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Dockerfile

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+# Use a lightweight Python base image
+FROM python:3.10-slim-buster
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy requirements.txt and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of your application code
+COPY . .
+
+# Ensure the static/uploads directory exists (if not created by your app)
+RUN mkdir -p static/uploads
+
+# Expose the port that Flask will run on (Hugging Face Spaces uses PORT env var)
+EXPOSE 7860
+
+# Command to run your Flask application
+# Hugging Face Spaces will set the PORT environment variable.
+# Your app.py is already set up to use it (os.environ.get('PORT', 7860)).
+CMD ["python", "app.py"]

app.py

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+import os
+import numpy as np
+import tensorflow as tf
+from flask import Flask, request, render_template, jsonify
+from tensorflow.keras.utils import load_img, img_to_array
+from werkzeug.utils import secure_filename
+from datetime import datetime
+
+app = Flask(__name__)
+
+# Load the trained model
+MODEL_PATH = r"model.keras"  # Update to correct path
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# Configurations
+UPLOAD_FOLDER = os.path.join('static', 'uploads')
+ALLOWED_EXTENSIONS = {'jpg', 'jpeg', 'png'}
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def preprocess_image(image_path):
+    img = load_img(image_path, target_size=(224, 224))
+    img_array = img_to_array(img) / 255.0
+    return np.expand_dims(img_array, axis=0)
+
+@app.route('/')
+def index():
+    return render_template('home.html')
+
+@app.route('/tool')
+def tool():
+    return render_template('tool.html')
+
+@app.route('/about')
+def about():
+    return render_template('about.html')
+
+@app.route('/contact') 
+def contact():
+    return render_template('contact.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No files uploaded'}), 400
+
+    files = request.files.getlist('file')
+    if not files or all(f.filename == '' for f in files):
+        return jsonify({'error': 'No files selected'}), 400
+
+    results = []
+    for file in files:
+        if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            timestamp = datetime.now().strftime("%Y%m%d%H%M%S%f")
+            unique_filename = f"{timestamp}_{filename}"
+            file_path = os.path.join(app.config['UPLOAD_FOLDER'], unique_filename)
+            file.save(file_path)
+
+            try:
+                img_array = preprocess_image(file_path)
+                prediction = model.predict(img_array)[0][0]
+                label = "Dirty" if prediction > 0.5 else "Clean"
+                confidence = prediction if label == "Dirty" else 1 - prediction
+
+                results.append({
+                    'label': label,
+                    'confidence': f"{confidence:.2%}",
+                    'image_url': f"/static/uploads/{unique_filename}"
+                })
+            except Exception as e:
+                results.append({
+                    'label': 'Error',
+                    'confidence': 'N/A',
+                    'image_url': None,
+                    'error': str(e)
+                })
+        else:
+            results.append({
+                'label': 'Error',
+                'confidence': 'N/A',
+                'image_url': None,
+                'error': f"Invalid file type: {file.filename}"
+            })
+
+    # Render a results page and pass results into it
+    return render_template('results.html', results=results)
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+Flask
+tensorflow
+numpy
+Pillow
+requests 
+scikit-learn
+matplotlib
+seaborn
+huggingface_hub