app.py

from datasets import load_dataset
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
from sentence_transformers import SentenceTransformer
import numpy as np
import random
import faiss
import json
import logging
import re
import streamlit as st
from datetime import datetime
import os

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ============================
# DATA PREPARATION
# ============================

 
def prepare_dataset():
    """Load and prepare the emotion dataset"""
    print("📊 Loading emotion dataset...")
    
    # Load the dataset
    ds = load_dataset("cardiffnlp/tweet_eval", "emotion")
    
    # Define emotion labels (matching the dataset)
    emotion_labels = ["anger", "joy", "optimism", "sadness"]
    
    def clean_text(text):
        """Clean and preprocess text"""
        text = text.lower()
        text = re.sub(r"http\S+", "", text)  # remove URLs
        text = re.sub(r"[^\w\s]", "", text)  # remove special characters
        text = re.sub(r"\d+", "", text)  # remove numbers
        text = re.sub(r"\s+", " ", text)  # normalize whitespace
        return text.strip()
    
    # Sample and prepare training data
    train_data = ds['train']
    train_sample = random.sample(list(train_data), min(1000, len(train_data)))
    
    # Convert to RAG format
    rag_json = []
    for row in train_sample:
        cleaned_text = clean_text(row['text'])
        if len(cleaned_text) > 10:  # Filter out very short texts
            rag_json.append({
                "text": cleaned_text,
                "emotion": emotion_labels[row['label']],
                "original_text": row['text']
            })
    
    print(f"Dataset prepared with {len(rag_json)} samples")
    return rag_json

# ============================
# EMOTION DETECTION MODEL
# ============================

class EmotionDetector:
    def __init__(self):
        self.model_name = "bhadresh-savani/distilbert-base-uncased-emotion"
        
        try:
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
            self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name)
            self.classifier = pipeline(
                "text-classification", 
                model=self.model, 
                tokenizer=self.tokenizer, 
                return_all_scores=False
            )
        except Exception as e:
            st.error(f"❌ Error loading emotion model: {e}")
            raise
    
    def detect_emotion(self, text):
        """Detect emotion from text"""
        try:
            result = self.classifier(text)
            emotion = result[0]['label'].lower()
            confidence = result[0]['score']
            
            # Map model outputs to our emotion categories
            emotion_mapping = {
                'anger': 'anger',
                'joy': 'joy', 
                'love': 'joy',
                'happiness': 'joy',
                'sadness': 'sadness',
                'fear': 'sadness',
                'surprise': 'optimism',
                'optimism': 'optimism'
            }
            
            mapped_emotion = emotion_mapping.get(emotion, 'optimism')
            return mapped_emotion, confidence
            
        except Exception as e:
            logger.error(f"Error in emotion detection: {e}")
            return 'optimism', 0.5

# ============================
# RAG SYSTEM WITH FAISS
# ============================

class RAGSystem:
    def __init__(self, rag_data):
        self.rag_data = rag_data
        self.texts = [entry['text'] for entry in rag_data]
        
        # Initialize embedding model
        self.embed_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
        
        # Create embeddings
        self.embeddings = self.embed_model.encode(
            self.texts, 
            convert_to_numpy=True, 
            show_progress_bar=False
        )
        
        # Create FAISS index
        dimension = self.embeddings.shape[1]
        self.index = faiss.IndexFlatL2(dimension)
        self.index.add(self.embeddings)
        
    def retrieve_templates(self, user_input, detected_emotion, top_k=3):
        """Retrieve relevant templates based on emotion and similarity"""
        
        # Filter by emotion first
        emotion_filtered_indices = [
            i for i, entry in enumerate(self.rag_data) 
            if entry['emotion'] == detected_emotion
        ]
        
        if not emotion_filtered_indices:
            emotion_filtered_indices = list(range(len(self.rag_data)))
        
        # Get filtered embeddings
        filtered_embeddings = self.embeddings[emotion_filtered_indices]
        filtered_texts = [self.texts[i] for i in emotion_filtered_indices]
        
        # Create temporary index for filtered data
        temp_index = faiss.IndexFlatL2(filtered_embeddings.shape[1])
        temp_index.add(filtered_embeddings)
        
        # Search for similar templates
        user_embedding = self.embed_model.encode([user_input], convert_to_numpy=True)
        distances, indices = temp_index.search(
            user_embedding, 
            min(top_k, len(filtered_texts))
        )
        
        # Get top templates
        top_templates = [filtered_texts[i] for i in indices[0]]
        
        return top_templates

# ============================
# RESPONSE GENERATOR
# ============================

class ResponseGenerator:
    def __init__(self, emotion_detector, rag_system):
        self.emotion_detector = emotion_detector
        self.rag_system = rag_system
        
