Update app.py

app.py

@@ -30,15 +30,24 @@ xception_model = tf.keras.models.load_model(
 
 def predict_fire(image):
     img = Image.fromarray(image).convert("RGB")
-    img = img.resize((224, 224))  # Match model input size
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
 
-    fire_pred = vgg16_model.predict(img_array)
+    # Preprocess for vgg16_model (128x128 input size)
+    vgg16_img = img.resize((128, 128))
+    vgg16_img_array = np.array(vgg16_img) / 255.0
+    vgg16_img_array = np.expand_dims(vgg16_img_array, axis=0)
+
+    # Fire detection using vgg16_model
+    fire_pred = vgg16_model.predict(vgg16_img_array)
     fire_status = "Fire Detected" if fire_pred[0][0] > 0.5 else "No Fire Detected"
 
+    # If fire is detected, preprocess for xception_model (224x224 input size)
     if fire_status == "Fire Detected":
-        severity_pred = xception_model.predict(img_array)
+        xception_img = img.resize((224, 224))
+        xception_img_array = np.array(xception_img) / 255.0
+        xception_img_array = np.expand_dims(xception_img_array, axis=0)
+
+        # Severity prediction using xception_model
+        severity_pred = xception_model.predict(xception_img_array)
         severity_level = np.argmax(severity_pred[0])
         severity = ["Mild", "Moderate", "Severe"][severity_level]
     else: