Update app.py

app.py

@@ -8,65 +8,51 @@ import base64
 emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
 toxicity_pipeline = pipeline("text-classification", model="martin-ha/toxic-comment-model")
 
-EMO_THRESHOLD = 0.05  # include even lower confidence emotions
+EMO_THRESHOLD = 0.05  # Minimum confidence threshold for emotions
 
-# Expanded emotion mapping with special cases
+# Comprehensive emotion mapping
 EMOTION_MAPPING = {
-    "joy": {
-        "underlying": ["Relief", "Contentment", "Pride", "Love", "Gratitude", "Hope"],
-        "recommendation": "Keep enjoying and share your happiness with others!",
-        "polarity": 1
+    "excitement": {
+        "underlying": ["Anticipation", "Eagerness", "Enthusiasm"],
+        "recommendation": "Channel this energy into planning and goal-setting!",
+        "polarity": 1,
+        "keywords": ["thrilled", "excited", "pumped"]
     },
-    "sadness": {
-        "underlying": ["Loneliness", "Grief", "Disappointment", "Shame", "Helplessness", "Regret"],
-        "recommendation": "Consider talking to someone or journaling about your feelings.",
-        "polarity": -1
+    "anxiety": {
+        "underlying": ["Worry", "Unease", "Apprehension", "Nervousness"],
+        "recommendation": "Try box breathing (4-4-4-4) and focus on preparation.",
+        "polarity": -1,
+        "keywords": ["anxious", "nervous", "apprehensive"]
     },
-    "anger": {
-        "underlying": ["Frustration", "Injustice", "Betrayal", "Resentment"],
-        "recommendation": "Try deep breathing or physical activity to release tension.",
-        "polarity": -1
+    "joy": {
+        "underlying": ["Contentment", "Pleasure", "Delight"],
+        "recommendation": "Savor this moment and share your happiness!",
+        "polarity": 1,
+        "keywords": ["happy", "joyful"]
     },
     "fear": {
-        "underlying": ["Anxiety", "Insecurity", "Dread", "Worry"],
-        "recommendation": "Focus on what you can control and practice grounding techniques.",
-        "polarity": -1
-    },
-    "disappointment": {
-        "underlying": ["Letdown", "Unmet expectations", "Discouragement"],
-        "recommendation": "Acknowledge the feeling, then refocus on what you can influence.",
-        "polarity": -1
+        "underlying": ["Dread", "Panic", "Terror"],
+        "recommendation": "Practice grounding techniques and focus on the present.",
+        "polarity": -1,
+        "keywords": ["scared", "afraid"]
     },
     "pride": {
         "underlying": ["Accomplishment", "Self-worth", "Achievement"],
         "recommendation": "Celebrate your success while staying humble.",
-        "polarity": 1
+        "polarity": 1,
+        "keywords": ["proud", "accomplished"]
     },
-    "anxiety": {
-        "underlying": ["Worry", "Unease", "Apprehension", "Nervousness"],
-        "recommendation": "Try box breathing (4-4-4-4) and focus on preparation.",
-        "polarity": -1
-    },
-    "excitement": {
-        "underlying": ["Anticipation", "Eagerness", "Enthusiasm"],
-        "recommendation": "Channel this energy into planning and goal-setting!",
-        "polarity": 1
+    "disappointment": {
+        "underlying": ["Letdown", "Frustration", "Regret"],
+        "recommendation": "Acknowledge the feeling, then focus on what you can control.",
+        "polarity": -1,
+        "keywords": ["disappointed", "let down"]
     }
 }
 
-# Special emotion triggers
-SPECIAL_EMOTION_TRIGGERS = {
-    "anxious": {"emotion": "anxiety", "confidence": 0.8},
-    "thrilled": {"emotion": "excitement", "confidence": 0.9},
-    "devastated": {"emotion": "grief", "confidence": 0.85},
-    "overjoyed": {"emotion": "elation", "confidence": 0.95},
-    "apprehensive": {"emotion": "wariness", "confidence": 0.75}
-}
-
 def create_visualization(emotions):
     fig, ax = plt.subplots(figsize=(8,5))
-    colors = ['#4CAF50' if EMOTION_MAPPING.get(e, {}).get('polarity', 0) > 0 
-              else '#F44336' for e in emotions.keys()]
+    colors = ['#4CAF50' if EMOTION_MAPPING[e]['polarity'] > 0 else '#F44336' for e in emotions.keys()]
     ax.barh(list(emotions.keys()), list(emotions.values()), color=colors)
     ax.set_xlim(0,1)
     ax.set_xlabel("Confidence Score")
@@ -79,41 +65,53 @@ def create_visualization(emotions):
     plt.close()
     return f'<img src="data:image/png;base64,{base64.b64encode(buf.read()).decode("utf-8")}">'
 
-def detect_special_emotions(text):
-    special_emotions = {}
+def detect_keyword_emotions(text):
+    emotions = {}
     text_lower = text.lower()
     
