Update app.py

app.py

@@ -5,48 +5,29 @@ import json
 import pickle
 import re
 
-# Create a simple fallback prediction function
-def predict_emotion_fallback(text, top_k=5):
-    """Fallback prediction function for testing"""
-    # Return some sample predictions for demonstration
-    sample_predictions = [
-        ("Wonder", 42.88),
-        ("Relief", 6.86),
-        ("Intrigue", 6.62),
-        ("Joy", 5.31),
-        ("Curiosity", 4.97)
-    ]
-    return sample_predictions[:top_k]
-
-# Load saved components with comprehensive error handling
+# Load saved components
 def load_model_components():
-    """Load all saved model components with error handling"""
+    """Load all saved model components"""
     try:
-        # Try to import tensorflow components
         from tensorflow.keras.models import load_model
         from tensorflow.keras.preprocessing.text import Tokenizer
         from tensorflow.keras.preprocessing.sequence import pad_sequences
         
-        # Load model
         model = load_model('best_emotion_model.h5')
         
-        # Load tokenizer
         with open('tokenizer.pickle', 'rb') as handle:
             tokenizer = pickle.load(handle)
         
-        # Load label encoder
         with open('label_encoder.pickle', 'rb') as handle:
             label_encoder = pickle.load(handle)
         
-        # Load config
         with open('model_config.json', 'r') as f:
             config = json.load(f)
         
         return model, tokenizer, label_encoder, config
     
     except Exception as e:
-        print(f"Model loading error: {str(e)}")
-        return None, None, None, None
+        raise ImportError(f"Error loading model components: {str(e)}")
 
 # Text cleaning function
 def clean_text(text, labels_to_remove=[]):
@@ -74,17 +55,11 @@ def clean_text(text, labels_to_remove=[]):
 
     return text
 
-# Prediction function with fallback
+# Prediction function
 def predict_emotion(text, top_k=5):
     """Predict emotion from text with top-k confidence scores"""
-    # Get model components
-    model, tokenizer, label_encoder, config = load_model_components()
-    
-    # If model failed to load, use fallback
-    if model is None or tokenizer is None or label_encoder is None:
-        return predict_emotion_fallback(text, top_k)
-    
     try:
+        model, tokenizer, label_encoder, config = load_model_components()
         MAX_LEN = config['MAX_LEN']
         
         # Clean text
@@ -113,8 +88,7 @@ def predict_emotion(text, top_k=5):
         return results
     
     except Exception as e:
-        print(f"Prediction error: {str(e)}")
-        return predict_emotion_fallback(text, top_k)
+        return [("Error", 0.0)]
 
 # Gradio interface
 def emotion_classifier(text, top_k):
@@ -128,20 +102,18 @@ def emotion_classifier(text, top_k):
         if not predictions or len(predictions) == 0:
             return "❌ No predictions generated. Please try different text."
         
-        # Format results as HTML table for better display
-        result_html = f"<h3>Emotion Predictions for:</h3><p>{text}</p>"
-        result_html += "<table border='1' cellpadding='5' cellspacing='0' style='border-collapse: collapse;'>"
-        result_html += "<tr><th>Emotion</th><th>Confidence (%)</th></tr>"
+        # Format results
+        result_text = f"**Emotion Predictions for:** {text}\n\n"
+        result_text += "| Emotion | Confidence (%) |\n"
+        result_text += "|---------|----------------|\n"
         
         for emotion, confidence in predictions:
             if confidence > 0:
-                result_html += f"<tr><td>{emotion}</td><td>{confidence:.2f}%</td></tr>"
+                result_text += f"| {emotion} | {confidence:.2f} |\n"
             else:
-                result_html += f"<tr><td>{emotion}</td><td>Not available</td></tr>"
-        
-        result_html += "</table>"
+                result_text += f"| {emotion} | Not available |\n"
         
-        return result_html
+        return result_text
     
     except Exception as e:
         return f"❌ Error during analysis: {str(e)}"
@@ -160,7 +132,7 @@ with gr.Blocks(title="Emotion Classification App", theme=gr.themes.Soft()) as de
                 label="Enter Text for Emotion Analysis",
                 placeholder="Type your text here (e.g., 'I feel so happy about my achievements!')",
                 lines=5,
-                value="I heard that rumor about my colleague, and honestly, I feel a rush of competitive schadenfreude."
+                value="I made the mistake, but I'm determined to fix it immediately and ensure it never happens again"
             )
             
             top_k_slider = gr.Slider(
@@ -188,9 +160,9 @@ with gr.Blocks(title="Emotion Classification App", theme=gr.themes.Soft()) as de
             )
         
         with gr.Column():
-            output = gr.HTML(
+            output = gr.Markdown(
                 label="Emotion Predictions", 
-                value="<p>Enter text and click 'Analyze Emotions' to see predictions.</p>"
+                value="Enter text and click 'Analyze Emotions' to see predictions."
             )
     
     submit_btn.click(