🔧 Fix Gradio compatibility - remove deprecated @gr .cache decorator

app.py

@@ -13,20 +13,23 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 
-# Initialize the model
-@gr.cache
+# Initialize the model globally
+classifier = None
+
 def load_model():
     """Load the emotion classification model"""
-    try:
-        classifier = pipeline(
-            "text-classification",
-            model="rmtariq/multilingual-emotion-classifier",
-            device=0 if torch.cuda.is_available() else -1
-        )
-        return classifier
-    except Exception as e:
-        gr.Error(f"Error loading model: {e}")
-        return None
+    global classifier
+    if classifier is None:
+        try:
+            classifier = pipeline(
+                "text-classification",
+                model="rmtariq/multilingual-emotion-classifier",
+                device=0 if torch.cuda.is_available() else -1
+            )
+        except Exception as e:
+            print(f"Error loading model: {e}")
+            return None
+    return classifier
 
 # Emotion mappings
 EMOTION_EMOJIS = {
@@ -51,14 +54,14 @@ def classify_emotion(text):
     """Classify emotion for a single text"""
     if not text.strip():
         return "Please enter some text to analyze.", None, None
-    
-    classifier = load_model()
-    if classifier is None:
+
+    model = load_model()
+    if model is None:
         return "Model failed to load. Please try again.", None, None
     
     try:
         # Get prediction
-        result = classifier(text)
+        result = model(text)
         emotion = result[0]['label'].lower()
         confidence = result[0]['score']
         
@@ -83,10 +86,10 @@ def classify_emotion(text):
         scores = []
         
         # Get scores for all emotions (if available)
-        if hasattr(result[0], 'scores'):
-            all_results = classifier(text, return_all_scores=True)
+        try:
+            all_results = model(text, return_all_scores=True)
             scores = [next((r['score'] for r in all_results if r['label'].lower() == e), 0) for e in emotions]
-        else:
+        except:
             # If only top prediction available, set others to 0
             scores = [confidence if e == emotion else 0 for e in emotions]
         
@@ -134,9 +137,9 @@ def classify_batch(text_input):
     """Classify emotions for multiple texts"""
     if not text_input.strip():
         return "Please enter texts to analyze (one per line).", None
-    
-    classifier = load_model()
-    if classifier is None:
+
+    model = load_model()
+    if model is None:
         return "Model failed to load. Please try again.", None
     
     try:
@@ -149,7 +152,7 @@ def classify_batch(text_input):
         # Classify all texts
         results = []
         for text in texts:
-            result = classifier(text)
+            result = model(text)
             emotion = result[0]['label'].lower()
             confidence = result[0]['score']
             emoji = EMOTION_EMOJIS.get(emotion, '🤔')
@@ -198,8 +201,8 @@ def classify_batch(text_input):
 
 def run_predefined_tests():
     """Run predefined test cases"""
-    classifier = load_model()
-    if classifier is None:
+    model = load_model()
+    if model is None:
         return "Model failed to load. Please try again.", None
     
     # Predefined test cases
@@ -228,7 +231,7 @@ def run_predefined_tests():
     correct = 0
     
     for text, expected, flag in test_cases:
-        result = classifier(text)
+        result = model(text)
         predicted = result[0]['label'].lower()
         confidence = result[0]['score']