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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
+
+# ----------------------------- Model Setup -----------------------------
+# Optional patch for custom DepthwiseConv2D
+class PatchedDepthwiseConv2D(DepthwiseConv2D):
+    def __init__(self, *args, groups=1, **kwargs):
+        super().__init__(*args, **kwargs)
+
+# Load pre-trained model
+model = load_model(
+    r"C:\Users\rani ghangare\OneDrive\Documents\Python Skill4Future Session\🌱 Smart Waste Classifier & R-Method Recommender\Smart Waste Classifier & R-Method Recommender\keras_model.h5",
+    compile=False,
+    custom_objects={"DepthwiseConv2D": PatchedDepthwiseConv2D}
+)
+
+# Load class labels and clean them (remove number prefixes)
+with open(r"C:\Users\rani ghangare\OneDrive\Documents\Python Skill4Future Session\🌱 Smart Waste Classifier & R-Method Recommender\Smart Waste Classifier & R-Method Recommender\labels.txt", "r") as f:
+    class_names = [label.strip().split(" ", 1)[-1] for label in f.readlines()]
+
+# Mapping of waste types to R-methods
+r_method_map = {
+    "plastic": "Recycle ♻️",
+    "paper": "Reuse 📄",
+    "glass": "Recycle 🧪",
+    "metal": "Recycle 🛠️",
+    "organic": "Reduce 🌿",
+    "e-waste": "Recycle ⚡",
+    "textile": "Reuse 👕",
+    "cardboard": "Reuse 📦",
+    "hazardous": "Reduce 🚫",
+    "other": "Reduce/Reuse ♻️"
+}
+
+# ----------------------------- Streamlit UI -----------------------------
+st.set_page_config(page_title="🌱 Smart Waste Classifier & R-Method Recommender", page_icon="♻️", layout="centered")
+
+# Sidebar information
+with st.sidebar:
+    st.title("🧭 About This Tool")
+    st.markdown("""
+    Upload an image of waste to identify its type using an AI model and receive a suitable action recommendation:
+    
+    - ♻️ Recycle
+    - 👕 Reuse
+    - 🌿 Reduce
+
+    This tool helps promote **environmentally responsible disposal**.
+    """)
+
+# Title and subtitle (updated)
+st.markdown("<h1 style='text-align: center;'>🌱 Smart Waste Classifier & R-Method Recommender</h1>", unsafe_allow_html=True)
+st.markdown("<h4 style='text-align: center; color: grey;'>An AI-based tool for waste type prediction and sustainable action guidance</h4>", unsafe_allow_html=True)
+st.markdown("---")
+
+# Upload image
+uploaded_file = st.file_uploader("📤 Upload an image of the waste item", type=["jpg", "jpeg", "png"])
+
+# Predict button
+if st.button("🔍 Analyze Image"):
+    if uploaded_file is not None:
+        try:
+            # Open and display image
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Image", use_container_width=True)
+
+            # Preprocess image for model
+            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
+            with st.spinner("Analyzing..."):
+                prediction = model.predict(data)
+                index = np.argmax(prediction)
+                predicted_label = class_names[index].strip()
+                confidence = prediction[0][index]
+
+                # Get R-method (safe fallback)
+                label_key = predicted_label.lower().strip()
+                r_method = r_method_map.get(label_key, "Dispose Responsibly ♻️")
+
+            # Display result
+            st.success("✅ Prediction Successful")
+            col1, col2 = st.columns(2)
+            col1.metric("Waste Type", predicted_label.title())
+            col2.metric("Confidence", f"{confidence * 100:.2f}%")
+
+            st.markdown(f"### 🧭 Recommended Action: <span style='color:green; background:#111;padding:5px;border-radius:4px;'> {r_method} </span>", unsafe_allow_html=True)
+            st.info("Please dispose of this waste responsibly according to local regulations.")
+
+        except Exception as e:
+            st.error(f"❌ Error: Unable to process the image.\n\nDetails: {e}")
+    else:
+        st.warning("⚠️ Please upload an image first.")
+
+# ----------------------------- Footer -----------------------------
+st.markdown("---")
+st.markdown(
+    "<p style='text-align: center;'>Developed with ❤️ by <strong>Twinkle Ghangare</strong> | Supported by 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