Update app.py

app.py

@@ -4,484 +4,713 @@ import torch.nn as nn
 from transformers import AutoTokenizer, AutoModel
 import numpy as np
 import os
-from typing import Dict, List, Tuple, Optional
+from typing import List, Dict, Tuple
 import json
 from datetime import datetime
-from collections import defaultdict
+import plotly.graph_objects as go
+from collections import Counter
+import time
 
-print("🌊 Sentiment Analyzer Starting...")
+print("🎭 EmotiScan Initializing...")
 
-MODEL_NAME = "roberta-base"
-SENTIMENTS = ["anger", "fear", "joy", "sadness", "surprise"]
-OPTIMIZED_THRESHOLDS = [0.24722222, 0.61666667, 0.59722222, 0.44166667, 0.46111111]
-SEQUENCE_LENGTH = 200
-WEIGHTS_FILE = "roberta.pth"
+# Configuration
+CONFIG = {
+    "model": "roberta-base",
+    "emotions": ["anger", "fear", "joy", "sadness", "surprise"],
+    "thresholds": [0.24722222, 0.61666667, 0.59722222, 0.44166667, 0.46111111],
+    "max_length": 200,
+    "weights_path": "roberta.pth"
+}
 
-SENTIMENT_CONFIG = {
+# Emotion metadata with unique styling
+EMOTION_META = {
     "anger": {
-        "icon": "🔥", 
-        "primary": "#ff6b6b", 
-        "secondary": "#ee5a6f",
-        "bg": "linear-gradient(120deg, #ff6b6b 0%, #c92a2a 100%)",
-        "label": "Anger Detected",
-        "levels": ["Annoyed", "Frustrated", "Angry", "Enraged"]
+        "emoji": "😠", "color": "#E74C3C", "gradient": ["#E74C3C", "#C0392B"],
+        "description": "Hostile or irritated state", "intensity_labels": ["Mild", "Moderate", "High", "Extreme"]
     },
     "fear": {
-        "icon": "⚡", 
-        "primary": "#845ef7", 
-        "secondary": "#7048e8",
-        "bg": "linear-gradient(120deg, #845ef7 0%, #5f3dc4 100%)",
-        "label": "Fear Detected",
-        "levels": ["Worried", "Anxious", "Fearful", "Terrified"]
+        "emoji": "😨", "color": "#9B59B6", "gradient": ["#9B59B6", "#8E44AD"],
+        "description": "Anxiety or apprehension", "intensity_labels": ["Uneasy", "Concerned", "Frightened", "Panicked"]
     },
     "joy": {
-        "icon": "✨", 
-        "primary": "#ffd43b", 
-        "secondary": "#fab005",
-        "bg": "linear-gradient(120deg, #ffd43b 0%, #f59f00 100%)",
-        "label": "Joy Detected",
-        "levels": ["Content", "Happy", "Joyful", "Ecstatic"]
+        "emoji": "😊", "color": "#F39C12", "gradient": ["#F39C12", "#E67E22"],
+        "description": "Positive emotional state", "intensity_labels": ["Pleasant", "Cheerful", "Delighted", "Euphoric"]
     },
     "sadness": {
-        "icon": "💧", 
-        "primary": "#4dabf7", 
-        "secondary": "#339af0",
-        "bg": "linear-gradient(120deg, #4dabf7 0%, #1971c2 100%)",
-        "label": "Sadness Detected",
-        "levels": ["Melancholic", "Sad", "Sorrowful", "Devastated"]
+        "emoji": "😢", "color": "#3498DB", "gradient": ["#3498DB", "#2980B9"],
+        "description": "Melancholic emotional tone", "intensity_labels": ["Down", "Unhappy", "Distressed", "Grieving"]
     },
     "surprise": {
-        "icon": "💫", 
-        "primary": "#ff6bc2", 
-        "secondary": "#e64980",
-        "bg": "linear-gradient(120deg, #ff6bc2 0%, #c2255c 100%)",
-        "label": "Surprise Detected",
-        "levels": ["Curious", "Surprised", "Shocked", "Stunned"]
+        "emoji": "😲", "color": "#E91E63", "gradient": ["#E91E63", "#C2185B"],
+        "description": "Unexpected reaction", "intensity_labels": ["Interested", "Intrigued", "Amazed", "Astonished"]
     }
 }
 
-class SentimentModel(nn.Module):
-    def __init__(self):
+class EmotionClassifier(nn.Module):
+    """Neural network for emotion classification"""
+    def __init__(self, model_name: str, num_labels: int):
         super().__init__()
-        self.encoder = AutoModel.from_pretrained(MODEL_NAME)
-        self.dropout_layer = nn.Dropout(0.35)
-        self.classifier = nn.Linear(768, 5)
-    
-    def forward(self, tokens, mask):
-        enc_output = self.encoder(input_ids=tokens, attention_mask=mask)
-        pooled_output = enc_output.pooler_output if hasattr(enc_output, "pooler_output") else enc_output.last_hidden_state[:, 0]
-        return self.classifier(self.dropout_layer(pooled_output))
+        self.base_model = AutoModel.from_pretrained(model_name)
+        self.dropout = nn.Dropout(0.35)
+        self.output_layer = nn.Linear(768, num_labels)
+    
+    def forward(self, input_ids, attention_mask):
+        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
+        pooled = outputs.pooler_output if hasattr(outputs, "pooler_output") else outputs.last_hidden_state[:, 0]
+        return self.output_layer(self.dropout(pooled))
 
-class AnalyzerState:
+class EmotiScanEngine:
+    """Core analysis engine"""
     def __init__(self):
-        self.compute_device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.neural_net = None
-        self.text_tokenizer = None
-        self.is_loaded = False
-        self.total_analyses = 0
-        self.analysis_log = []
-        self.sentiment_counts = defaultdict(int)
-
-app_state = AnalyzerState()
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = None
+        self.tokenizer = None
+        self.ready = False
+        self.session_stats = {
+            "total_scans": 0,
+            "emotion_detections": Counter(),
+            "scan_history": [],
+            "session_start": datetime.now()
+        }
+    
+    def load_model(self) -> Tuple[bool, str]:
+        """Initialize model and tokenizer"""
+        try:
+            self.model = EmotionClassifier(CONFIG["model"], len(CONFIG["emotions"]))
+            
+            if os.path.exists(CONFIG["weights_path"]):
+                self.model.load_state_dict(
+                    torch.load(CONFIG["weights_path"], map_location=self.device)
+                )
+                status = "✅ Model loaded with trained weights"
+            else:
+                status = "⚠️ Model initialized without pre-trained weights"
+            
+            self.model.to(self.device).eval()
+            self.tokenizer = AutoTokenizer.from_pretrained(CONFIG["model"])
+            self.ready = True
+            
+            return True, status
+        except Exception as e:
+            return False, f"❌ Initialization failed: {str(e)}"
+    
+    def analyze_text(self, text: str) -> Dict:
+        """Perform emotion analysis on input text"""
+        if not self.ready:
+            raise RuntimeError("Model not initialized")
+        
+        if not text.strip():
+            raise ValueError("Empty input text")
+        
+        # Tokenize
+        encoded = self.tokenizer(
+            text.strip(),
+            truncation=True,
+            padding="max_length",
+            max_length=CONFIG["max_length"],
+            return_tensors="pt"
+        )
+        
+        # Inference
+        with torch.no_grad():
+            logits = self.model(
+                encoded["input_ids"].to(self.device),
+                encoded["attention_mask"].to(self.device)
+            )
+            scores = torch.sigmoid(logits).cpu().numpy()[0]
+            predictions = (scores > np.array(CONFIG["thresholds"])).astype(int)
+        
+        # Update statistics
+        self.session_stats["total_scans"] += 1
+        for idx, emotion in enumerate(CONFIG["emotions"]):
+            if predictions[idx]:
+                self.session_stats["emotion_detections"][emotion] += 1
+        
+        self.session_stats["scan_history"].append({
+            "timestamp": datetime.now().isoformat(),
+            "text_preview": text[:80],
+            "detected_count": int(predictions.sum())
+        })
+        
+        # Build result
+        results = {
+            "emotions": {},
+            "metadata": {
+                "text_length": len(text.split()),
+                "detected_emotions": int(predictions.sum()),
+                "dominant_emotion": None,
+                "confidence": 0.0
+            }
+        }
+        
+        max_score_idx = np.argmax(scores)
+        results["metadata"]["dominant_emotion"] = CONFIG["emotions"][max_score_idx]
+        results["metadata"]["confidence"] = float(scores[max_score_idx])
+        
+        for idx, emotion in enumerate(CONFIG["emotions"]):
+            results["emotions"][emotion] = {
+                "score": float(scores[idx]),
+                "detected": bool(predictions[idx]),
+                "threshold": float(CONFIG["thresholds"][idx]),
+                "intensity": self._get_intensity(scores[idx])
+            }
+        
+        return results
 
-def apply_sigmoid(x):
-    return 1 / (1 + np.exp(-np.clip(x, -500, 500)))
+    def _get_intensity(self, score: float) -> str:
+        """Determine intensity level"""
+        if score >= 0.75: return "Very High"
+        elif score >= 0.55: return "High"
+        elif score >= 0.35: return "Medium"
+        elif score >= 0.20: return "Low"
+        else: return "Very Low"
 
-def calculate_strength(probability):
-    if probability >= 0.80: return "Critical", "#fa5252"
-    elif probability >= 0.60: return "Strong", "#ff8787"
-    elif probability >= 0.40: return "Moderate", "#ffc078"
-    elif probability >= 0.20: return "Weak", "#ffe066"
-    else: return "Minimal", "#d0d0d0"
+# Global engine instance
+engine = EmotiScanEngine()
 
-def get_sentiment_level(sentiment_name, prob):
-    levels = SENTIMENT_CONFIG[sentiment_name]["levels"]
-    if prob >= 0.80: return levels[3]
-    elif prob >= 0.60: return levels[2]
-    elif prob >= 0.40: return levels[1]
-    else: return levels[0]
+def create_radar_chart(emotion_scores: Dict) -> go.Figure:
+    """Generate radar chart for emotion visualization"""
+    emotions = list(emotion_scores.keys())
+    scores = [emotion_scores[e]["score"] * 100 for e in emotions]
+    
+    fig = go.Figure()
+    
+    fig.add_trace(go.Scatterpolar(
+        r=scores,
+        theta=[e.capitalize() for e in emotions],
+        fill='toself',
+        fillcolor='rgba(52, 152, 219, 0.3)',
+        line=dict(color='#3498DB', width=3),
+        marker=dict(size=10, color='#2980B9')
+    ))
+    
+    fig.update_layout(
+        polar=dict(
+            radialaxis=dict(
+                visible=True,
+                range=[0, 100],
+                showticklabels=True,
+                tickfont=dict(size=11, color="#ECF0F1"),
+                gridcolor="#34495E"
+            ),
+            angularaxis=dict(
+                tickfont=dict(size=13, color="#ECF0F1", family="Arial Black")
+            ),
+            bgcolor="#1A1F2E"
+        ),
+        showlegend=False,
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        height=400,
+        margin=dict(l=80, r=80, t=40, b=40)
+    )
+    
+    return fig
 
-def initialize_model():
-    try:
-        app_state.neural_net = SentimentModel()
-        if os.path.exists(WEIGHTS_FILE):
-            app_state.neural_net.load_state_dict(torch.load(WEIGHTS_FILE, map_location=app_state.compute_device))
-            status_msg, emoji, gradient = "Model Loaded Successfully", "✅", "linear-gradient(135deg, #20c997 0%, #12b886 100%)"
-        else:
-            status_msg, emoji, gradient = "Model Initialized (Untrained)", "⚠️", "linear-gradient(135deg, #fab005 0%, #fd7e14 100%)"
-        
-        app_state.neural_net.to(app_state.compute_device).eval()
-        app_state.text_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-        app_state.is_loaded = True
+def render_emotion_card(emotion: str, data: Dict, rank: int) -> str:
+    """Generate individual emotion card HTML"""
+    meta = EMOTION_META[emotion]
+    score_pct = data["score"] * 100
+    is_detected = data["detected"]
+    
+    border_width = "4px" if is_detected else "2px"
+    bg_opacity = "0.15" if is_detected else "0.05"
+    
+    return f"""
+    <div style='
+        background: linear-gradient(145deg, rgba(26, 31, 46, 0.8), rgba(44, 62, 80, 0.6));
+        border: {border_width} solid {meta["color"]};
+        border-radius: 16px;
+        padding: 20px;
+        margin: 12px 0;
+        box-shadow: 0 8px 24px rgba(0, 0, 0, 0.3);
+        position: relative;
+        overflow: hidden;
+    '>
+        <div style='
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            height: 6px;
+            background: linear-gradient(90deg, {meta["gradient"][0]}, {meta["gradient"][1]});
+        '></div>
         
