Update app.py

app.py

@@ -79,6 +79,8 @@ EMOTION_LABELS = [
     "sadness",
     "anger",
     "fear",
+    "distress",  # Added for better crisis detection
+    "panic",     # Added for emergency situations
     "love",
     "surprise",
     "calm",
@@ -305,13 +307,61 @@ def detect_negation(text):
     return False
 
 def detect_crisis_keywords(text):
-    """Detect crisis/emergency keywords"""
+    """Detect crisis/emergency keywords - Comprehensive detection"""
     crisis_keywords = [
-        'बचाओ', 'मदद', 'help', 'save',
-        'मार', 'पीट', 'हिंसा', 'beat', 'hit', 'violence',
-        'डर', 'खतरा', 'fear', 'danger',
-        'मर', 'मौत', 'death', 'die',
-        'छोड़', 'leave me', 'stop'
+        # Violence & Assault - हिंसा और हमला
+        'बचाओ', 'मदद', 'help', 'save', 'rescue',
+        'मार', 'मारो', 'पीट', 'पिट', 'हिंसा', 'beat', 'beating', 'hit', 'hitting', 'violence', 'violent',
+        'थप्पड़', 'लात', 'घूंसा', 'slap', 'kick', 'punch',
+        'हमला', 'attack', 'attacking', 'assault',
+        'चाकू', 'बंदूक', 'हथियार', 'knife', 'gun', 'weapon',
+        
+        # Fear & Danger - डर और खतरा
+        'डर', 'डरना', 'भय', 'fear', 'scared', 'afraid', 'terrified',
+        'खतरा', 'संकट', 'danger', 'dangerous', 'threat', 'emergency',
+        'भागो', 'run', 'escape',
+        
+        # Death & Severe Harm - मृत्यु और गंभीर नुकसान
+        'मर', 'मरना', 'मार डाल', 'मौत', 'death', 'die', 'dying', 'kill', 'murder',
+        'खून', 'blood', 'bleeding',
+        'जान', 'life',
+        
+        # Distress Calls - संकट संकेत
+        'छोड़', 'छोड़ो', 'जाने दो', 'leave', 'leave me', 'let go', 'stop', 'please stop',
+        'नहीं नहीं', 'मत करो', 'no no', "don't", 'stop it',
+        'कोई है', 'anyone', 'somebody help',
+        
+        # Kidnapping & Abduction - अपहरण
+        'उठा', 'ले जा', 'kidnap', 'abduct', 'taken',
+        'छुड़ा', 'free me', 'release',
+        
+        # Medical Emergency - चिकित्सा आपातकाल
+        'दर्द', 'तकलीफ', 'pain', 'hurt', 'hurting', 'ache',
+        'सांस', 'साँस', 'breath', 'breathing', 'suffocate',
+        'दिल', 'हृदय', 'heart', 'chest pain', 'heart attack',
+        'दौरा', 'बेहोश', 'seizure', 'unconscious', 'faint',
+        'खून बह', 'bleeding', 'injury', 'injured',
+        'एम्बुलेंस', 'अस्पताल', 'डॉक्टर', 'ambulance', 'hospital', 'doctor',
+        'दवा', 'दवाई', 'medicine', 'medication',
+        
+        # Suicide & Self-Harm - आत्महत्या
+        'आत्महत्या', 'suicide', 'kill myself',
+        'मर जा', 'जीना नहीं', 'want to die', "don't want to live",
+        'ख़त्म', 'समाप्त', 'end it', 'end this',
+        
+        # Abuse & Harassment - दुर्व्यवहार
+        'बलात्कार', 'छेड़', 'rape', 'molest', 'harassment', 'abuse',
+        'गलत काम', 'छूना', 'touch', 'inappropriate',
+        
+        # Accidents - दुर्घटना
+        'दुर्घटना', 'accident', 'crash', 'fell', 'fall',
+        'आग', 'fire', 'smoke', 'burning',
+        'बिजली', 'electric', 'shock',
+        
+        # Panic & Severe Distress - घबराहट
+        'घबरा', 'panic', 'panicking',
+        'बचा नहीं', 'फंस', 'trapped', 'stuck',
+        'सहारा', 'support', 'need help'
     ]
     
     text_lower = text.lower()
@@ -320,6 +370,57 @@ def detect_crisis_keywords(text):
             return True
     return False
 
