Create app.py

app.py

@@ -0,0 +1,25 @@
+from flask import Flask, request, jsonify
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+app = Flask(__name__)
+model = tf.keras.models.load_model("MobileNet_Fire.h5")
+
+class_labels = {0: "Fake", 1: "Low", 2: "Medium", 3: "High"}  # Update as per your training
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    file = request.files["file"]
+    img = image.load_img(file, target_size=(128, 128))
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    predictions = model.predict(img_array)
+    predicted_class = class_labels[np.argmax(predictions)]
+    confidence = float(np.max(predictions))
+
+    return jsonify({"prediction": predicted_class, "confidence": confidence})
+
+if __name__ == "__main__":
+    app.run(debug=True)