-        status_html = f"""
-        <div style='background:{gradient};padding:25px;border-radius:16px;box-shadow:0 8px 32px rgba(0,0,0,0.2);'>
-            <div style='display:flex;align-items:center;gap:20px;'>
-                <div style='font-size:52px;'>{emoji}</div>
-                <div style='color:white;'>
-                    <h2 style='margin:0;font-size:28px;font-weight:700;'>{status_msg}</h2>
-                    <p style='margin:10px 0 0;font-size:16px;opacity:0.95;'>
-                        Runtime: {app_state.compute_device.upper()} • Accuracy: 87.2% • Ready for Analysis
-                    </p>
+        <div style='display: flex; justify-content: space-between; align-items: center; margin-bottom: 14px;'>
+            <div style='display: flex; align-items: center; gap: 12px;'>
+                <span style='font-size: 40px;'>{meta["emoji"]}</span>
+                <div>
+                    <h3 style='margin: 0; color: #ECF0F1; font-size: 20px; font-weight: 700;'>
+                        {emotion.upper()}
+                    </h3>
+                    <p style='margin: 4px 0 0; color: #95A5A6; font-size: 12px;'>{meta["description"]}</p>
                 </div>
             </div>
-        </div>
-        """
-        return status_html, _render_stats_widget()
-    
-    except Exception as error:
-        error_html = f"""
-        <div style='background:linear-gradient(135deg, #fa5252 0%, #e03131 100%);padding:25px;border-radius:16px;box-shadow:0 8px 32px rgba(0,0,0,0.2);'>
-            <div style='color:white;'>
-                <h2 style='margin:0;font-size:24px;'>❌ Initialization Failed</h2>
-                <p style='margin:10px 0 0;font-size:14px;opacity:0.9;'>{str(error)}</p>
+            <div style='text-align: right;'>
+                <div style='font-size: 32px; font-weight: 900; color: {meta["color"]};'>
+                    {score_pct:.1f}%
+                </div>
+                <div style='
+                    background: {meta["color"]};
+                    color: white;
+                    padding: 4px 10px;
+                    border-radius: 12px;
+                    font-size: 11px;
+                    font-weight: 700;
+                    margin-top: 4px;
+                '>
+                    RANK #{rank}
+                </div>
             </div>
         </div>
-        """
-        return error_html, ""
+        
+        <div style='
+            background: rgba(0, 0, 0, 0.3);
+            border-radius: 10px;
+            height: 14px;
+            overflow: hidden;
+            margin-bottom: 10px;
+        '>
+            <div style='
+                height: 100%;
+                width: {score_pct}%;
+                background: linear-gradient(90deg, {meta["gradient"][0]}, {meta["gradient"][1]});
+                border-radius: 10px;
+                transition: width 0.6s ease;
+            '></div>
+        </div>
+        
+        <div style='display: flex; justify-content: space-between; font-size: 12px; color: #BDC3C7;'>
+            <span>Intensity: <strong style='color: {meta["color"]};'>{data["intensity"]}</strong></span>
+            <span>Status: <strong style='color: {"#2ECC71" if is_detected else "#7F8C8D"};'>
+                {"DETECTED" if is_detected else "Below Threshold"}
+            </strong></span>
+        </div>
+    </div>
+    """
 
-def _render_stats_widget():
+def initialize_system():
+    """Initialize the EmotiScan engine"""
+    success, message = engine.load_model()
+    
+    status_color = "#2ECC71" if success else "#E74C3C"
+    icon = "✅" if success else "❌"
+    
+    status_html = f"""
+    <div style='
+        background: linear-gradient(135deg, {status_color}15, {status_color}25);
+        border: 3px solid {status_color};
+        border-radius: 18px;
+        padding: 28px;
+        text-align: center;
+    '>
+        <div style='font-size: 64px; margin-bottom: 12px;'>{icon}</div>
+        <h2 style='color: #ECF0F1; margin: 0 0 12px 0; font-size: 26px;'>{message}</h2>
+        <p style='color: #BDC3C7; margin: 0; font-size: 14px;'>
+            Device: {engine.device.type.upper()} | Model: {CONFIG["model"]} | Ready: {"Yes" if success else "No"}
+        </p>
+    </div>
+    """
+    
+    stats_html = generate_stats_panel()
+    
+    return status_html, stats_html
+
+def generate_stats_panel() -> str:
+    """Create statistics panel"""
+    stats = engine.session_stats
+    runtime = (datetime.now() - stats["session_start"]).seconds
+    
     return f"""
-    <div style='background:linear-gradient(135deg, #667eea 0%, #764ba2 100%);padding:20px;border-radius:12px;box-shadow:0 4px 20px rgba(102,126,234,0.3);'>
-        <div style='display:grid;grid-template-columns:repeat(3,1fr);gap:20px;color:white;text-align:center;'>
+    <div style='
+        background: linear-gradient(135deg, #16A085, #1ABC9C);
+        border-radius: 16px;
+        padding: 24px;
+        margin-top: 20px;
+    '>
+        <div style='display: grid; grid-template-columns: repeat(3, 1fr); gap: 20px; text-align: center;'>
             <div>
-                <div style='font-size:32px;font-weight:800;color:#ffd43b;'>{app_state.total_analyses}</div>
-                <div style='font-size:13px;opacity:0.9;margin-top:5px;'>Total Analyses</div>
+                <div style='font-size: 36px; font-weight: 900; color: white;'>
+                    {stats["total_scans"]}
+                </div>
+                <div style='color: rgba(255,255,255,0.9); font-size: 13px; margin-top: 6px;'>
+                    Total Analyses
+                </div>
             </div>
             <div>
-                <div style='font-size:32px;font-weight:800;color:#20c997;'>5</div>
-                <div style='font-size:13px;opacity:0.9;margin-top:5px;'>Sentiment Types</div>
+                <div style='font-size: 36px; font-weight: 900; color: white;'>
+                    {len(stats["emotion_detections"])}
+                </div>
+                <div style='color: rgba(255,255,255,0.9); font-size: 13px; margin-top: 6px;'>
+                    Unique Emotions
+                </div>
             </div>
             <div>
-                <div style='font-size:32px;font-weight:800;color:#4dabf7;'>87.2%</div>
-                <div style='font-size:13px;opacity:0.9;margin-top:5px;'>Accuracy</div>
-            </div>
-        </div>
-    </div>
-    """
-
-def _render_sentiment_pulse(sentiment, probability, is_active, threshold, position):
-    config = SENTIMENT_CONFIG[sentiment]
-    pct = probability * 100
-    strength_label, strength_color = calculate_strength(probability)
-    
-    position_badges = {1: "1st", 2: "2nd", 3: "3rd", 4: "4th", 5: "5th"}
-    rank_badge = position_badges.get(position, f"{position}th")
-    
-    border_style = f"3px solid {config['primary']}" if is_active else "2px solid #2c3e50"
-    bg_color = "#2c3e50" if is_active else "#1a1f2e"
-    shadow_style = f"0 8px 24px {config['primary']}40" if is_active else "0 2px 8px rgba(0,0,0,0.2)"
-    
-    pulse_html = f"""
-    <div style='background:{bg_color};padding:22px;margin:10px 0;border-radius:14px;border:{border_style};box-shadow:{shadow_style};transition:all 0.3s ease;'>
-        <div style='display:flex;justify-content:space-between;align-items:center;'>
-            <div style='display:flex;gap:15px;align-items:center;flex:1;'>
-                <div style='font-size:42px;filter:drop-shadow(0 0 12px {config['primary']});'>{config['icon']}</div>
-                <div style='flex:1;'>
-                    <div style='display:flex;gap:10px;align-items:center;margin-bottom:5px;'>
-                        <span style='font-weight:700;font-size:22px;color:#ecf0f1;'>{sentiment.upper()}</span>
-                        <span style='background:{config['primary']};color:#000;padding:3px 10px;border-radius:10px;font-size:11px;font-weight:700;'>{rank_badge}</span>
-                    </div>
-                    <div style='font-size:14px;color:#95a5a6;font-weight:500;'>{get_sentiment_level(sentiment, probability)}</div>
+                <div style='font-size: 36px; font-weight: 900; color: white;'>
+                    {runtime}s
+                </div>
+                <div style='color: rgba(255,255,255,0.9); font-size: 13px; margin-top: 6px;'>
+                    Session Time
                 </div>
-            </div>
-            <div style='text-align:right;margin-left:15px;'>
-                <div style='font-size:28px;font-weight:800;color:{config['primary']};text-shadow:0 0 12px {config['primary']};'>{pct:.1f}%</div>
-                <div style='font-size:12px;color:{strength_color};font-weight:600;margin-top:3px;'>{strength_label}</div>
             </div>
         </div>
-        <div style='position:relative;background:#1a1f2e;height:32px;border-radius:16px;overflow:hidden;margin-top:12px;border:2px solid #34495e;'>
-            <div style='position:absolute;height:100%;background:{config['bg']};width:{min(pct,100)}%;border-radius:14px;box-shadow:inset 0 0 15px rgba(255,255,255,0.3);transition:width 0.5s ease;'></div>
-        </div>
     </div>
     """
-    return pulse_html
 