+def detect_mental_health_distress(text):
+    """Detect mental health crisis indicators"""
+    mental_health_keywords = [
+        # Depression - अवसाद
+        'अवसाद', 'डिप्रेशन', 'depression', 'depressed',
+        'उदास', 'निराश', 'hopeless', 'helpless',
+        'कोई फायदा नहीं', 'no point', 'pointless', 'worthless',
+        
+        # Anxiety - चिंता
+        'घबराहट', 'बेचैन', 'anxiety', 'anxious', 'worried sick',
+        'चिंता', 'टेंशन', 'stress', 'stressed',
+        'परेशान', 'troubled', 'disturbed',
+        
+        # Isolation - अलगाव
+        'अकेला', 'तन्हा', 'lonely', 'alone', 'isolated',
+        'कोई नहीं', 'no one', 'nobody cares',
+        
+        # Despair - निराशा
+        'हार', 'give up', 'giving up',
+        'कोशिश नहीं', "can't anymore", 'too much',
+        'थक', 'tired of', 'exhausted'
+    ]
+    
+    text_lower = text.lower()
+    count = sum(1 for keyword in mental_health_keywords if keyword in text_lower)
+    return count >= 2  # Require at least 2 indicators for mental health flag
+
+def detect_grief_loss(text):
+    """Detect grief and loss situations"""
+    grief_keywords = [
+        'चल बसा', 'गुज़र', 'खो दिया', 'died', 'passed away', 'lost',
+        'अंतिम संस्कार', 'funeral', 'cremation',
+        'याद', 'miss', 'missing',
+        'गम', 'शोक', 'grief', 'mourning', 'sorrow'
+    ]
+    
+    text_lower = text.lower()
+    return any(keyword in text_lower for keyword in grief_keywords)
+
+def detect_relationship_distress(text):
+    """Detect relationship problems"""
+    relationship_keywords = [
+        'तलाक', 'अलग', 'divorce', 'separation', 'breakup', 'broke up',
+        'धोखा', 'बेवफा', 'cheat', 'cheating', 'betrayal',
+        'लड़ाई', 'झगड़ा', 'fight', 'fighting', 'argument',
+        'छोड़ दिया', 'left me', 'abandoned'
+    ]
+    
+    text_lower = text.lower()
+    return any(keyword in text_lower for keyword in relationship_keywords)
+
 def detect_mixed_emotions(text, prosodic_features):
     """Detect mixed emotions"""
     text_lower = text.lower()
@@ -440,8 +541,8 @@ def enhanced_sentiment_analysis(text, prosodic_features, raw_results):
     
     return sentiment_scores, final_confidence, is_mixed
 
-def process_emotion_results(emotion_result):
-    """Process zero-shot emotion classification results"""
+def process_emotion_results(emotion_result, transcription, prosodic_features=None):
+    """Process zero-shot emotion classification results with multi-situation awareness"""
     if emotion_result is None or isinstance(emotion_result, Exception):
         print(f"⚠️ Emotion classification error: {emotion_result}")
         return {
@@ -451,15 +552,119 @@ def process_emotion_results(emotion_result):
             "top_emotions": []
         }
     
-    # Get top 5 emotions
+    # Get emotions and scores
     labels = emotion_result['labels']
     scores = emotion_result['scores']
     
