Create app.py

app.py

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+import streamlit as st
+from gtts import gTTS
+from io import BytesIO
+import json
+import datetime
+import re
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+# Page config
+st.set_page_config(
+    page_title="شفیق - AI Mental Health Assistant",
+    page_icon="🧠",
+    layout="wide"
+)
+
+# ============== LOAD MENTAL HEALTH BERT MODEL ==============
+
+@st.cache_resource
+def load_mental_health_model():
+    """Load the mental health diagnosis model"""
+    try:
+        # Primary model: Mental Health BERT
+        model_name = "mental/mental-roberta-base"
+        
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        
+        # Create pipeline
+        classifier = pipeline(
+            "text-classification",
+            model=model,
+            tokenizer=tokenizer,
+            return_all_scores=True,
+            device=-1  # Use CPU (change to 0 if you have GPU)
+        )
+        
+        return classifier, model.config.id2label
+    except Exception as e:
+        st.error(f"Model loading error: {e}")
+        return None, None
+
+@st.cache_resource
+def load_emotion_model():
+    """Load emotion detection model"""
+    try:
+        emotion_classifier = pipeline(
+            "text-classification",
+            model="j-hartmann/emotion-english-distilroberta-base",
+            return_all_scores=True,
+            device=-1
+        )
+        return emotion_classifier
+    except:
+        return None
+
+@st.cache_resource
+def load_suicide_risk_model():
+    """Load suicide risk detection model"""
+    try:
+        # Using a general classifier for risk assessment
+        risk_classifier = pipeline(
+            "text-classification",
+            model="distilbert-base-uncased-finetuned-sst-2-english",
+            device=-1
+        )
+        return risk_classifier
+    except:
+        return None
+
+# Load models
+with st.spinner("🔄 AI models loading... please wait"):
+    mental_health_classifier, id2label = load_mental_health_model()
+    emotion_classifier = load_emotion_model()
+    risk_classifier = load_suicide_risk_model()
+
+# ============== CSS STYLING ==============
+
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Noto+Nastaliq+Urdu&display=swap');
+    
+    .urdu-text {
+        font-family: 'Noto Nastaliq Urdu', 'Jameel Noori Nastaleeq', serif;
+        direction: rtl;
+        text-align: right;
+        line-height: 2.5;
+        font-size: 20px;
+    }
+    
+    .diagnosis-box {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        padding: 20px;
+        border-radius: 15px;
+        margin: 10px 0;
+    }
+    
+    .risk-high { border-left: 5px solid #ff4757; background: #ffebee; }
+    .risk-medium { border-left: 5px solid #ffa502; background: #fff3e0; }
+    .risk-low { border-left: 5px solid #2ed573; background: #e8f5e9; }
+    
+    .metric-card {
+        background: white;
+        padding: 15px;
+        border-radius: 10px;
+        box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        text-align: center;
+    }
+    
+    .severity-critical { color: #ff4757; font-weight: bold; }
+    .severity-high { color: #ff6348; font-weight: bold; }
+    .severity-moderate { color: #ffa502; font-weight: bold; }
+    .severity-low { color: #2ed573; font-weight: bold; }
+</style>
+""", unsafe_allow_html=True)
+
+# ============== MENTAL HEALTH ANALYSIS FUNCTIONS ==============
+
+def analyze_mental_health(text):
+    """
+    Use BERT model to analyze mental health conditions
+    Returns: dict with conditions, scores, and severity
+    """
+    results = {
+        'primary_condition': 'unknown',
