update app.py

app.py

@@ -9,348 +9,479 @@ import json
 from datetime import datetime
 from collections import defaultdict
 
-print("🎭 Emotion Classifier Starting...")
+print("🌊 Sentiment Analyzer Starting...")
 
 MODEL_NAME = "roberta-base"
-EMOTIONS = ["anger", "fear", "joy", "sadness", "surprise"]
-BEST_THRESHOLDS = [0.24722222, 0.61666667, 0.59722222, 0.44166667, 0.46111111]
-MAX_LEN = 200
-MODEL_PATH = "roberta.pth"
+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"
 
-EMOTION_META = {
-    "anger": {"emoji": "😠", "color": "#ef4444", "gradient": "linear-gradient(135deg, #dc2626 0%, #991b1b 100%)", "glow": "0 0 20px rgba(239, 68, 68, 0.5)", "description": "Frustration, irritation, or rage", "intensity_labels": ["Mild irritation", "Moderate anger", "Strong anger", "Intense rage"]},
-    "fear": {"emoji": "😨", "color": "#a78bfa", "gradient": "linear-gradient(135deg, #8b5cf6 0%, #6d28d9 100%)", "glow": "0 0 20px rgba(139, 92, 246, 0.5)", "description": "Anxiety, worry, or terror", "intensity_labels": ["Slight concern", "Moderate fear", "Strong fear", "Extreme terror"]},
-    "joy": {"emoji": "😊", "color": "#fbbf24", "gradient": "linear-gradient(135deg, #f59e0b 0%, #d97706 100%)", "glow": "0 0 20px rgba(251, 191, 36, 0.5)", "description": "Happiness, excitement, or delight", "intensity_labels": ["Mild pleasure", "Moderate happiness", "Strong joy", "Intense euphoria"]},
-    "sadness": {"emoji": "😢", "color": "#60a5fa", "gradient": "linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%)", "glow": "0 0 20px rgba(59, 130, 246, 0.5)", "description": "Sorrow, grief, or disappointment", "intensity_labels": ["Mild sadness", "Moderate sorrow", "Strong grief", "Deep despair"]},
-    "surprise": {"emoji": "😲", "color": "#f472b6", "gradient": "linear-gradient(135deg, #ec4899 0%, #be185d 100%)", "glow": "0 0 20px rgba(236, 72, 153, 0.5)", "description": "Astonishment, shock, or amazement", "intensity_labels": ["Mild surprise", "Moderate shock", "Strong astonishment", "Complete disbelief"]}
+SENTIMENT_CONFIG = {
+    "anger": {
+        "icon": "🔥", 
+        "primary": "#ff6b6b", 
+        "secondary": "#ee5a6f",
+        "bg": "linear-gradient(120deg, #ff6b6b 0%, #c92a2a 100%)",
+        "label": "Anger Detected",
+        "levels": ["Annoyed", "Frustrated", "Angry", "Enraged"]
+    },
+    "fear": {
+        "icon": "⚡", 
+        "primary": "#845ef7", 
+        "secondary": "#7048e8",
+        "bg": "linear-gradient(120deg, #845ef7 0%, #5f3dc4 100%)",
+        "label": "Fear Detected",
+        "levels": ["Worried", "Anxious", "Fearful", "Terrified"]
+    },
+    "joy": {
+        "icon": "✨", 
+        "primary": "#ffd43b", 
+        "secondary": "#fab005",
+        "bg": "linear-gradient(120deg, #ffd43b 0%, #f59f00 100%)",
+        "label": "Joy Detected",
+        "levels": ["Content", "Happy", "Joyful", "Ecstatic"]
+    },
+    "sadness": {
+        "icon": "💧", 
+        "primary": "#4dabf7", 
+        "secondary": "#339af0",
+        "bg": "linear-gradient(120deg, #4dabf7 0%, #1971c2 100%)",
+        "label": "Sadness Detected",
+        "levels": ["Melancholic", "Sad", "Sorrowful", "Devastated"]
+    },
+    "surprise": {
+        "icon": "💫", 
+        "primary": "#ff6bc2", 
+        "secondary": "#e64980",
+        "bg": "linear-gradient(120deg, #ff6bc2 0%, #c2255c 100%)",
+        "label": "Surprise Detected",
+        "levels": ["Curious", "Surprised", "Shocked", "Stunned"]
+    }
 }
 
-class RobertaEmotion(nn.Module):
+class SentimentModel(nn.Module):
     def __init__(self):
         super().__init__()
-        self.backbone = AutoModel.from_pretrained(MODEL_NAME)
-        self.dropout = nn.Dropout(0.35)
-        self.head = nn.Linear(768, 5)
+        self.encoder = AutoModel.from_pretrained(MODEL_NAME)
+        self.dropout_layer = nn.Dropout(0.35)
+        self.classifier = nn.Linear(768, 5)
     
-    def forward(self, input_ids, attention_mask):
-        outputs = self.backbone(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.head(self.dropout(pooled))
+    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))
 
-class ModelState:
+class AnalyzerState:
     def __init__(self):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.model = None
-        self.tokenizer = None
-        self.ready = False
-        self.predictions_count = 0
-        self.history = []
-        self.emotion_stats = defaultdict(int)
+        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)
 