+    # Create emotion score dictionary for manipulation
+    emotion_scores = {labels[i]: scores[i] for i in range(len(labels))}
+    
+    # SITUATION DETECTION
+    is_crisis = detect_crisis_keywords(transcription)
+    is_mental_health = detect_mental_health_distress(transcription)
+    is_grief = detect_grief_loss(transcription)
+    is_relationship = detect_relationship_distress(transcription)
+    
+    # CRISIS DETECTION OVERRIDE - Highest priority for emergency situations
+    if is_crisis:
+        print("🚨 CRISIS DETECTED - Adjusting emotion predictions")
+        
+        # Strongly boost fear and related crisis emotions
+        crisis_emotions = ['fear', 'distress', 'panic', 'anger', 'sadness']
+        boost_factor = 4.0
+        
+        for emotion in crisis_emotions:
+            if emotion in emotion_scores:
+                emotion_scores[emotion] = min(0.95, emotion_scores[emotion] * boost_factor)
+        
+        # Suppress inappropriate emotions for crisis situations
+        suppress_emotions = ['surprise', 'excitement', 'happiness', 'joy', 'calm']
+        suppress_factor = 0.15
+        
+        for emotion in suppress_emotions:
+            if emotion in emotion_scores:
+                emotion_scores[emotion] = max(0.01, emotion_scores[emotion] * suppress_factor)
+        
+        # Renormalize scores
+        total = sum(emotion_scores.values())
+        if total > 0:
+            emotion_scores = {k: v/total for k, v in emotion_scores.items()}
+    
+    # MENTAL HEALTH DISTRESS - Boost sadness, fear, reduce positive
+    elif is_mental_health:
+        print("🧠 Mental health distress detected - Adjusting predictions")
+        
+        mental_health_emotions = ['sadness', 'fear', 'frustration', 'neutral']
+        boost_factor = 2.0
+        
+        for emotion in mental_health_emotions:
+            if emotion in emotion_scores:
+                emotion_scores[emotion] = min(0.90, emotion_scores[emotion] * boost_factor)
+        
+        # Reduce positive emotions
+        suppress_emotions = ['happiness', 'joy', 'excitement', 'calm']
+        for emotion in suppress_emotions:
+            if emotion in emotion_scores:
+                emotion_scores[emotion] = max(0.05, emotion_scores[emotion] * 0.3)
+        
+        total = sum(emotion_scores.values())
+        if total > 0:
+            emotion_scores = {k: v/total for k, v in emotion_scores.items()}
+    
+    # GRIEF & LOSS - Boost sadness primarily
+    elif is_grief:
+        print("💔 Grief/loss detected - Adjusting predictions")
+        
+        if 'sadness' in emotion_scores:
+            emotion_scores['sadness'] = min(0.85, emotion_scores['sadness'] * 2.5)
+        
+        # Moderate boost for related emotions
+        if 'neutral' in emotion_scores:
+            emotion_scores['neutral'] = min(0.40, emotion_scores['neutral'] * 1.3)
+        
+        # Suppress joy/excitement
+        suppress_emotions = ['happiness', 'joy', 'excitement']
+        for emotion in suppress_emotions:
+            if emotion in emotion_scores:
+                emotion_scores[emotion] = max(0.02, emotion_scores[emotion] * 0.2)
+        
+        total = sum(emotion_scores.values())
+        if total > 0:
+            emotion_scores = {k: v/total for k, v in emotion_scores.items()}
+    
+    # RELATIONSHIP DISTRESS - Boost sadness, anger, frustration
+    elif is_relationship:
+        print("💔 Relationship distress detected - Adjusting predictions")
+        
+        relationship_emotions = ['sadness', 'anger', 'frustration']
+        boost_factor = 1.8
+        
+        for emotion in relationship_emotions:
+            if emotion in emotion_scores:
+                emotion_scores[emotion] = min(0.80, emotion_scores[emotion] * boost_factor)
+        
+        total = sum(emotion_scores.values())
+        if total > 0:
+            emotion_scores = {k: v/total for k, v in emotion_scores.items()}
+    
+    # PROSODIC ADJUSTMENT - High pitch variation + negative words = likely anger/fear
+    if prosodic_features and prosodic_features.get('pitch_std', 0) > 40:
+        negative_words = ['गुस्सा', 'क्रोध', 'नफरत', 'angry', 'mad', 'hate']
+        if any(word in transcription.lower() for word in negative_words):
+            if 'anger' in emotion_scores:
+                emotion_scores['anger'] = min(0.90, emotion_scores['anger'] * 1.5)
+                total = sum(emotion_scores.values())
+                if total > 0:
+                    emotion_scores = {k: v/total for k, v in emotion_scores.items()}
+    
+    # Sort by score and create top emotions list
+    sorted_emotions = sorted(emotion_scores.items(), key=lambda x: x[1], reverse=True)
+    
     top_emotions = []
-    for i in range(min(5, len(labels))):
+    for i in range(min(5, len(sorted_emotions))):
         top_emotions.append({
-            "emotion": labels[i],
-            "score": round(scores[i], 4)
+            "emotion": sorted_emotions[i][0],
+            "score": round(sorted_emotions[i][1], 4)
         })
     
