Upload 4 files

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code into the container
+COPY . .
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Define environment variable that Hugging Face uses
+ENV PORT 7860
+
+# Create the necessary folders for the app to store uploads
+RUN mkdir -p static/uploads
+
+# Run the Flask app using Gunicorn when the container launches
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]
+

app_D.py

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+import os
+import tensorflow as tf
+from flask import Flask, request, render_template, redirect, url_for
+from werkzeug.utils import secure_filename
+
+# Initialize the Flask application
+app = Flask(__name__)
+
+# --- Load the Clean, Compatible .h5 Model ---
+# This model was saved with save_format='h5' for maximum compatibility.
+MODEL_PATH = 'waste_classifier_v2_clean.h5'
+try:
+    model = tf.keras.models.load_model(MODEL_PATH)
+    print("Image classification model loaded successfully!")
+except Exception as e:
+    print(f"Error loading image model: {e}")
+    exit()
+
+# Define the class names in the correct order for the model's output
+CLASS_NAMES = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
+
+def preprocess_image(image_path):
+    """
+    Loads an image from a file path and preprocesses it for the model.
+    This function ensures the input image matches the format used during training.
+    """
+    img = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224))
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0) # Create a batch of one
+    # Apply the MobileNetV2-specific preprocessing
+    return tf.keras.applications.mobilenet_v2.preprocess_input(img_array)
+
+@app.route('/', methods=['GET'])
+def index():
+    """Renders the main upload page."""
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """Handles the image upload, prediction, and renders the result."""
+    # Check if a file was uploaded
+    if 'file' not in request.files:
+        return redirect(request.url)
+    file = request.files['file']
+    if file.filename == '':
+        return redirect(request.url)
+
+    if file:
+        # Save the file securely
+        filename = secure_filename(file.filename)
+        filepath = os.path.join('static/uploads', filename)
+        file.save(filepath)
+
+        # Preprocess the image and get a prediction
+        preprocessed_image = preprocess_image(filepath)
+        prediction = model.predict(preprocessed_image)
+        
+        # Decode the prediction
+        predicted_class_index = tf.argmax(prediction[0]).numpy()
+        predicted_class = CLASS_NAMES[predicted_class_index]
+        confidence = tf.reduce_max(prediction[0]).numpy() * 100
+
+        # Pass the results to the HTML template
+        return render_template('index.html',
+                               prediction=f'Prediction: {predicted_class}',
+                               confidence=f'Confidence: {confidence:.2f}%',
+                               uploaded_image=filepath)
+    return redirect(request.url)
+
+if __name__ == '__main__':
+    # Ensure the upload folder exists
+    os.makedirs('static/uploads', exist_ok=True)
+    # This host and port configuration is important for deployment services like Hugging Face
+    app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))
+

requirements.txt

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+Flask
+tensorflow==2.16.1
+tf-keras==2.16.0
+numpy<2.0
+gradio
+gunicorn

waste_classifier_v2_clean.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b7d18edfcba92d50b60e8bf6b9d0bc778d79ef5f7aed0e36ea22a56f031516e
+size 21730560