Create app.py

app.py

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+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+import cv2
+import os
+from flask import Flask, request, jsonify
+from werkzeug.utils import secure_filename
+
+# Initialize Flask app
+app = Flask(_name_)
+
+# Load the trained Keras model
+MODEL_PATH = "model.weights.h5"
+model = load_model(MODEL_PATH)
+
+# Define allowed extensions for image upload
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def preprocess_image(image_path):
+    """ Preprocess the uploaded image to match the model's input shape """
+    img = cv2.imread(image_path)
+    img = cv2.resize(img, (224, 224))  # Resize to model's expected input size
+    img = img / 255.0  # Normalize pixel values
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    return img
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """ API endpoint to predict melanoma from an uploaded image """
+    if 'file' not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({"error": "No selected file"}), 400
+
+    if file and allowed_file(file.filename):
+        filename = secure_filename(file.filename)
+        file_path = os.path.join("uploads", filename)
+        file.save(file_path)
+
+        # Preprocess and predict
+        image = preprocess_image(file_path)
+        prediction = model.predict(image)
+        os.remove(file_path)  # Remove the file after prediction
+
+        # Assuming model outputs a probability (0 = not melanoma, 1 = melanoma)
+        result = "Melanoma" if prediction[0][0] > 0.5 else "Not Melanoma"
+
+        return jsonify({"prediction": result, "confidence": float(prediction[0][0])})
+
+    return jsonify({"error": "Invalid file format"}), 400
+
+if _name_ == '_main_':
+    os.makedirs("uploads", exist_ok=True)  # Ensure upload directory exists
+    app.run(host='0.0.0.0', port=5000, debug=True)