Update app.py

app.py

@@ -15,15 +15,14 @@ import pickle
 from PIL import Image
 
 # ================================
-# LOAD FUSION MODEL (CRITICAL FIX)
+# LOAD FUSION MODEL (KERAS 3 SAFE)
 # ================================
 fusion_model = tf.keras.models.load_model(
-    "fusion_ticket_model.h5",
-    compile=False,
-    safe_mode=False   # 🔥 REQUIRED FOR MULTI-INPUT MODELS
+    "fusion_ticket_model_final.keras",
+    compile=False
 )
 
-print("✅ Fusion model loaded")
+print("✅ Fusion model loaded successfully")
 
 # ================================
 # LOAD TOKENIZER
@@ -46,7 +45,7 @@ LABELS = ["Critical", "High", "Medium", "Low"]
 def preprocess_image(image: Image.Image):
     image = image.convert("RGB")
     image = image.resize(IMG_SIZE)
-    img = np.array(image, dtype=np.float32) / 255.0
+    img = np.asarray(image, dtype=np.float32) / 255.0
     img = np.expand_dims(img, axis=0)
     return img
 
@@ -54,7 +53,7 @@ def preprocess_image(image: Image.Image):
 # TEXT PREPROCESSING
 # ================================
 def preprocess_text(text: str):
-    if text is None:
+    if not text:
         text = ""
     seq = tokenizer.texts_to_sequences([text])
     padded = tf.keras.preprocessing.sequence.pad_sequences(
@@ -67,7 +66,12 @@ def preprocess_text(text: str):
 # ================================
 def predict_ticket(image, text):
     if image is None:
-        return {"Critical": 0, "High": 0, "Medium": 0, "Low": 0}
+        return {
+            "Critical": 0.0,
+            "High": 0.0,
+            "Medium": 0.0,
+            "Low": 0.0
+        }
 
     img = preprocess_image(image)
     txt = preprocess_text(text)
@@ -103,4 +107,4 @@ interface = gr.Interface(
     )
 )
 
-interface.launch(server_name="0.0.0.0", server_port=7860)
+interface.launch()