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app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import DepthwiseConv2D
+from PIL import Image, ImageOps
+import numpy as np
+
+# Optional: Patch DepthwiseConv2D if needed
+class PatchedDepthwiseConv2D(DepthwiseConv2D):
+    def __init__(self, *args, groups=1, **kwargs):
+        super().__init__(*args, **kwargs)
+
+# Load model
+model = load_model(r"D:\garbage\keras_model.h5", compile=False, custom_objects={"DepthwiseConv2D": PatchedDepthwiseConv2D})
+
+# Load class labels
+with open(r"D:\garbage\labels.txt", "r") as f:
+    class_names = f.readlines()
+
+st.title("♻️ Garbage Classification Predictor")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload a waste image (jpg, png)", type=["jpg", "jpeg", "png"])
+
+if st.button("🧪 Predict Waste Type"):
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, use_container_width=True)
+
+
+        # Preprocess image
+        image = image.convert("RGB")
+        image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
+        image_array = np.asarray(image)
+        normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+        data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+        data[0] = normalized_image_array
+
+        # Make prediction
+        prediction = model.predict(data)
+        index = np.argmax(prediction)
+        predicted_label = class_names[index].strip()
+        confidence = prediction[0][index]
+
+        # Display result
+        st.success(f"Predicted Waste Type: **{predicted_label.upper()}**")
+        st.write(f"Confidence Score: **{confidence:.2f}**")
+        st.write("♻️ Dispose responsibly!")
+    else:
+        st.warning("⚠️ Please upload an image before predicting.")
+# 🔚 Footer
+st.markdown("---")
+st.markdown("<p style='text-align: center; font-size: 18px;'>Developed with ❤️ By Twinkle Ghangare for EDUNET FOUNDATION </p>", unsafe_allow_html=True)
+

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d80bf760a260153e1aa76937e4f82183f188a43c703abd09463e59087b1e7883
+size 2456608

labels.txt

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+0 PLASTICS
+1 GLASS
+2 PAPER
+3 METAL
+4 CARDBOARD