+        'confidence': 0.0,
+        'all_conditions': {},
+        'severity': 'low',
+        'risk_factors': [],
+        'recommendations': []
+    }
+    
+    if mental_health_classifier is None:
+        return fallback_analysis(text)
+    
+    try:
+        # Get predictions from BERT model
+        predictions = mental_health_classifier(text[:512])  # Limit text length
+        
+        if predictions and len(predictions) > 0:
+            scores = predictions[0]
+            
+            # Sort by score
+            sorted_scores = sorted(scores, key=lambda x: x['score'], reverse=True)
+            
+            # Get primary condition
+            primary = sorted_scores[0]
+            results['primary_condition'] = primary['label']
+            results['confidence'] = round(primary['score'] * 100, 2)
+            
+            # Store all conditions
+            for item in sorted_scores:
+                results['all_conditions'][item['label']] = round(item['score'] * 100, 2)
+            
+            # Determine severity based on confidence and condition type
+            high_risk_conditions = ['suicidal', 'self-harm', 'severe-depression', 'psychosis']
+            medium_risk_conditions = ['depression', 'anxiety', 'ptsd', 'bipolar']
+            
+            if results['primary_condition'].lower() in high_risk_conditions or results['confidence'] > 85:
+                results['severity'] = 'critical'
+            elif results['primary_condition'].lower() in medium_risk_conditions or results['confidence'] > 70:
+                results['severity'] = 'moderate-high'
+            elif results['confidence'] > 50:
+                results['severity'] = 'moderate'
+            else:
+                results['severity'] = 'low'
+            
+            # Extract risk factors from text
+            results['risk_factors'] = extract_risk_factors(text)
+            
+            # Generate recommendations
+            results['recommendations'] = generate_recommendations(
+                results['primary_condition'], 
+                results['severity']
+            )
+    
+    except Exception as e:
+        st.error(f"Analysis error: {e}")
+        return fallback_analysis(text)
+    
+    return results
+
+def fallback_analysis(text):
+    """Fallback when BERT model fails"""
+    text_lower = text.lower()
+    
+    # Keyword-based fallback
+    conditions = {
+        'depression': ['اداس', 'مایوس', 'udas', 'mayoos', 'hopeless', 'khamoshi', 'تنہا'],
+        'anxiety': ['پریشان', 'ghabrahat', 'tension', 'fikar', 'bechaini', 'گھبراہٹ'],
+        'ptsd': ['خوف', 'khoof', 'nightmare', 'flashback', 'حادثہ', 'trauma'],
+        'suicidal': ['خودکشی', 'mar jaun', 'موت', 'zehar', 'مرنا', 'khatam'],
+        'stress': ['tension', 'دباؤ', 'stress', 'bojh', 'بوجھ', 'pressure']
+    }
+    
+    detected = {}
+    for condition, keywords in conditions.items():
+        score = sum(1 for kw in keywords if kw in text_lower)
+        if score > 0:
+            detected[condition] = min(score * 20, 100)
+    
+    if not detected:
+        return {
+            'primary_condition': 'unknown',
+            'confidence': 0,
+            'all_conditions': {},
+            'severity': 'low',
+            'risk_factors': [],
+            'recommendations': ['general_support']
+        }
+    
+    primary = max(detected, key=detected.get)
+    return {
+        'primary_condition': primary,
+        'confidence': detected[primary],
+        'all_conditions': detected,
+        'severity': 'moderate' if detected[primary] > 50 else 'low',
+        'risk_factors': extract_risk_factors(text),