     primary_emotion = top_emotions[0]["emotion"] if top_emotions else "unknown"
@@ -555,8 +760,12 @@ def predict(audio_filepath):
                 sentiment_result
             )
             
-            # Process emotion
-            emotion_data = process_emotion_results(emotion_result)
+            # Process emotion with crisis awareness
+            emotion_data = process_emotion_results(
+                emotion_result, 
+                transcription, 
+                prosodic_features
+            )
             
             print(f"✅ Detected Emotion: {emotion_data['primary']}")
             print(f"✅ Sentiment: {max(sentiment_scores, key=sentiment_scores.get)}")
@@ -579,8 +788,13 @@ def predict(audio_filepath):
                 "analysis": {
                     "mixed_emotions": is_mixed,
                     "hindi_content_percentage": round(hindi_ratio * 100, 2),
-                    "is_crisis": detect_crisis_keywords(transcription),
-                    "has_negation": detect_negation(transcription)
+                    "has_negation": detect_negation(transcription),
+                    "situations": {
+                        "is_crisis": detect_crisis_keywords(transcription),
+                        "is_mental_health_distress": detect_mental_health_distress(transcription),
+                        "is_grief_loss": detect_grief_loss(transcription),
+                        "is_relationship_distress": detect_relationship_distress(transcription)
+                    }
                 },
                 "prosodic_features": {
                     "pitch_mean": round(prosodic_features['pitch_mean'], 2),
@@ -632,8 +846,10 @@ demo = gr.Interface(
     
     ### ✨ Features:
     - **🎙️ Indic Conformer 600M** - State-of-the-art multilingual ASR
-    - **🎭 Zero-Shot Emotion Detection** - 11 emotions using joeddav/xlm-roberta-large-xnli
+    - **🎭 Zero-Shot Emotion Detection** - 13 emotions using joeddav/xlm-roberta-large-xnli
     - **💭 Sentiment Analysis** - Positive/Neutral/Negative classification
+    - **🚨 Multi-Situation Awareness** - Detects crisis, mental health, grief, relationship distress
+    - **🧠 Context-Aware Adjustment** - Emotions adjusted based on detected situations
     - **⚡ Optimized Processing** - 2-3x faster with batch feature extraction
     - **🎵 Voice Analysis** - Fast pitch (PYIN), energy, and spectral features
     - **🌐 Hinglish Support** - Works with Hindi + English mix
@@ -666,8 +882,13 @@ demo = gr.Interface(
       "analysis": {
         "mixed_emotions": false,
         "hindi_content_percentage": 100.0,
-        "is_crisis": false,
-        "has_negation": false
+        "has_negation": false,
+        "situations": {
+          "is_crisis": false,
+          "is_mental_health_distress": false,
+          "is_grief_loss": false,
+          "is_relationship_distress": false
+        }
       },
       "prosodic_features": {
         "pitch_mean": 180.45,
@@ -679,18 +900,46 @@ demo = gr.Interface(
     }
     ```
     
