
# Upload OR Camera input
uploaded_file = st.file_uploader("📂 Upload an image...", type=["jpg", "jpeg", "png"])
camera_file = st.camera_input("📸 Or take a photo")
image_input = camera_file if camera_file is not None else uploaded_file
# Prediction
if image_input is not None and model is not None:
img = Image.open(image_input).convert("RGB")
st.image(img, caption="Input Image", use_column_width=True)
with st.spinner("🔍 Analyzing..."):
img_resized = img.resize((224, 224))
img_array = image.img_to_array(img_resized)
img_array = preprocess_input(img_array)
img_array = np.expand_dims(img_array, axis=0)
predictions = model.predict(img_array, verbose=0)
predicted_class_idx = np.argmax(predictions)
predicted_class = class_names[predicted_class_idx]
confidence = float(predictions[0][predicted_class_idx])
st.markdown(f"### 🧠 Prediction: **{predicted_class.title()}**")
st.markdown(f"**Confidence:** {confidence:.2%}")
if confidence < 0.75:
st.warning("🤔 Confidence is low. This might be a mixed or unclear image.")
st.info("Tip: Make sure the image is clear and focused.")
# Show all class probabilities
st.markdown("### 📊 All Class Probabilities:")
for name, prob in zip(class_names, predictions[0]):
st.write(f"**{name.title()}**: {prob:.2%}")
# Feedback form
with st.expander("📝 Give Feedback"):
feedback = st.text_area("Was the prediction correct? If not, tell us the correct type or leave a suggestion.")
if st.button("Submit Feedback"):
st.success("💌 Thank you for your feedback!")
elif image_input is not None and model is None:
st.error("⚠️ Model could not be loaded. Please check logs.")
# About Section
with st.expander("ℹ️ About this App"):
st.markdown("""
This app uses a pre-trained **EfficientNetV2-B2** model to classify waste into six categories:
- 📦 Cardboard
- 🥃 Glass
- 🔩 Metal
- 📄 Paper
- 🥤 Plastic
- 🗑️ Trash
Built with ❤️ by Sampu using TensorFlow and Streamlit.
""")
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