        # Empathetic response templates by emotion
        self.response_templates = {
            'anger': [
                "I can understand why you're feeling frustrated. It's completely valid to feel this way.",
                "Your anger is understandable. Sometimes situations can be really challenging.",
                "I hear that you're upset, and that's okay. These feelings are important."
            ],
            'sadness': [
                "I'm sorry you're going through a difficult time. Your feelings are valid.",
                "It sounds like you're dealing with something really tough right now.",
                "I can sense your sadness, and I want you to know that it's okay to feel this way."
            ],
            'joy': [
                "I'm so happy to hear about your positive experience! That's wonderful.",
                "Your joy is contagious! It's great to hear such positive news.",
                "I love hearing about things that make you happy. That sounds amazing!"
            ],
            'optimism': [
                "Your positive outlook is inspiring. That's a great way to look at things.",
                "I appreciate your hopeful perspective. That's really encouraging.",
                "It's wonderful to hear your optimistic thoughts. Keep that positive energy!"
            ]
        }
    
    def generate_response(self, user_input, top_k=3):
        """Generate empathetic response using RAG and few-shot prompting"""
        
        try:
            # Step 1: Detect emotion
            detected_emotion, confidence = self.emotion_detector.detect_emotion(user_input)
            
            # Step 2: Retrieve relevant templates
            templates = self.rag_system.retrieve_templates(
                user_input, detected_emotion, top_k=top_k
            )
            
            # Step 3: Create response using templates and emotion
            base_responses = self.response_templates.get(
                detected_emotion, 
                self.response_templates['optimism']
            )
            
            # Combine base response with context from templates
            selected_base = random.choice(base_responses)
            
            # Create contextual response
            if templates:
                context_template = random.choice(templates)
                # Enhanced response generation
                response = f"{selected_base} I can relate to what you're sharing - {context_template[:80]}. Remember that your feelings are important and valid."
            else:
                response = selected_base
            
            # Add disclaimer
            disclaimer = "\n\n⚠️ This is an automated response. For serious emotional concerns, please consult a mental health professional."
            
            return response + disclaimer, detected_emotion, confidence
            
        except Exception as e:
            error_msg = f"I apologize, but I encountered an error: {str(e)}"
            disclaimer = "\n\n⚠️ This is an automated response. Please consult a professional if needed."
            return error_msg + disclaimer, 'neutral', 0.0

# ============================
# STREAMLIT APP
# ============================

def main():
    # Page config
    st.set_page_config(
        page_title="Empathetic Chatbot",
        page_icon="💬",
        layout="wide",
        initial_sidebar_state="expanded"
    )
    
    # Custom CSS
    st.markdown("""

    <style>

    .stApp {

        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);

    }

    .main-header {

        background: rgba(255,255,255,0.1);

        padding: 1rem;

        border-radius: 10px;

        text-align: center;

        margin-bottom: 2rem;

    }

    .chat-message {

    padding: 1rem;

    margin: 0.5rem 0;

    border-radius: 10px;

    background: rgba(255,255,255,0.9);

    max-width: 70%;       /* limit bubble width */

}



.user-message {

    background: rgba(100, 149, 237, 0.2);

    margin-left: auto;     /* push to right */

    margin-right: 1rem;    /* spacing from edge */

    text-align: left;      /* keep text aligned inside bubble */

}



.bot-message {

    background: rgba(50, 205, 50, 0.1);

    margin-left: 1rem;     /* spacing from left edge */

    margin-right: auto;    /* push to left */

    text-align: left;

}



    .emotion-badge {

        display: inline-block;

        padding: 0.25rem 0.5rem;

        border-radius: 15px;

        font-size: 0.8rem;

        font-weight: bold;

        margin: 0.25rem;

    }

    .anger { background-color: #ffebee; color: #c62828; }

    .sadness { background-color: #e3f2fd; color: #1565c0; }

    .joy { background-color: #f3e5f5; color: #7b1fa2; }

    .optimism { background-color: #e8f5e8; color: #2e7d32; }

    </style>

    """, unsafe_allow_html=True)
    
    # Header
    st.markdown("""

    <div class="main-header">

        <h1>💬 Emotion-Aware Empathetic Chatbot</h1>

        <p>Your AI companion for emotional support and understanding</p>

    </div>

    """, unsafe_allow_html=True)
    
    # Initialize session state
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = []
    
    if "initialized" not in st.session_state:
        initialize_chatbot()
    