-def _render_overview_panel(probabilities, predictions, input_text):
-    active_count = sum(predictions)
-    primary_idx = np.argmax(probabilities)
-    dominant_sentiment = SENTIMENTS[primary_idx]
-    active_sentiments = [SENTIMENTS[idx] for idx in range(len(SENTIMENTS)) if predictions[idx] == 1]
-    sentiment_icons = " ".join([SENTIMENT_CONFIG[s]["icon"] for s in active_sentiments]) if active_sentiments else "➖"
-    text_length = len(input_text.split())
-    
-    overview_html = f"""
-    <div style='background:linear-gradient(135deg, #667eea 0%, #764ba2 100%);padding:35px;border-radius:18px;box-shadow:0 10px 40px rgba(102,126,234,0.4);border:3px solid #8b5cf6;'>
-        <div style='text-align:center;color:white;'>
-            <div style='font-size:64px;filter:drop-shadow(0 0 20px rgba(255,255,255,0.4));margin-bottom:15px;'>{sentiment_icons}</div>
-            <h1 style='margin:0;font-size:36px;font-weight:800;text-shadow:0 0 25px rgba(255,255,255,0.3);'>
-                {active_count} Sentiment Pulse{'s' if active_count!=1 else ''} Active
-            </h1>
-            <p style='margin:15px 0 0;font-size:18px;opacity:0.95;'>
-                Primary: {SENTIMENT_CONFIG[dominant_sentiment]['icon']} {dominant_sentiment.upper()} at {probabilities[primary_idx]:.0%} • {text_length} words analyzed
-            </p>
+def scan_emotion(text: str, show_chart: bool):
+    """Main analysis function"""
+    if not engine.ready:
+        error_html = """
+        <div style='padding: 40px; text-align: center; background: linear-gradient(135deg, #E74C3C, #C0392B); border-radius: 18px;'>
+            <div style='font-size: 64px;'>⚠️</div>
+            <h2 style='color: white; margin: 12px 0;'>System Not Ready</h2>
+            <p style='color: rgba(255,255,255,0.9);'>Please initialize the system first</p>
         </div>
-    </div>
-    """
-    return overview_html
-
-def _render_data_matrix(probabilities, predictions):
-    table_rows = ""
-    for idx in range(5):
-        sentiment = SENTIMENTS[idx]
-        config = SENTIMENT_CONFIG[sentiment]
-        status_icon = "●" if predictions[idx] else "○"
-        row_color = config['primary'] if predictions[idx] else "#7f8c8d"
-        
-        table_rows += f"""
-        <tr style='border-bottom:1px solid #34495e;'>
-            <td style='padding:14px;color:{row_color};font-weight:600;'>{config['icon']} {sentiment.upper()}</td>
-            <td style='padding:14px;text-align:center;color:#ecf0f1;font-family:monospace;'>{probabilities[idx]:.4f}</td>
-            <td style='padding:14px;text-align:center;color:#95a5a6;font-family:monospace;'>{OPTIMIZED_THRESHOLDS[idx]:.4f}</td>
-            <td style='padding:14px;text-align:center;color:{row_color};font-size:20px;'>{status_icon}</td>
-        </tr>
         """
+        return error_html, "", None, "{}", generate_stats_panel()
     
-    matrix_html = f"""
-    <div style='background:#2c3e50;padding:25px;border-radius:14px;border:2px solid #34495e;margin-top:20px;'>
-        <h3 style='color:#ecf0f1;margin:0 0 15px 0;font-size:20px;'>📋 Sentiment Data Matrix</h3>
-        <table style='width:100%;border-collapse:collapse;'>
-            <thead>
-                <tr style='background:#1a1f2e;border-bottom:3px solid #667eea;'>
-                    <th style='padding:14px;text-align:left;color:#ecf0f1;font-size:14px;'>Sentiment</th>
-                    <th style='padding:14px;text-align:center;color:#ecf0f1;font-size:14px;'>Score</th>
-                    <th style='padding:14px;text-align:center;color:#ecf0f1;font-size:14px;'>Threshold</th>
-                    <th style='padding:14px;text-align:center;color:#ecf0f1;font-size:14px;'>Status</th>
-                </tr>
-            </thead>
-            <tbody>{table_rows}</tbody>
-        </table>
-    </div>
-    """
-    return matrix_html
-
-def analyze_sentiment(text_input, display_matrix=True):
-    if not app_state.is_loaded:
-        return "<div style='padding:50px;text-align:center;background:linear-gradient(135deg, #fa5252 0%, #e03131 100%);border-radius:18px;border:3px solid #c92a2a;'><div style='font-size:72px;'>⛔</div><h2 style='color:white;margin:15px 0;'>Model Not Loaded</h2><p style='color:#ffe0e0;'>Please initialize the model first</p></div>", "", "", "{}", _render_stats_widget()
-    
-    if not text_input.strip():
-        return "<div style='padding:50px;text-align:center;background:linear-gradient(135deg, #fab005 0%, #fd7e14 100%);border-radius:18px;border:3px solid #f59f00;'><div style='font-size:72px;'>📝</div><h2 style='color:white;margin:15px 0;'>No Input Detected</h2><p style='color:#fff5e0;'>Please enter text to analyze</p></div>", "", "", "{}", _render_stats_widget()
+    if not text.strip():
+        empty_html = """
+        <div style='padding: 40px; text-align: center; background: linear-gradient(135deg, #F39C12, #E67E22); border-radius: 18px;'>
+            <div style='font-size: 64px;'>📝</div>
+            <h2 style='color: white; margin: 12px 0;'>No Input Provided</h2>
+            <p style='color: rgba(255,255,255,0.9);'>Please enter text to analyze</p>
+        </div>
+        """
+        return empty_html, "", None, "{}", generate_stats_panel()
     
     try:
-        tokenized = app_state.text_tokenizer(text_input.strip(), truncation=True, padding="max_length", max_length=SEQUENCE_LENGTH, return_tensors="pt")
+        results = engine.analyze_text(text)
         
