Upload 7 files

best_waste_classification_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0092396cc692370cf36693bd81ece842fca56c897b6b4bfa1b74fc8fb0620b51
+size 12924688

classifier.py

@@ -0,0 +1,48 @@
+
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+class WasteClassifier:
+    def __init__(self, model_path, labels_path):
+        # Load the TFLite model
+        self.interpreter = tf.lite.Interpreter(model_path=model_path)
+        self.interpreter.allocate_tensors()
+        
+        # Get input and output details
+        self.input_details = self.interpreter.get_input_details()
+        self.output_details = self.interpreter.get_output_details()
+        
+        # Load labels
+        with open(labels_path, 'r') as f:
+            self.labels = [line.strip().split(':')[1] for line in f.readlines()]
+    
+    def preprocess_image(self, image_path):
+        img = Image.open(image_path).convert('RGB')
+        img = img.resize((224, 224))
+        img_array = np.array(img).astype(np.float32) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        return img_array
+    
+    def predict(self, image_path):
+        # Preprocess image
+        img_array = self.preprocess_image(image_path)
+        
+        # Set input tensor
+        self.interpreter.set_tensor(self.input_details[0]['index'], img_array)
+        
+        # Run inference
+        self.interpreter.invoke()
+        
+        # Get output tensor
+        output_data = self.interpreter.get_tensor(self.output_details[0]['index'])
+        predicted_class = np.argmax(output_data[0])
+        confidence = float(np.max(output_data[0]))
+        
+        return self.labels[predicted_class], confidence
+
+# Example usage
+if __name__ == "__main__":
+    classifier = WasteClassifier('waste_classification.tflite', 'labels.txt')
+    prediction, confidence = classifier.predict('sample_image.jpg')
+    print(f"Predicted: {prediction} with {confidence:.2%} confidence")

flask_app.py

@@ -0,0 +1,76 @@
+
+from flask import Flask, request, render_template, jsonify
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import os
+from werkzeug.utils import secure_filename
+import cv2
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB max file size
+
+# Load the trained model
+model = load_model('/content/drive/MyDrive/best_waste_classification_model.h5')
+classes = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
+
+# Enhanced recycling tips dictionary
+recycling_tips = {
+    # ... (copy the enhanced recycling tips dictionary here)
+}
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file uploaded'})
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({'error': 'No file selected'})
+
+    if file:
+        # Save the uploaded file
+        filename = secure_filename(file.filename)
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+        file.save(filepath)
+
+        # Preprocess the image
+        img = image.load_img(filepath, target_size=(224, 224))
+        img_array = image.img_to_array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        # Make prediction
+        prediction = model.predict(img_array)
+        predicted_class_idx = np.argmax(prediction)
+        predicted_class = classes[predicted_class_idx]
+        confidence = float(np.max(prediction))
+
+        # Get recycling tips
+        tips_info = recycling_tips.get(predicted_class, {})
+        tips = tips_info.get('tips', ['No specific tips available.'])
+        preparation = tips_info.get('preparation', 'No specific preparation instructions.')
+        recyclability = tips_info.get('recyclability', 'Unknown recyclability.')
+        common_uses = tips_info.get('common_uses', 'Unknown common uses.')
+
+        # Select a random tip from the list
+        import random
+        random_tip = random.choice(tips) if tips else 'No specific tips available.'
+
+        return jsonify({
+            'class': predicted_class,
+            'confidence': confidence,
+            'tip': random_tip,
+            'preparation': preparation,
+            'recyclability': recyclability,
+            'common_uses': common_uses,
+            'all_tips': tips
+        })
+
+if __name__ == '__main__':
+    app.run(debug=True)

labels.txt

@@ -0,0 +1,6 @@
+0:cardboard
+1:glass
+2:metal
+3:paper
+4:plastic
+5:trash

recycling_tips.json

@@ -0,0 +1,56 @@
+{
+    "cardboard": {
+        "tips": [
+            "Flatten boxes before recycling to save space",
+            "Remove any tape, labels, or plastic windows",
+            "Keep cardboard dry and clean"
+        ],
+        "preparation": "Flatten and remove contaminants",
+        "recyclability": "Highly recyclable"
+    },
+    "glass": {
+        "tips": [
+            "Rinse containers to remove residue",
+            "Remove metal lids and caps (recycle separately)",
+            "Do not mix different colored glass if required by your facility"
+        ],
+        "preparation": "Rinse and remove non-glass components",
+        "recyclability": "Infinitely recyclable"
+    },
+    "metal": {
+        "tips": [
+            "Rinse cans to remove food residue",
+            "Remove paper labels if possible",
+            "Crush aluminum cans to save space"
+        ],
+        "preparation": "Rinse and separate by type if needed",
+        "recyclability": "Highly recyclable"
+    },
+    "paper": {
+        "tips": [
+            "Keep paper dry and clean",
+            "Remove any plastic coatings or laminations",
+            "Shred confidential documents before recycling"
+        ],
+        "preparation": "Keep dry and remove contaminants",
+        "recyclability": "Highly recyclable"
+    },
+    "plastic": {
+        "tips": [
+            "Check the recycling number (1-7) on the item",
+            "Rinse containers to remove residue",
+            "Remove caps and pumps (often different plastic types)"
+        ],
+        "preparation": "Rinse and check local guidelines",
+        "recyclability": "Varies by type"
+    },
+    "trash": {
+        "tips": [
+            "Consider if items can be reused or repurposed",
+            "Remove any recyclable components",
+            "Dispose of properly in designated trash bins"
+        ],
+        "preparation": "Separate recyclables and compostables",
+        "recyclability": "Not recyclable"
+    }
+}

requirements.txt

@@ -0,0 +1,4 @@
+
+tensorflow>=2.0.0
+numpy>=1.0.0
+Pillow>=8.0.0

waste_classification.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:755f30503dd49e3a710251e71dc432342a8ea41f8cb21879439b21e82937975d
+size 19294464