Update app.py

app.py

@@ -1,70 +1,69 @@
-# flask_app.py
-import os
-import numpy as np
-import cv2
-from flask import Flask, request, jsonify, render_template
-from tensorflow.keras.models import load_model  # type: ignore
-
-app = Flask(__name__)
-
-# Load the saved model
-model = load_model('pneumonia_model_final.keras')
-
-def preprocess_image(image):
-    """Preprocess the image for prediction."""
-    img = cv2.imread(image, cv2.IMREAD_GRAYSCALE)  # Read image as grayscale
-    if img is None:
-        return None
-    
-    img = cv2.resize(img, (224, 224))  # Resize to 224x224 pixels
-    img = img.astype(np.float32) / 255.0  # Normalize to [0, 1] range
-    img = (img * 255).astype(np.uint8)  # Convert back to 8-bit unsigned integer format
-    
-    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # Convert to RGB
-    img = np.expand_dims(img, axis=0)  # Add batch dimension
-    return img
-
-@app.route('/')
-def home():
-    """Render the home page for uploading images."""
-    return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    """Endpoint for making predictions."""
-    if 'file' not in request.files:
-        return jsonify({'error': 'No file provided.'}), 400
-
-    file = request.files['file']
-    
-    # Save the uploaded file temporarily
-    file_path = 'temp_image.jpg'
-    file.save(file_path)
-
-    # Preprocess the image
-    processed_image = preprocess_image(file_path)
-    if processed_image is None:
-        return jsonify({'error': 'Invalid image format.'}), 400
-
-    # Make a prediction
-    prediction = model.predict(processed_image)
-    probability = prediction[0][0]  # Get the probability score
-
-    # Calculate percentage probability
-    probability_percent = round(probability * 100, 2)
-    
-    # Classification threshold
-    threshold = 0.7  # Adjust threshold if necessary
-    is_pneumonia = probability > threshold
-
-    # Determine result message
-    result_text = f"Pneumonia" if is_pneumonia else f"Not Pneumonia"
-
-    # Remove the temporary file
-    os.remove(file_path)
-
-    # Render the result page with prediction details
-    return render_template('result.html', result=result_text, probability=probability_percent)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+import os
+import numpy as np
+import cv2
+from flask import Flask, request, jsonify, render_template
+from tensorflow.keras.models import load_model  # type: ignore
+
+app = Flask(__name__)
+
+# Load the saved model
+model = load_model('pneumonia_model_final.keras')
+
+def preprocess_image(image):
+    """Preprocess the image for prediction."""
+    img = cv2.imread(image, cv2.IMREAD_GRAYSCALE)  # Read image as grayscale
+    if img is None:
+        return None
+    
+    img = cv2.resize(img, (224, 224))  # Resize to 224x224 pixels
+    img = img.astype(np.float32) / 255.0  # Normalize to [0, 1] range
+    img = (img * 255).astype(np.uint8)  # Convert back to 8-bit unsigned integer format
+    
+    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # Convert to RGB
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    return img
+
+@app.route('/')
+def home():
+    """Render the home page for uploading images."""
+    return render_template('index.html', template_folder='.')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """Endpoint for making predictions."""
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file provided.'}), 400
+
+    file = request.files['file']
+    
+    # Save the uploaded file temporarily
+    file_path = 'temp_image.jpg'
+    file.save(file_path)
+
+    # Preprocess the image
+    processed_image = preprocess_image(file_path)
+    if processed_image is None:
+        return jsonify({'error': 'Invalid image format.'}), 400
+
+    # Make a prediction
+    prediction = model.predict(processed_image)
+    probability = prediction[0][0]  # Get the probability score
+
+    # Calculate percentage probability
+    probability_percent = round(probability * 100, 2)
+    
+    # Classification threshold
+    threshold = 0.7  # Adjust threshold if necessary
+    is_pneumonia = probability > threshold
+
+    # Determine result message
+    result_text = f"Pneumonia" if is_pneumonia else f"Not Pneumonia"
+
+    # Remove the temporary file
+    os.remove(file_path)
+
+    # Render the result page with prediction details
+    return render_template('result.html', result=result_text, probability=probability_percent, template_folder='.')
+
+if __name__ == '__main__':
+    app.run(debug=True)