+        'recommendations': generate_recommendations(primary, 'moderate')
+    }
+
+def extract_risk_factors(text):
+    """Extract specific risk factors from text"""
+    text_lower = text.lower()
+    factors = []
+    
+    risk_indicators = {
+        'sleep_issues': ['نیند', 'neend', 'neend nahi', 'جاگنا', 'so nahi pa raha'],
+        'social_isolation': ['اکیلا', 'tanha', 'koi nahi', 'دور', 'alone'],
+        'substance_abuse': ['شراب', 'drugs', 'nasha', 'سیگریٹ', 'smoking'],
+        'self_harm_history': ['زخم', 'cutting', 'khud ko chot', 'خون'],
+        'family_history': ['ghar mein', 'والدین', 'maa baap', 'خاندان'],
+        'work_stress': ['نوکری', 'job', 'kaam', 'boss', 'office', 'پیسے']
+    }
+    
+    for factor, keywords in risk_indicators.items():
+        if any(kw in text_lower for kw in keywords):
+            factors.append(factor)
+    
+    return factors
+
+def generate_recommendations(condition, severity):
+    """Generate therapeutic recommendations"""
+    recommendations = {
+        'critical': [
+            "🚨 فوری پیشہ ورانہ مدد ضروری ہے",
+            "کسی قریبی ہسپتال یا کلینک جائیں",
+            "کسی قریبی رشتہ دار کو مطلع کریں",
+            "ہیلپ لائن 1122 پر کال کریں"
+        ],
+        'moderate-high': [
+            "پیشہ ورانہ مدد مشورہ دینا چاہیے",
+            "نفسیاتی ماہر سے ملاقات کریں",
+            "مستقل مانیٹرنگ ضروری ہے",
+            "دواؤں پر غور کریں"
+        ],
+        'moderate': [
+            "کاؤنسلنگ سے فائدہ ہوگا",
+            "مشقیں اور تھراپی جاری رکھیں",
+            "دوستوں سے بات کریں",
+            "ورزش اور冥思 کریں"
+        ],
+        'low': [
+            "خود مدد کی تکنیکیں استعمال کریں",
+            "مثبت سرگرمیاں جاری رکھیں",
+            "ضرورت ہو تو کاؤنسلنگ کریں"
+        ]
+    }
+    
+    specific_recs = {
+        'depression': ["روزانہ شیڈول بنائیں", "کھیل کود میں حصہ لیں", "نیند درست کریں"],
+        'anxiety': ["گہری سانسیں لیں", "زمینی حقیقتوں پر توجہ دیں", "پیشہ ورانہ مدد لیں"],
+        'ptsd': ["ٹریما سے نمٹنے کی تربیت", "محفوظ ماحول بنائیں", "پیشہ ورانہ تھراپی"],
+        'suicidal': ["فوری ��سپتال جائیں", "کسی کو بتائیں", "ہتھیار دور رکھیں"]
+    }
+    
+    base_recs = recommendations.get(severity, recommendations['low'])
+    specific = specific_recs.get(condition, [])
+    
+    return base_recs + specific
+
+def analyze_emotion_enhanced(text):
+    """Enhanced emotion analysis using BERT"""
+    if emotion_classifier:
+        try:
+            results = emotion_classifier(text[:512])
+            if results:
+                emotions = {item['label']: item['score'] for item in results[0]}
+                dominant = max(emotions, key=emotions.get)
+                return dominant, emotions
+        except:
+            pass
+    
+    # Fallback to keyword
+    return detect_emotion_fallback(text)
+
+def detect_emotion_fallback(text):
+    """Fallback emotion detection"""
+    text_lower = text.lower()
+    
+    emotion_keywords = {
+        'sadness': ['اداس', 'rona', 'udas', 'غم', 'dukh', 'tanha'],
+        'fear': ['ڈر', 'khoof', 'ghabrahat', 'fear', 'خوف'],
+        'anger': ['غصہ', 'gussa', 'naraz', 'angry'],
+        'joy': ['خوش', 'khush', 'happy', 'khushi'],
+        'surprise': ['حیران', 'heran', ' shocked', 'واہ'],
+        'disgust': ['نفرت', 'nafrat', 'گھن', 'ghin']
+    }
+    
+    scores = {}
+    for emotion, keywords in emotion_keywords.items():
+        scores[emotion] = sum(1 for kw in keywords if kw in text_lower)
+    
+    if max(scores.values()) == 0:
+        return 'neutral', {'neutral': 1.0}
+    
+    dominant = max(scores, key=scores.get)
+    total = sum(scores.values())
+    normalized = {k: v/total for k, v in scores.items()}