-        with torch.no_grad():
-            output_logits = app_state.neural_net(tokenized["input_ids"].to(app_state.compute_device), tokenized["attention_mask"].to(app_state.compute_device))
-            probabilities = apply_sigmoid(output_logits.cpu().numpy())[0]
-            predictions = (probabilities > np.array(OPTIMIZED_THRESHOLDS)).astype(int)
+        # Summary card
+        dominant = results["metadata"]["dominant_emotion"]
+        confidence = results["metadata"]["confidence"]
+        detected_count = results["metadata"]["detected_emotions"]
         
-        app_state.total_analyses += 1
-        for idx, sentiment_name in enumerate(SENTIMENTS):
-            if predictions[idx]: 
-                app_state.sentiment_counts[sentiment_name] += 1
+        summary_html = f"""
+        <div style='
+            background: linear-gradient(135deg, #2C3E50, #34495E);
+            border: 3px solid #16A085;
+            border-radius: 18px;
+            padding: 32px;
+            text-align: center;
+            margin-bottom: 24px;
+        '>
+            <div style='font-size: 72px; margin-bottom: 16px;'>
+                {EMOTION_META[dominant]["emoji"]}
+            </div>
+            <h2 style='color: #ECF0F1; margin: 0 0 12px 0; font-size: 32px; font-weight: 800;'>
+                Dominant Emotion: {dominant.upper()}
+            </h2>
+            <p style='color: #BDC3C7; font-size: 18px; margin: 0 0 20px 0;'>
+                Confidence: {confidence:.1%} | Detected: {detected_count}/{len(CONFIG["emotions"])} emotions
+            </p>
+            <div style='color: #95A5A6; font-size: 14px;'>
+                📊 {results["metadata"]["text_length"]} words analyzed
+            </div>
+        </div>
+        """
         
-        app_state.analysis_log.append({
-            "snippet": text_input[:100], 
-            "time": datetime.now().isoformat(), 
-            "active": sum(predictions)
-        })
+        # Emotion cards
+        sorted_emotions = sorted(
+            results["emotions"].items(),
+            key=lambda x: x[1]["score"],
+            reverse=True
+        )
         
-        ranked_indices = np.argsort(probabilities)[::-1]
-        overview = _render_overview_panel(probabilities, predictions, text_input)
+        cards_html = "<div style='max-width: 900px; margin: 0 auto;'>"
+        for rank, (emotion, data) in enumerate(sorted_emotions, 1):
+            cards_html += render_emotion_card(emotion, data, rank)
+        cards_html += "</div>"
         
-        pulses = "<div style='background:#2c3e50;padding:25px;border-radius:14px;border:2px solid #34495e;'><h3 style='color:#ecf0f1;margin:0 0 15px 0;font-size:20px;'>🎯 Sentiment Pulse Analysis</h3>"
-        for rank, idx in enumerate(ranked_indices):
-            pulses += _render_sentiment_pulse(SENTIMENTS[idx], probabilities[idx], predictions[idx]==1, OPTIMIZED_THRESHOLDS[idx], rank+1)
-        pulses += "</div>"
+        # Chart
+        chart = create_radar_chart(results["emotions"]) if show_chart else None
         
-        matrix = _render_data_matrix(probabilities, predictions) if display_matrix else ""
+        # JSON
+        json_output = json.dumps(results, indent=2)
         
-        json_output = json.dumps({
-            "sentiments": {SENTIMENTS[idx]: {
-                "score": round(float(probabilities[idx]), 4),
-                "active": bool(predictions[idx]),
-                "rank": int(np.where(ranked_indices == idx)[0][0] + 1),
-                "level": get_sentiment_level(SENTIMENTS[idx], probabilities[idx])
-            } for idx in range(5)},
-            "analysis": {
-                "active_count": int(sum(predictions)),
-                "primary": SENTIMENTS[np.argmax(probabilities)],
-                "primary_score": round(float(probabilities[np.argmax(probabilities)]), 4),
-                "word_count": len(text_input.split())
-            }
-        }, indent=2)
+        return summary_html, cards_html, chart, json_output, generate_stats_panel()
         
-        return overview, pulses, matrix, json_output, _render_stats_widget()
-    
-    except Exception as error:
-        return f"<div style='background:linear-gradient(135deg, #fa5252 0%, #e03131 100%);padding:25px;border-radius:14px;border:3px solid #c92a2a;'><h3 style='color:white;'>⚠️ Analysis Error: {str(error)}</h3></div>", "", "", "{}", _render_stats_widget()
+    except Exception as e:
+        error_html = f"""
+        <div style='background: linear-gradient(135deg, #E74C3C, #C0392B); padding: 24px; border-radius: 16px;'>
+            <h3 style='color: white; margin: 0;'>❌ Analysis Error</h3>
+            <p style='color: rgba(255,255,255,0.9); margin: 10px 0 0;'>{str(e)}</p>
+        </div>
+        """
+        return error_html, "", None, "{}", generate_stats_panel()
 
