Update app.py

app.py

@@ -1,70 +1,10 @@
-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
+from flask import Flask, jsonify
 
-# Specify the template folder manually
-app = Flask(__name__, template_folder='.')
+app = Flask(__name__)
 
-# Load the saved model
-model = load_model('pneumonia_model.keras')
+@app.route('/api/data')
+def get_data():
+    return jsonify({"message": "Hello from Flask!"})
 
-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')  # This will look for index.html in the specified 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)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+if __name__ == "__main__":
+    app.run(port=5000)  # Run on port 5000