    # Sidebar
    with st.sidebar:
        st.header("🎛️ Controls")
        
        # Statistics
        if st.session_state.chat_history:
            emotions = [chat['emotion'] for chat in st.session_state.chat_history if 'emotion' in chat]
            if emotions:
                emotion_counts = {}
                for emotion in emotions:
                    emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1
                
                st.subheader("📊 Emotion Statistics")
                for emotion, count in emotion_counts.items():
                    st.markdown(f'<span class="emotion-badge {emotion}">{emotion.title()}: {count}</span>', unsafe_allow_html=True)
        
        st.markdown("---")
        
        # Test buttons
        if st.button("🧪 Test Emotion Detection"):
            test_emotion_detection()
        
        if st.button("🗑️ Clear Chat History"):
            st.session_state.chat_history = []
            st.rerun()
        
        st.markdown("---")
        
        # Sample messages
        st.subheader("💡 Try these sample messages:")
        sample_messages = [
            "I'm feeling really happy today!",
            "I'm so frustrated with everything",
            "I feel really sad and alone",
            " I’m really surprised about what happend!"
        ]
        
        for msg in sample_messages:
            if st.button(f"📝 {msg[:20]}...", key=f"sample_{msg}"):
                process_message(msg)
    
    # Main chat interface
    col1, col2 = st.columns([3, 1])
    
    with col1:
        st.subheader("💭 Chat Interface")
        
        # Display chat history
        chat_container = st.container()
        with chat_container:
            if st.session_state.chat_history:
                for i, chat in enumerate(st.session_state.chat_history[-10:]):  # Show last 10, in chronological order
                    # User message
                    st.markdown(f"""

                    <div class="chat-message user-message">

                        <strong>🧑 You ({chat['timestamp']}):</strong><br>

                        💭 {chat['user']}

                    </div>

                    """, unsafe_allow_html=True)
                    
                    # Bot response with emotion
                    emotion_class = chat.get('emotion', 'optimism')
                    confidence = chat.get('confidence', 0.0)
                    
                    st.markdown(f"""

                    <div class="chat-message bot-message">

                        <strong>🤖 Bot:</strong>

                        <span class="emotion-badge {emotion_class}">

                            {emotion_class.title()} ({confidence:.2f})

                        </span><br>

                        💝 {chat['bot']}

                    </div>

                    """, unsafe_allow_html=True)
                    
                    st.markdown("---")
        
        # User input at the bottom
        user_input = st.text_input(
            "Your message:",
            placeholder="Type how you're feeling...",
            key="user_input",
            help="Share your thoughts and emotions"
        )
        
        col_send = st.columns([1])[0]
        
        with col_send:
            if st.button("Send 📤", type="primary", use_container_width=True):
                if user_input.strip():
                    process_message(user_input)
                    st.rerun()
                else:
                    st.warning("⚠️ Please enter a message.")
        
    with col2:
        st.subheader("ℹ️ About")
        st.info("""

        This chatbot uses:

        - **Emotion Detection**: Identifies your emotional state

        - **RAG System**: Retrieves relevant response templates

        - **Empathetic Responses**: Provides caring, supportive replies

        """)

def initialize_chatbot():
    """Initialize the chatbot systems"""
    try:
        with st.spinner("🚀 Initializing chatbot systems..."):
            # Prepare dataset
            rag_data = prepare_dataset()
            
            # Initialize systems
            emotion_detector = EmotionDetector()
            rag_system = RAGSystem(rag_data)
            response_generator = ResponseGenerator(emotion_detector, rag_system)
            
            # Store in session state
            st.session_state.response_generator = response_generator
            st.session_state.initialized = True
            
            st.success("✅ Chatbot initialized successfully!")
            
    except Exception as e:
        st.error(f"❌ Initialization failed: {e}")
        st.stop()

def process_message(user_input):
    """Process user message and generate response"""
    if hasattr(st.session_state, 'response_generator'):
        with st.spinner("🤔 Generating response..."):
            response, emotion, confidence = st.session_state.response_generator.generate_response(user_input)
            
            # Add to chat history
            st.session_state.chat_history.append({
                "user": user_input,
                "bot": response,
                "emotion": emotion,
                "confidence": confidence,
                "timestamp": datetime.now().strftime("%H:%M:%S")
            })

def test_emotion_detection():
    """Test emotion detection functionality"""
    if hasattr(st.session_state, 'response_generator'):
        test_messages = [
            "I'm so happy today!",
            "I feel terrible and sad",
            "This makes me really angry",
            " I’m really surprised about what happend!"
        ]
        
        st.subheader("🧪 Emotion Detection Test Results")
        
        for msg in test_messages:
            emotion, confidence = st.session_state.response_generator.emotion_detector.detect_emotion(msg)
            st.markdown(f"""

            **Message:** "{msg}"  

            **Emotion:** <span class="emotion-badge {emotion}">{emotion.title()} ({confidence:.3f})</span>

            """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()