-def batch_analysis(multi_text):
-    if not app_state.is_loaded:
-        return "<div style='padding:25px;background:linear-gradient(135deg, #fa5252 0%, #e03131 100%);border-radius:14px;border:3px solid #c92a2a;color:white;'>❌ Model not initialized</div>"
-    
-    text_lines = [line.strip() for line in multi_text.split('\n') if line.strip()]
-    if not text_lines:
-        return "<div style='padding:25px;background:linear-gradient(135deg, #fab005 0%, #fd7e14 100%);border-radius:14px;border:3px solid #f59f00;color:white;'>⚠️ No input provided</div>"
-    
-    output_html = f"<div style='background:linear-gradient(135deg, #667eea 0%, #764ba2 100%);padding:25px;border-radius:14px;color:white;margin-bottom:20px;box-shadow:0 8px 32px rgba(102,126,234,0.3);'><h2 style='margin:0;font-size:28px;font-weight:800;'>📊 Batch Analysis Report</h2><p style='margin:12px 0 0;opacity:0.95;font-size:16px;'>{len(text_lines)} samples processed</p></div>"
-    
-    for idx, text in enumerate(text_lines, 1):
-        _, _, _, json_data, _ = analyze_sentiment(text, False)
-        data = json.loads(json_data)
-        primary = data['analysis']['primary']
-        primary_score = data['analysis']['primary_score']
-        active = data['analysis']['active_count']
-        
-        preview = text[:80]
-        if len(text) > 80:
-            preview += "..."
-        
-        config = SENTIMENT_CONFIG[primary]
-        
-        output_html += f"""
-        <div style='background:#2c3e50;padding:18px;margin:12px 0;border-radius:12px;border-left:5px solid {config['primary']};box-shadow:0 4px 12px rgba(0,0,0,0.2);'>
-            <div style='display:flex;gap:15px;align-items:start;'>
-                <div style='font-size:32px;'>{config['icon']}</div>
-                <div style='flex:1;'>
-                    <div style='color:{config['primary']};font-weight:700;font-size:16px;margin-bottom:8px;'>Sample #{idx}</div>
-                    <div style='color:#ecf0f1;font-style:italic;margin-bottom:10px;line-height:1.5;'>"{preview}"</div>
-                    <div style='display:flex;gap:12px;flex-wrap:wrap;'>
-                        <span style='background:#1a1f2e;padding:6px 14px;border-radius:8px;font-size:13px;color:#95a5a6;'>
-                            Primary: {primary.upper()} ({primary_score:.0%})
-                        </span>
-                        <span style='background:#1a1f2e;padding:6px 14px;border-radius:8px;font-size:13px;color:#95a5a6;'>
-                            Active: {active}/5
-                        </span>
+def batch_scan(texts: str):
+    """Batch processing function"""
+    if not engine.ready:
+        return "<div style='padding: 24px; background: #E74C3C; border-radius: 14px; color: white;'>❌ System not initialized</div>"
+    
+    lines = [l.strip() for l in texts.split('\n') if l.strip()]
+    if not lines:
+        return "<div style='padding: 24px; background: #F39C12; border-radius: 14px; color: white;'>⚠️ No input provided</div>"
+    
+    output = f"""
+    <div style='
+        background: linear-gradient(135deg, #16A085, #1ABC9C);
+        padding: 28px;
+        border-radius: 16px;
+        margin-bottom: 24px;
+        color: white;
+    '>
+        <h2 style='margin: 0; font-size: 28px; font-weight: 800;'>📦 Batch Analysis Report</h2>
+        <p style='margin: 12px 0 0; font-size: 16px; opacity: 0.95;'>{len(lines)} samples processed</p>
+    </div>
+    """
+    
+    for idx, text in enumerate(lines, 1):
+        try:
+            result = engine.analyze_text(text)
+            dom = result["metadata"]["dominant_emotion"]
+            conf = result["metadata"]["confidence"]
+            meta = EMOTION_META[dom]
+            
+            preview = text[:70] + ("..." if len(text) > 70 else "")
+            
+            output += f"""
+            <div style='
+                background: linear-gradient(145deg, #2C3E50, #34495E);
+                border-left: 6px solid {meta["color"]};
+                border-radius: 12px;
+                padding: 20px;
+                margin: 14px 0;
+            '>
+                <div style='display: flex; gap: 16px; align-items: start;'>
+                    <div style='font-size: 36px;'>{meta["emoji"]}</div>
+                    <div style='flex: 1;'>
+                        <div style='color: {meta["color"]}; font-weight: 700; font-size: 15px; margin-bottom: 8px;'>
+                            Sample #{idx}
+                        </div>
+                        <div style='color: #ECF0F1; font-style: italic; margin-bottom: 12px; line-height: 1.6;'>
+                            "{preview}"
+                        </div>
+                        <div style='display: flex; gap: 10px;'>
+                            <span style='background: #1A1F2E; padding: 6px 12px; border-radius: 8px; font-size: 12px; color: #95A5A6;'>
+                                {dom.upper()} ({conf:.0%})
+                            </span>
+                            <span style='background: #1A1F2E; padding: 6px 12px; border-radius: 8px; font-size: 12px; color: #95A5A6;'>
+                                {result["metadata"]["detected_emotions"]}/5 Active
+                            </span>
+                        </div>
                     </div>
                 </div>
             </div>
-        </div>
-        """
+            """
+        except Exception as e:
+            output += f"<div style='padding: 16px; background: #E74C3C; border-radius: 10px; color: white; margin: 10px 0;'>Error in sample #{idx}: {str(e)}</div>"
     
-    return output_html
+    return output
 
-def show_analysis_history():
-    if not app_state.analysis_log:
-        return "<div style='padding:25px;color:#95a5a6;text-align:center;background:#2c3e50;border-radius:14px;border:2px solid #34495e;'>No analyses performed yet</div>"
+def show_history():
+    """Display scan history"""
+    history = engine.session_stats["scan_history"]
+    
+    if not history:
+        return "<div style='padding: 24px; color: #95A5A6; text-align: center; background: #2C3E50; border-radius: 14px;'>No scans performed yet</div>"
     
-    history_html = f"<div style='background:#2c3e50;padding:25px;border-radius:14px;border:2px solid #34495e;'><h3 style='color:#ecf0f1;margin:0 0 15px 0;font-size:20px;'>📚 Analysis History ({len(app_state.analysis_log)} total)</h3>"
+    output = f"""
+    <div style='background: #2C3E50; padding: 24px; border-radius: 14px;'>
+        <h3 style='color: #ECF0F1; margin: 0 0 16px 0; font-size: 20px;'>
+            📜 Scan History ({len(history)} total)
+        </h3>
+    """
     
-    for idx, record in enumerate(reversed(app_state.analysis_log[-10:]), 1):
-        timestamp = datetime.fromisoformat(record['time']).strftime('%I:%M:%S %p')
-        snippet = record['snippet']
-        if len(record['snippet']) >= 100:
-            snippet += "..."
+    for record in reversed(history[-15:]):
+        timestamp = datetime.fromisoformat(record['timestamp']).strftime('%H:%M:%S')
         
-        history_html += f"""
-        <div style='padding:15px;margin:10px 0;background:#1a1f2e;border-radius:10px;border-left:4px solid #667eea;'>
-            <div style='display:flex;justify-content:space-between;align-items:center;'>
-                <div style='flex:1;'>
-                    <div style='color:#95a5a6;font-size:13px;margin-bottom:5px;'>{timestamp}</div>
-                    <div style='color:#ecf0f1;'>"{snippet}"</div>
-                </div>
-                <span style='background:#667eea;color:white;padding:4px 12px;border-radius:10px;font-size:13px;font-weight:700;margin-left:15px;white-space:nowrap;'>
-                    {record['active']}/5
-                </span>
-            </div>
+        output += f"""
+        <div style='
+            background: #1A1F2E;
+            padding: 16px;
+            margin: 10px 0;
+            border-radius: 10px;
+            border-left: 4px solid #16A085;
+        '>
+            <div style='color: #95A5A6; font-size: 12px; margin-bottom: 6px;'>{timestamp}</div>
+            <div style='color: #ECF0F1; font-size: 14px; margin-bottom: 8px;'>"{record["text_preview"]}"</div>
+            <span style='
+                background: #16A085;
+                color: white;
+                padding: 4px 10px;
+                border-radius: 8px;
+                font-size: 12px;
+                font-weight: 700;
+            '>
+                {record["detected_count"]}/5 Detected
+            </span>
         </div>
         """
     
-    history_html += "</div>"
-    return history_html
+    output += "</div>"
+    return output
 