-    ### 🎯 Supported Emotions (11):
+    ### 🎯 Supported Emotions (13):
     - **Positive**: joy, happiness, love, excitement, calm
-    - **Negative**: sadness, anger, fear, frustration
+    - **Negative**: sadness, anger, fear, distress, panic, frustration
     - **Neutral**: neutral, surprise
     
+    ### 🎯 Situation Detection:
+    
+    **🚨 Crisis/Emergency:**
+    - Violence, assault, abuse
+    - Medical emergencies
+    - Suicide/self-harm
+    - Accidents, fire, danger
+    - Keywords: बचाओ, मदद, मार, खून, दर्द, आग, etc.
+    
+    **🧠 Mental Health Distress:**
+    - Depression, anxiety
+    - Hopelessness, isolation
+    - Requires 2+ indicators
+    - Keywords: अवसाद, अकेला, निराश, थक गया, etc.
+    
+    **💔 Grief & Loss:**
+    - Death of loved ones
+    - Mourning, sorrow
+    - Keywords: गुज़र गया, खो दिया, याद आती है, etc.
+    
+    **💔 Relationship Distress:**
+    - Breakup, divorce
+    - Betrayal, cheating
+    - Conflict, arguments
+    - Keywords: तलाक, धोखा, झगड़ा, छोड़ दिया, etc.
+    
     ### 🧪 Test Examples:
     - **😊 Joy**: "मैं बहुत खुश हूं आज"
     - **😢 Sadness**: "मुझे बहुत दुख हो रहा है"
     - **😠 Anger**: "मुझे बहुत गुस्सा आ रहा है"
     - **😨 Fear**: "मुझे डर लग रहा है"
-    - **😐 Calm**: "सब ठीक है, मैं शांत हूं"
-    - **❤️ Love**: "मुझे तुमसे बहुत प्यार है"
+    - **🚨 Crisis**: "बचाओ बचाओ मुझे कोई मदद करो"
+    - **🧠 Mental Health**: "मैं बहुत अकेला और निराश महसूस कर रहा हूं"
+    - **💔 Grief**: "मेरे पिताजी गुज़र गए, बहुत याद आती है"
+    - **💔 Relationship**: "मेरी पत्नी ने मुझे छोड़ दिया, बहुत दुख है"
     
     ### 💡 API Usage:
     
@@ -713,10 +962,34 @@ demo = gr.Interface(
     ```
     
     **Performance Optimizations:**
-    - ⚡ 2-3x faster emotion classification (reduced labels from 30 to 11)
+    - ⚡ 2-3x faster emotion classification (optimized to 13 labels)
     - 🎵 3-5x faster pitch detection (PYIN vs piptrack)
     - 💾 Cached audio resampler (no redundant object creation)
     - 📊 Batch spectral feature extraction (single STFT pass)
+    
+    **🚨 Multi-Situation Awareness:**
+    
+    **Crisis Detection (4x boost):**
+    - 100+ emergency keywords in Hindi/English
+    - Violence, medical, suicide, accidents, fire
+    - Boosts: fear, distress, panic, anger
+    - Suppresses: surprise, excitement, joy (85%)
+    
+    **Mental Health (2x boost):**
+    - Depression, anxiety, isolation indicators
+    - Requires 2+ keywords for detection
+    - Boosts: sadness, fear, frustration
+    - Suppresses: happiness, excitement (70%)
+    
+    **Grief/Loss (2.5x boost):**
+    - Death, mourning, bereavement
+    - Boosts: sadness primarily
+    - Suppresses: joy, excitement (80%)
+    
+    **Relationship Distress (1.8x boost):**
+    - Breakup, divorce, betrayal
+    - Boosts: sadness, anger, frustration
+    - Maintains nuanced emotional detection
     """,
     theme=gr.themes.Soft(),
     flagging_mode="never",