-    for trigger, data in SPECIAL_EMOTION_TRIGGERS.items():
-        if trigger in text_lower:
-            emotion = data["emotion"]
-            # Only add if not already detected or if our confidence is higher
-            if data["confidence"] > special_emotions.get(emotion, 0):
-                special_emotions[emotion] = data["confidence"]
-    
-    return special_emotions
+    for emo, data in EMOTION_MAPPING.items():
+        for keyword in data['keywords']:
+            if keyword in text_lower:
+                # Use higher confidence for exact matches
+                confidence = 0.9 if keyword in text_lower.split() else 0.7
+                if confidence > emotions.get(emo, 0):
+                    emotions[emo] = confidence
+    return emotions
 
 def analyze_text(text):
-    # Detect special cases first
-    special_emotions = detect_special_emotions(text)
+    # Detect keyword-based emotions first
+    keyword_emotions = detect_keyword_emotions(text)
     
-    # Original emotion detection
+    # Get base model emotions
     emo_results = emotion_pipeline(text, top_k=None)
-    emotions = {e["label"].lower(): e["score"] for e in emo_results if e["score"] > EMO_THRESHOLD}
+    base_emotions = {e["label"].lower(): e["score"] for e in emo_results 
+                    if e["score"] > EMO_THRESHOLD}
+    
+    # Merge emotions with keyword emotions taking priority
+    final_emotions = {}
+    
+    # Add keyword emotions first
+    for emo, score in keyword_emotions.items():
+        final_emotions[emo] = score
     
-    # Merge with special cases (keeping higher confidence)
-    for emo, score in special_emotions.items():
-        if score > emotions.get(emo, 0):
-            emotions[emo] = score
+    # Add base emotions only if not already detected by keywords
+    for emo, score in base_emotions.items():
+        # Skip if we already have a more specific version of this emotion
+        if (emo == 'joy' and 'excitement' in final_emotions) or \
+           (emo == 'fear' and 'anxiety' in final_emotions):
+            continue
+        if emo not in final_emotions:
+            final_emotions[emo] = score
     
     # Generate detailed analysis
     details = ""
     positive_emotions = []
     negative_emotions = []
     
-    for i, (emo, score) in enumerate(emotions.items(), 1):
+    for i, (emo, score) in enumerate(final_emotions.items(), 1):
         emotion_data = EMOTION_MAPPING.get(emo, {
-            "underlying": ["Unknown"],
-            "recommendation": "This emotion is complex. Reflect on its source.",
+            "underlying": ["Complex feeling"],
+            "recommendation": "This emotion warrants careful reflection.",
             "polarity": 0
         })
         
@@ -128,45 +126,42 @@ def analyze_text(text):
         elif emotion_data["polarity"] < 0:
             negative_emotions.append(emo)
     
-    # Generate balanced conclusion
+    # Generate final analysis
     conclusion = "\n=== Final Analysis ===\n"
     
     if positive_emotions:
-        conclusion += f"Positive emotions detected: {', '.join(positive_emotions)}\n"
+        conclusion += f"Positive emotions: {', '.join(positive_emotions)}\n"
     if negative_emotions:
-        conclusion += f"Negative emotions detected: {', '.join(negative_emotions)}\n"
+        conclusion += f"Negative emotions: {', '.join(negative_emotions)}\n"
     
-    # Final recommendation blending
-    if positive_emotions and not negative_emotions:
-        final_rec = "Enjoy this positive moment fully!"
+    # Context-aware final recommendation
+    if "excitement" in positive_emotions and "anxiety" in negative_emotions:
+        final_rec = "This exciting transition naturally brings some nerves. Make an action plan to build confidence."
+    elif positive_emotions and not negative_emotions:
+        final_rec = "Fully enjoy these positive feelings!"
     elif negative_emotions and not positive_emotions:
-        final_rec = "Consider reaching out for support if these feelings persist."
-    elif positive_emotions and negative_emotions:
-        if "anxiety" in negative_emotions and "excitement" in positive_emotions:
-            final_rec = "This exciting change comes with natural nerves. Make lists of what you can control to ease anxiety."
-        else:
-            final_rec = "You're experiencing mixed emotions. Acknowledge both sides - celebrate the positive while addressing the negative."
+        final_rec = "Consider discussing these feelings with someone you trust."
     else:
-        final_rec = "The emotional tone is neutral or complex."
+        final_rec = "Reflect on these mixed emotions to understand yourself better."
     
     conclusion += f"\nFinal Recommendation: {final_rec}"
     
-    visualization = create_visualization(emotions)
+    visualization = create_visualization(final_emotions)
     return details + conclusion, visualization
 
 iface = gr.Interface(
     fn=analyze_text,
-    inputs=gr.Textbox(label="Your Text", placeholder="I feel proud but also disappointed..."),
+    inputs=gr.Textbox(label="Your Text", placeholder="Describe your feelings..."),
     outputs=[
-        gr.Textbox(label="Detailed Analysis"),
-        gr.HTML(label="Emotion Visualization")
+        gr.Textbox(label="Emotion Analysis"),
+        gr.HTML(label="Visualization")
     ],
-    title="Advanced Emotion Analyzer",
-    description="Detects complex emotional states including underlying emotions and provides balanced recommendations.",
+    title="Precision Emotion Analyzer",
+    description="Accurately detects specific emotions without duplicates, providing tailored recommendations.",
     examples=[
-        ["I'm thrilled about my promotion but anxious about the increased responsibilities"],
-        ["I feel overjoyed yet apprehensive about the future"],
-        ["This situation makes me both hopeful and devastated"]
+        ["I'm thrilled about my promotion but anxious about the responsibilities"],
+        ["Feeling proud of my accomplishment yet disappointed with the recognition"],
+        ["I'm excited and nervous about the first day at my new job"]
     ]
 )