+    
+    return dominant, normalized
+
+def get_therapeutic_response_enhanced(mental_health_data, emotion, text):
+    """Generate response based on BERT diagnosis"""
+    condition = mental_health_data['primary_condition']
+    severity = mental_health_data['severity']
+    confidence = mental_health_data['confidence']
+    
+    # Crisis response for critical cases
+    if severity == 'critical':
+        return """
+        🚨 **اہم انتباہ / Critical Alert**
+        
+        میں نوٹ کر رہا ہوں کہ آپ بہت پریشان ہیں۔ آپ کی زندگی قیمتی ہے۔
+        
+        **فوری اقدامات:**
+        - 📞 ہیلپ لائن: 1122
+        - 🏥 قریبی ہسپتال جائیں
+        - 👨‍👩‍👧 کسی کو بتائیں
+        
+        آپ اکیلے نہیں ہیں۔ مدد دستیاب ہے۔
+        """
+    
+    # Condition-specific responses
+    responses = {
+        'depression': [
+            f"میں سمجھتا ہوں آپ {condition} کا سامنا کر رہے ہیں ({confidence}% یقین)۔",
+            "یہ ایک طبی حالت ہے جو علاج سے ٹھیک ہو سکتی ہے۔",
+            "پیشہ ورانہ مدد سے آپ بہتر محسوس کریں گے۔"
+        ],
+        'anxiety': [
+            f"آپ کے {condition} کی نشانیاں نظر آ رہی ہیں ({confidence}% یقین)۔",
+            "گہری سانسیں اور زمینی تکنیکیں مددگار ثابت ہو سکتی ہیں۔",
+            "یہ قابل علاج ہے، امید رکھیں۔"
+        ],
+        'ptsd': [
+            f"ممکنہ طور پر {condition} کے اثرات ({confidence}% یقین)۔",
+            "ٹریما بہت گہرا ہوتا ہے، پیشہ ورانہ مدد ضروری ہے۔",
+            "آپ محفوظ ہیں، یہ احساس گزر جائے گا۔"
+        ],
+        'stress': [
+            "زندگی کے دباؤ آپ پر بھاری ہو رہے ہیں۔",
+            "چھوٹے وقفے لیں، خود کو ترجیح دیں۔",
+            "تناؤ کا نظم سیکھنا ضروری ہے۔"
+        ]
+    }
+    
+    base_response = responses.get(condition, responses.get('stress', ["میں آپ کی مدد کرنا چاہتا ہوں۔"]))
+    
+    # Add recommendations
+    recs = mental_health_data.get('recommendations', [])
+    if recs:
+        base_response.append("\n**سفارشات:**")
+        for i, rec in enumerate(recs[:3], 1):
+            base_response.append(f"{i}. {rec}")
+    
+    return "\n\n".join(base_response)
+
+def text_to_speech(text):
+    """Convert text to Urdu speech"""
+    try:
+        # Clean text for TTS
+        clean_text = re.sub(r'[^\w\s\u0600-\u06FF]', ' ', text)
+        clean_text = clean_text[:500]  # Limit length
+        
+        tts = gTTS(text=clean_text, lang='ur', slow=False)
+        mp3 = BytesIO()
+        tts.write_to_fp(mp3)
+        mp3.seek(0)
+        return mp3
+    except:
+        return None
+
+# ============== MAIN APP ==============
+
+def main():
+    # Header
+    st.markdown('<h1 style="text-align: center; color: #667eea;">🧠 شفیق Pro</h1>', 
+                unsafe_allow_html=True)
+    st.markdown('<h4 style="text-align: center; color: #666;">AI-Powered Mental Health Assistant</h4>', 
+                unsafe_allow_html=True)
+    
+    # Initialize session
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = []
+        st.session_state.diagnosis_history = []
+    
+    # Sidebar - Diagnosis Dashboard
+    with st.sidebar:
+        st.header("📊 طبی تجزیہ / Medical Analysis")
+        
+        if st.session_state.diagnosis_history:
+            latest = st.session_state.diagnosis_history[-1]
+            
+            # Severity indicator
+            severity = latest['severity']
+            severity_class = f"severity-{severity.replace('-', '')}"
+            
+            st.markdown(f"""
+            <div class="metric-card {severity_class}">
+                <h3>سنگینی / Severity</h3>
+                <h2>{severity.upper()}</h2>
+            </div>
+            """, unsafe_allow_html=True)
+            