-def build_interface():
-    app = gr.Blocks(title="🌊 Sentiment Pulse Analyzer")
-    
-    with app:
-        gr.HTML("""
-        <div style='text-align:center;padding:50px 30px;background:linear-gradient(135deg, #667eea 0%, #764ba2 100%);border-radius:20px;margin-bottom:30px;box-shadow:0 10px 50px rgba(102,126,234,0.4);border:3px solid #8b5cf6;'>
-            <div style='font-size:80px;margin-bottom:15px;filter:drop-shadow(0 0 20px rgba(255,255,255,0.3));'>🌊</div>
-            <h1 style='font-size:52px;margin:0;font-weight:900;color:white;text-shadow:0 0 30px rgba(255,255,255,0.3);'>Sentiment Pulse Analyzer</h1>
-            <p style='font-size:22px;margin:20px 0;color:white;opacity:0.95;font-weight:500;'>Advanced Multi-Dimensional Sentiment Detection</p>
-            <p style='font-size:16px;opacity:0.9;color:white;'>RoBERTa Architecture • 87.2% Accuracy • Real-time Analysis</p>
-            <div style='margin-top:25px;display:flex;justify-content:center;gap:12px;flex-wrap:wrap;'>
-                <span style='background:rgba(255,255,255,0.2);padding:10px 18px;border-radius:25px;backdrop-filter:blur(10px);color:white;font-weight:600;'>🔥 Anger</span>
-                <span style='background:rgba(255,255,255,0.2);padding:10px 18px;border-radius:25px;backdrop-filter:blur(10px);color:white;font-weight:600;'>⚡ Fear</span>
-                <span style='background:rgba(255,255,255,0.2);padding:10px 18px;border-radius:25px;backdrop-filter:blur(10px);color:white;font-weight:600;'>✨ Joy</span>
-                <span style='background:rgba(255,255,255,0.2);padding:10px 18px;border-radius:25px;backdrop-filter:blur(10px);color:white;font-weight:600;'>💧 Sadness</span>
-                <span style='background:rgba(255,255,255,0.2);padding:10px 18px;border-radius:25px;backdrop-filter:blur(10px);color:white;font-weight:600;'>💫 Surprise</span>
-            </div>
+# Build interface
+with gr.Blocks(title="🎭 EmotiScan", theme=gr.themes.Base()) as app:
+    gr.HTML("""
+    <div style='
+        text-align: center;
+        padding: 48px 32px;
+        background: linear-gradient(135deg, #16A085 0%, #1ABC9C 50%, #2ECC71 100%);
+        border-radius: 24px;
+        margin-bottom: 32px;
+        box-shadow: 0 12px 48px rgba(22, 160, 133, 0.4);
+    '>
+        <div style='font-size: 96px; margin-bottom: 16px; filter: drop-shadow(0 0 20px rgba(255,255,255,0.4));'>
+            🎭
         </div>
-        """)
+        <h1 style='
+            font-size: 56px;
+            margin: 0;
+            font-weight: 900;
+            color: white;
+            text-shadow: 0 4px 12px rgba(0,0,0,0.3);
+            letter-spacing: -1px;
+        '>
+            EmotiScan
+        </h1>
+        <p style='font-size: 24px; margin: 16px 0; color: white; opacity: 0.95; font-weight: 600;'>
+            Neural Emotion Detection System
+        </p>
+        <p style='font-size: 15px; opacity: 0.9; color: white; font-weight: 500;'>
+            Powered by RoBERTa Transformer • Multi-Label Classification • Real-Time Processing
+        </p>
+        <div style='margin-top: 28px; display: flex; justify-content: center; gap: 12px; flex-wrap: wrap;'>
+            <span style='background: rgba(255,255,255,0.25); padding: 10px 16px; border-radius: 20px; color: white; font-weight: 700; font-size: 14px;'>😠 Anger</span>
+            <span style='background: rgba(255,255,255,0.25); padding: 10px 16px; border-radius: 20px; color: white; font-weight: 700; font-size: 14px;'>😨 Fear</span>
+            <span style='background: rgba(255,255,255,0.25); padding: 10px 16px; border-radius: 20px; color: white; font-weight: 700; font-size: 14px;'>😊 Joy</span>
+            <span style='background: rgba(255,255,255,0.25); padding: 10px 16px; border-radius: 20px; color: white; font-weight: 700; font-size: 14px;'>😢 Sadness</span>
+            <span style='background: rgba(255,255,255,0.25); padding: 10px 16px; border-radius: 20px; color: white; font-weight: 700; font-size: 14px;'>😲 Surprise</span>
+        </div>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            system_status = gr.HTML("<div style='padding: 28px; text-align: center; background: #34495E; border-radius: 18px; color: white;'><h3>🔄 Awaiting Initialization</h3></div>")
+        with gr.Column(scale=1):
+            init_btn = gr.Button("🚀 Initialize System", variant="primary", size="lg", scale=1)
+    
+    stats_display = gr.HTML("")
+    
+    with gr.Tabs():
+        with gr.Tab("🔬 Single Scan"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    input_text = gr.Textbox(
+                        label="📄 Text Input",
+                        placeholder="Enter text for emotion analysis...",
+                        lines=8
+                    )
+                    with gr.Row():
+                        scan_btn = gr.Button("⚡ Scan Emotions", variant="primary", size="lg")
+                        clear_btn = gr.ClearButton([input_text], value="🧹 Clear", size="lg")
+                    show_radar = gr.Checkbox(label="Show Radar Visualization", value=True)
+                    
+                    gr.Examples([
+                        ["This is absolutely amazing! I'm so thrilled and excited!"],
+                        ["I'm furious about this completely unacceptable situation!"],
+                        ["I'm terrified and extremely worried about the future."],
+                        ["Wow! I never expected this to happen at all!"],
+                        ["I'm heartbroken and feel completely devastated."]
+                    ], inputs=[input_text], label="💡 Example Inputs")
+                
+                with gr.Column(scale=1):
+                    gr.Markdown("### 📊 Analysis Results")
+                    summary_output = gr.HTML()
+                    cards_output = gr.HTML()
         
-        with gr.Row():
-            with gr.Column(scale=3):
-                model_status = gr.HTML("<div style='background:linear-gradient(135deg, #fab005 0%, #fd7e14 100%);padding:25px;border-radius:16px;border:3px solid #f59f00;'><h3 style='color:white;margin:0;font-size:24px;'>⏳ Awaiting Initialization</h3><p style='color:rgba(255,255,255,0.9);margin:10px 0 0;'>Click the button to load the model</p></div>")
-            with gr.Column(scale=1):
-                init_button = gr.Button("🚀 Initialize Model", variant="primary", size="lg")
+        with gr.Tab("📈 Visualization"):
+            radar_chart = gr.Plot(label="Emotion Radar Chart")
         
-        stats_panel = gr.HTML("")
+        with gr.Tab("🔄 Batch Processing"):
+            gr.Markdown("### Process Multiple Texts\nEnter one text per line")
+            batch_input = gr.Textbox(
+                label="Batch Input",
+                placeholder="Text 1\nText 2\nText 3...",
+                lines=10
+            )
+            batch_btn = gr.Button("⚡ Process Batch", variant="primary", size="lg")
+            batch_output = gr.HTML()
         
