Upload app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+from tensorflow.keras.models import load_model
+import numpy as np
+import cv2
+import base64
+
+app = Flask(__name__)
+
+# Load model
+model = load_model("vgg16_mask.keras")
+classes = ["Mask", "No Mask"]
+
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    try:
+        # Get base64 image from frontend
+        data = request.json["image"]
+        img_data = base64.b64decode(data.split(",")[1])
+        nparr = np.frombuffer(img_data, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+        # Preprocess image
+        img = cv2.resize(img, (224, 224))
+        img = img / 255.0
+        img = np.expand_dims(img, axis=0)
+
+        # Predict
+        pred = model.predict(img)
+        label = classes[int(pred[0][0] > 0.5)]
+
+        return jsonify({"label": label})
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+if __name__ == "__main__":
+    from flask import Flask
+    import os
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host="0.0.0.0", port=port)
+