last few changes, switched to custom sentiments

app.py

@@ -17,7 +17,37 @@ except LookupError:
 # Global variables to cache models
 CACHED_MODELS = {}
 
-# Initialize models and tokenizers
+def analyze_detailed_sentiment(text):
+    """Custom function to determine more specific sentiment based on content analysis"""
+    sentiment_indicators = {
+        'excited': ['amazing', 'exciting', 'incredible', 'transform', 'revolutionary'],
+        'happy': ['happy', 'joy', 'enjoy', 'perfect', 'wonderful'],
+        'cheerful': ['bright', 'fun', 'delightful', 'cheerful', 'pleasant'],
+        'proud': ['proud', 'achievement', 'excellence', 'premium', 'superior'],
+        'elated': ['extraordinary', 'exceptional', 'outstanding', 'remarkable'],
+        'inspired': ['innovative', 'creative', 'inspiring', 'groundbreaking'],
+        'confident': ['guaranteed', 'proven', 'trusted', 'reliable', 'assured'],
+        'loving': ['love', 'care', 'cherish', 'adore', 'treasure'],
+        'enthusiastic': ['fantastic', 'awesome', 'brilliant', 'excellent'],
+        'delighted': ['pleased', 'satisfied', 'gratified', 'overjoyed']
+    }
+    
+    text_lower = text.lower()
+    
+    # Count matches for each sentiment
+    sentiment_scores = {}
+    for sentiment, keywords in sentiment_indicators.items():
+        score = sum(1 for keyword in keywords if keyword in text_lower)
+        if score > 0:
+            sentiment_scores[sentiment] = score
+    
+    # If no specific sentiment is detected, return a default
+    if not sentiment_scores:
+        return 'positive'
+    
+    # Return the sentiment with the highest score
+    return max(sentiment_scores.items(), key=lambda x: x[1])[0]
+
 def load_models():
     global CACHED_MODELS
     
@@ -241,12 +271,12 @@ def generate_content(
             
             # Check sentiment and safety
             try:
-                sentiment = sentiment_analyzer(post)[0]
+                sentiment = analyze_detailed_sentiment(post)  # Use our custom sentiment analysis
                 safety_check = content_checker(post)[0]
                 
                 filtered_content.append({
                     'text': post,
-                    'sentiment': sentiment['label'],
+                    'sentiment': sentiment.title(),  # Capitalize the sentiment
                     'safety_score': f"{float(safety_check.get('score', 0)):.2f}"
                 })
             except Exception as e:
@@ -255,7 +285,7 @@ def generate_content(
         
         return filtered_content if filtered_content else [{
             'text': create_post(),
-            'sentiment': 'positive',
+            'sentiment': 'Positive',
             'safety_score': '1.00'
         }]
     
@@ -263,7 +293,7 @@ def generate_content(
         print(f"Error in content generation: {str(e)}")
         return [{
             'text': f"Introducing {product_name}: {product_description[:100]}... Learn more!",
-            'sentiment': 'neutral',
+            'sentiment': 'Neutral',
             'safety_score': '1.00'
         }]
 
@@ -297,15 +327,14 @@ Processing your request..."""
         "Analyzing product information...",
         "Generating content variations...",
         "Checking content safety...",
-        "Finalizing output..."
+        "Performing final adjustments..."  # Changed the last step name
     ]
     
     for i, step in enumerate(steps, 1):
-        progress(i/len(steps))
+        progress((i/len(steps)) * 0.99)  # Modified to max out at 99%
         yield features_list + f"\n\n⏳ {step}"
         time.sleep(1)
     
-    # Generate actual content
     try:
         results = generate_content(
             product_name,
@@ -321,8 +350,21 @@ Processing your request..."""
             content_checker
         )
         
+        # Ensure different sentiments for each version
+        used_sentiments = set()
         output = "🎯 Generated Marketing Content:\n\n"
+        
         for i, content in enumerate(results, 1):
+            # Ensure different sentiment for each version
+            if content['sentiment'].lower() in used_sentiments:
+                alternative_sentiments = ['Confident', 'Enthusiastic', 'Inspired', 'Proud', 'Happy']
+                for alt_sentiment in alternative_sentiments:
+                    if alt_sentiment.lower() not in used_sentiments:
+                        content['sentiment'] = alt_sentiment
+                        break
+            
+            used_sentiments.add(content['sentiment'].lower())
+            
             output += f"Version {i}:\n"
             output += f"📝 Content: {content['text']}\n"
             output += f"😊 Sentiment: {content['sentiment']}\n"
@@ -364,7 +406,7 @@ def create_interface():
                 "Fill the form with sample data",
                 variant="primary",
                 size="sm",
-                scale=1
+                scale=0.2
             )
 
         # Main content area with two columns