-        with gr.Tabs():
-            with gr.Tab("🔍 Single Analysis"):
-                with gr.Row():
-                    with gr.Column():
-                        text_input = gr.Textbox(label="💬 Input Text for Analysis", placeholder="Enter your text here for sentiment analysis...", lines=10)
-                        with gr.Row():
-                            analyze_btn = gr.Button("⚡ Analyze Sentiment", variant="primary", size="lg")
-                            clear_btn = gr.ClearButton([text_input], value="🗑️ Clear Input", size="lg")
-                        with gr.Row():
-                            show_matrix = gr.Checkbox(label="Display Data Matrix", value=True)
-                        gr.Examples([
-                            ["I'm so excited and thrilled! This is the best day ever!"],
-                            ["I'm extremely frustrated and angry about this terrible situation!"],
-                            ["I'm really scared and worried about what might happen next."],
-                            ["Wow! I can't believe this is actually happening right now!"],
-                            ["I feel completely devastated and heartbroken. Nothing feels right."]
-                        ], inputs=[text_input], label="💡 Sample Inputs")
-                    
-                    with gr.Column():
-                        gr.Markdown("### 📊 Analysis Output")
-                        clear_output_btn = gr.Button("🗑️ Clear Results", variant="secondary", size="sm")
-                        overview_display = gr.HTML()
-                        pulse_display = gr.HTML()
-                        matrix_display = gr.HTML()
-            
-            with gr.Tab("🔄 Batch Processing"):
-                gr.Markdown("### Process Multiple Texts\nEnter one text per line for batch analysis")
-                batch_input = gr.Textbox(label="Multiple Text Inputs", placeholder="I love this product!\nThis service is terrible.\nWhat an incredible surprise!", lines=12)
-                batch_analyze_btn = gr.Button("⚡ Process Batch", variant="primary", size="lg")
-                batch_output = gr.HTML()
-            
-            with gr.Tab("💾 JSON Output"):
-                gr.Markdown("### Structured Data Export")
-                json_display = gr.Code(label="JSON Results", language="json", lines=25)
-            
-            with gr.Tab("📚 History Log"):
-                gr.Markdown("### Analysis History")
-                refresh_history_btn = gr.Button("🔄 Refresh History", variant="secondary")
-                history_display = gr.HTML()
+        with gr.Tab("💾 JSON Export"):
+            gr.Markdown("### Structured Data Output")
+            json_output = gr.Code(label="JSON Results", language="json", lines=20)
         
-        gr.HTML("""
-        <div style='margin-top:35px;padding:25px;background:#2c3e50;border-radius:14px;border:2px solid #34495e;'>
-            <h2 style='color:#ecf0f1;margin-top:0;font-size:24px;'>📖 Technical Specifications</h2>
-            <p style='color:#ecf0f1;line-height:1.8;margin:10px 0;'>
-                <strong>Model Architecture:</strong> RoBERTa-base transformer with 125M parameters, fine-tuned for multi-label sentiment classification
+        with gr.Tab("📜 History"):
+            gr.Markdown("### Scan History Log")
+            refresh_btn = gr.Button("🔄 Refresh", variant="secondary")
+            history_output = gr.HTML()
+    
+    gr.HTML("""
+    <div style='
+        margin-top: 32px;
+        padding: 28px;
+        background: linear-gradient(145deg, #2C3E50, #34495E);
+        border-radius: 16px;
+        border: 2px solid #16A085;
+    '>
+        <h2 style='color: #ECF0F1; margin-top: 0; font-size: 26px; font-weight: 700;'>
+            🔬 Technical Specifications
+        </h2>
+        <div style='color: #BDC3C7; line-height: 1.9;'>
+            <p style='margin: 12px 0;'>
+                <strong style='color: #16A085;'>Architecture:</strong> RoBERTa-base transformer with 125M parameters, 
+                fine-tuned for multi-label emotion classification
+            </p>
+            <p style='margin: 12px 0;'>
+                <strong style='color: #16A085;'>Emotion Categories:</strong> Anger, Fear, Joy, Sadness, Surprise
             </p>
-            <p style='color:#ecf0f1;line-height:1.8;margin:10px 0;'>
-                <strong>Sentiment Categories:</strong> Anger, Fear, Joy, Sadness, Surprise
+            <p style='margin: 12px 0;'>
+                <strong style='color: #16A085;'>Performance Metrics:</strong> 87.2% F1 Score on validation dataset
             </p>
-            <p style='color:#ecf0f1;line-height:1.8;margin:10px 0;'>
-                <strong>Model Performance:</strong> 87.2% F1 Score on validation dataset
+            <p style='margin: 12px 0;'>
+                <strong style='color: #16A085;'>Processing Speed:</strong> Real-time inference with optimized threshold detection
             </p>
-            <p style='color:#ecf0f1;line-height:1.8;margin:10px 0;'>
-                <strong>Processing:</strong> Real-time analysis with optimized threshold detection
+            <p style='margin: 12px 0;'>
+                <strong style='color: #16A085;'>Max Sequence Length:</strong> 200 tokens with truncation support
             </p>
         </div>
-        """)
-        
-        # Event handlers
-        init_button.click(initialize_model, outputs=[model_status, stats_panel])
-        analyze_btn.click(analyze_sentiment, inputs=[text_input, show_matrix], outputs=[overview_display, pulse_display, matrix_display, json_display, stats_panel])
-        text_input.submit(analyze_sentiment, inputs=[text_input, show_matrix], outputs=[overview_display, pulse_display, matrix_display, json_display, stats_panel])
-        clear_output_btn.click(lambda: ("", "", "", "{}", _render_stats_widget()), outputs=[overview_display, pulse_display, matrix_display, json_display, stats_panel])
-        batch_analyze_btn.click(batch_analysis, inputs=[batch_input], outputs=[batch_output])
-        refresh_history_btn.click(show_analysis_history, outputs=[history_display])
-    
-    return app
+    </div>
+    """)
+    
+    # Event Handlers
+    init_btn.click(
+        initialize_system,
+        outputs=[system_status, stats_display]
+    )
+    
+    scan_btn.click(
+        scan_emotion,
+        inputs=[input_text, show_radar],
+        outputs=[summary_output, cards_output, radar_chart, json_output, stats_display]
+    )
+    
+    input_text.submit(
+        scan_emotion,
+        inputs=[input_text, show_radar],
+        outputs=[summary_output, cards_output, radar_chart, json_output, stats_display]
+    )
+    
+    batch_btn.click(
+        batch_scan,
+        inputs=[batch_input],
+        outputs=[batch_output]
+    )
+    
+    refresh_btn.click(
+        show_history,
+        outputs=[history_output]
+    )
 
 if __name__ == "__main__":
-    print("🌊 Launching Sentiment Pulse Analyzer interface...")
-    interface = build_interface()
-    interface.launch(server_name="0.0.0.0", server_port=7860, share=False, show_error=True)
+    print("🎭 Launching EmotiScan interface...")
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )