Update app.py

app.py

@@ -1,108 +1,52 @@
-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
-)
+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"keras_model.h5", compile=False, custom_objects={"DepthwiseConv2D": PatchedDepthwiseConv2D})
+
+# Load class labels
+with open(r"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)