-state = ModelState()
+app_state = AnalyzerState()
 
-def sigmoid(x):
+def apply_sigmoid(x):
     return 1 / (1 + np.exp(-np.clip(x, -500, 500)))
 
-def get_intensity_level(prob):
-    if prob >= 0.85: return "Very High", "#10b981"
-    elif prob >= 0.70: return "High", "#60a5fa"
-    elif prob >= 0.50: return "Moderate", "#fbbf24"
-    elif prob >= 0.30: return "Low", "#9ca3af"
-    else: return "Very Low", "#6b7280"
+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"
 
-def get_emotion_intensity_label(emotion, prob):
-    labels = EMOTION_META[emotion]["intensity_labels"]
-    if prob >= 0.85: return labels[3]
-    elif prob >= 0.65: return labels[2]
-    elif prob >= 0.45: return labels[1]
-    else: return labels[0]
+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 load_model():
+def initialize_model():
     try:
-        state.model = RobertaEmotion()
-        if os.path.exists(MODEL_PATH):
-            state.model.load_state_dict(torch.load(MODEL_PATH, map_location=state.device))
-            status, icon, bg, border = "success", "✅", "#065f46", "#10b981"
+        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, icon, bg, border = "warning", "⚠️", "#78350f", "#f59e0b"
-        state.model.to(state.device).eval()
-        state.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-        state.ready = True
-        html = f"<div style='background:{bg};padding:20px;border-radius:12px;border:2px solid {border};box-shadow:{border} 0 0 30px;'><div style='display:flex;gap:15px;align-items:center;'><div style='font-size:48px;'>{icon}</div><div style='color:#f9fafb;'><h3 style='margin:0;'>Model Ready</h3><p style='margin:8px 0 0;color:#d1d5db;'>Device: {state.device.upper()} | F1: 0.872</p></div></div></div>"
-        return html, _create_controls_panel()
-    except Exception as e:
-        return f"<div style='background:#7f1d1d;padding:20px;border-radius:12px;border:2px solid #ef4444;'><h3 style='color:#fecaca;'>Error: {e}</h3></div>", ""
+            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
+        
+        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>
+            </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>
+        </div>
+        """
+        return error_html, ""
 
-def _create_controls_panel():
-    return f"<div style='background:linear-gradient(135deg,#4c1d95 0%,#5b21b6 100%);padding:15px;border-radius:10px;color:white;box-shadow:0 0 30px rgba(139,92,246,0.4);'><div style='display:flex;justify-content:space-around;flex-wrap:wrap;gap:15px;'><div style='text-align:center;'><div style='font-size:28px;font-weight:bold;color:#fbbf24;'>{state.predictions_count}</div><div style='font-size:12px;'>Predictions</div></div><div style='text-align:center;'><div style='font-size:28px;font-weight:bold;color:#10b981;'>5</div><div style='font-size:12px;'>Classes</div></div><div style='text-align:center;'><div style='font-size:28px;font-weight:bold;color:#60a5fa;'>0.872</div><div style='font-size:12px;'>F1 Score</div></div></div></div>"
+def _render_stats_widget():
+    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>
+                <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>
+            <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>
+            <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 _create_emotion_card(emotion, prob, detected, threshold, rank):
-    meta = EMOTION_META[emotion]
-    prob_pct = prob * 100
-    intensity, intensity_color = get_intensity_level(prob)
-    medals = {1: "🥇", 2: "🥈", 3: "🥉"}
-    rank_display = medals.get(rank, f"#{rank}")
-    border = f"2px solid {meta['color']}" if detected else "2px solid #374151"
-    bg = "#1f2937" if detected else "#111827"
-    shadow = f"{meta['glow']}, 0 6px 12px rgba(0,0,0,0.4)" if detected else "0 2px 4px rgba(0,0,0,0.3)"
+def _render_sentiment_pulse(sentiment, probability, is_active, threshold, position):
+    config = SENTIMENT_CONFIG[sentiment]
+    pct = probability * 100
+    strength_label, strength_color = calculate_strength(probability)
     
-    card_html = f"""<div style='background:{bg};padding:18px;margin:12px 0;border-radius:12px;border:{border};box-shadow:{shadow};'>
-    <div style='display:flex;justify-content:space-between;align-items:center;'>
-        <div style='display:flex;gap:12px;align-items:center;'>
-            <span style='font-size:36px;filter:drop-shadow(0 0 10px {meta['color']});'>{meta['emoji']}</span>
-            <div>
-                <div style='display:flex;gap:8px;align-items:center;'>
-                    <span style='font-weight:bold;font-size:20px;color:#f9fafb;'>{emotion.capitalize()}</span>
-                    <span style='background:{meta['color']};color:#000;padding:2px 8px;border-radius:12px;font-size:12px;font-weight:bold;'>{rank_display}</span>
+    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>
-                <div style='font-size:13px;color:#9ca3af;'>{get_emotion_intensity_label(emotion, prob)}</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='text-align:right;'>
-            <div style='font-size:24px;font-weight:bold;color:{meta['color']};text-shadow:0 0 10px {meta['color']};'>{prob_pct:.1f}%</div>
-            <div style='font-size:11px;color:{intensity_color};'>{intensity}</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>
-    <div style='position:relative;background:#0f172a;height:28px;border-radius:14px;overflow:hidden;margin:10px 0;border:1px solid #1e293b;'>
-        <div style='position:absolute;height:100%;background:{meta['gradient']};width:{min(prob_pct,100)}%;border-radius:14px;box-shadow:inset 0 0 20px rgba(255,255,255,0.2);'></div>
-    </div>
-</div>"""
-    return card_html
+    """
+    return pulse_html
 