+            # Primary condition
+            st.write(f"**Primary Condition:** {latest['primary_condition']}")
+            st.write(f"**Confidence:** {latest['confidence']}%")
+            
+            # Risk factors
+            if latest['risk_factors']:
+                st.write("**Risk Factors:**")
+                for factor in latest['risk_factors']:
+                    st.write(f"- {factor}")
+            
+            # History chart
+            if len(st.session_state.diagnosis_history) > 1:
+                st.write("**Trend:**")
+                conditions = [d['primary_condition'] for d in st.session_state.diagnosis_history]
+                st.bar_chart(pd.Series(conditions).value_counts())
+        
+        st.markdown("---")
+        st.info("""
+        ⚠️ **Disclaimer:** This AI provides preliminary screening only.
+        Not a substitute for professional psychiatric evaluation.
+        """)
+        
+        if st.button("🗑️ New Session"):
+            st.session_state.chat_history = []
+            st.session_state.diagnosis_history = []
+            st.rerun()
+    
+    # Main chat area
+    st.markdown("---")
+    
+    # Display chat
+    for msg in st.session_state.chat_history:
+        if msg['role'] == 'user':
+            st.markdown(f"""
+            <div style="background: #f3e5f5; padding: 15px; border-radius: 15px; 
+                        text-align: right; margin: 10px 0;">
+                <strong>👤 You:</strong><br>
+                <span class="urdu-text">{msg['content']}</span>
+            </div>
+            """, unsafe_allow_html=True)
+        else:
+            st.markdown(f"""
+            <div class="diagnosis-box urdu-text">
+                <strong>🤖 شفیق:</strong><br>
+                {msg['content']}
+                <br><small>Diagnosis: {msg.get('diagnosis', 'N/A')} | 
+                Confidence: {msg.get('confidence', 0)}%</small>
+            </div>
+            """, unsafe_allow_html=True)
+            if msg.get('audio'):
+                st.audio(msg['audio'], format='audio/mp3')
+    
+    # Input
+    st.markdown("---")
+    
+    col1, col2 = st.columns([4, 1])
+    with col1:
+        user_input = st.text_input("Message / پیغام...", 
+                                  key="input",
+                                  placeholder="اپنے جذبات بیان کریں...")
+    with col2:
+        send = st.button("📤 Send", use_container_width=True)
+    
+    if send and user_input:
+        with st.spinner("Analyzing with AI..."):
+            # Run BERT analysis
+            mental_health_data = analyze_mental_health(user_input)
+            
+            # Get emotion
+            emotion, emotion_scores = analyze_emotion_enhanced(user_input)
+            
+            # Generate response
+            response = get_therapeutic_response_enhanced(
+                mental_health_data, emotion, user_input
+            )
+            
+            # Text to speech
+            audio = text_to_speech(response)
+            
+            # Save to history
+            st.session_state.chat_history.append({
+                'role': 'user',
+                'content': user_input
+            })
+            st.session_state.chat_history.append({
+                'role': 'bot',
+                'content': response,
+                'diagnosis': mental_health_data['primary_condition'],
+                'confidence': mental_health_data['confidence'],
+                'audio': audio
+            })
+            
+            st.session_state.diagnosis_history.append(mental_health_data)
+        
+        st.rerun()
+
+if __name__ == "__main__":
+    import pandas as pd
+    main()