-def _create_summary_header(probs, preds, text):
-    detected_count = sum(preds)
-    max_idx = np.argmax(probs)
-    dominant = EMOTIONS[max_idx]
-    detected_emotions = [EMOTIONS[i] for i in range(len(EMOTIONS)) if preds[i] == 1]
-    emojis = " ".join([EMOTION_META[e]["emoji"] for e in detected_emotions]) if detected_emotions else "😐"
-    word_count = len(text.split())
+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())
     
-    summary_html = f"""<div style='background:linear-gradient(135deg,#4c1d95 0%,#5b21b6 100%);padding:30px;border-radius:16px;color:white;box-shadow:0 0 40px rgba(139,92,246,0.5);border:2px solid #7c3aed;'>
-    <div style='text-align:center;'>
-        <div style='font-size:56px;filter:drop-shadow(0 0 15px rgba(255,255,255,0.3));'>{emojis}</div>
-        <h2 style='margin:10px 0;font-size:32px;text-shadow:0 0 20px rgba(255,255,255,0.3);'>{detected_count} Emotion{'s' if detected_count!=1 else ''} Detected</h2>
-        <p style='opacity:0.95;'>Dominant: {EMOTION_META[dominant]['emoji']} {dominant.capitalize()} ({probs[max_idx]:.0%}) | Words: {word_count}</p>
+    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>
+        </div>
     </div>
-</div>"""
-    return summary_html
+    """
+    return overview_html
 
-def _create_comparison_table(probs, preds):
-    rows = ""
-    for i in range(5):
-        emotion = EMOTIONS[i]
-        meta = EMOTION_META[emotion]
-        detected_mark = "✓" if preds[i] else "○"
-        color = meta['color'] if preds[i] else "#6b7280"
-        rows += f"""<tr style='border-bottom:1px solid #374151;'>
-            <td style='padding:12px;color:{color};'>{meta['emoji']} {emotion.capitalize()}</td>
-            <td style='padding:12px;text-align:center;color:#d1d5db;'>{probs[i]:.3f}</td>
-            <td style='padding:12px;text-align:center;color:#9ca3af;'>{BEST_THRESHOLDS[i]:.3f}</td>
-            <td style='padding:12px;text-align:center;color:{color};font-size:18px;'>{detected_mark}</td>
-        </tr>"""
+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>
+        """
     
-    table_html = f"""<div style='background:#1f2937;padding:20px;border-radius:12px;border:2px solid #374151;margin-top:20px;'>
-    <h3 style='color:#f9fafb;margin-top:0;'>📊 Comparison Table</h3>
-    <table style='width:100%;border-collapse:collapse;'>
-        <thead>
-            <tr style='background:#111827;border-bottom:2px solid #4b5563;'>
-                <th style='padding:12px;text-align:left;color:#d1d5db;'>Emotion</th>
-                <th style='padding:12px;text-align:center;color:#d1d5db;'>Probability</th>
-                <th style='padding:12px;text-align:center;color:#d1d5db;'>Threshold</th>
-                <th style='padding:12px;text-align:center;color:#d1d5db;'>Detected</th>
-            </tr>
-        </thead>
-        <tbody>{rows}</tbody>
-    </table>
-</div>"""
-    return table_html
+    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 predict_emotion(text, show_table=True):
-    if not state.ready:
-        return "<div style='padding:40px;text-align:center;background:#7f1d1d;border:2px solid #ef4444;border-radius:16px;'><div style='font-size:64px;'>⚠️</div><h2 style='color:#fecaca;'>Model Not Loaded</h2><p style='color:#fca5a5;'>Click Load Model button</p></div>", "", "", "{}", _create_controls_panel()
+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.strip():
-        return "<div style='padding:40px;text-align:center;background:#78350f;border:2px solid #f59e0b;border-radius:16px;'><div style='font-size:64px;'>✍️</div><h2 style='color:#fef3c7;'>No Text Provided</h2><p style='color:#fde68a;'>Enter text above</p></div>", "", "", "{}", _create_controls_panel()
+    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()
     
     try:
-        encoding = state.tokenizer(text.strip(), truncation=True, padding="max_length", max_length=MAX_LEN, return_tensors="pt")
+        tokenized = app_state.text_tokenizer(text_input.strip(), truncation=True, padding="max_length", max_length=SEQUENCE_LENGTH, return_tensors="pt")
+        
         with torch.no_grad():
-            logits = state.model(encoding["input_ids"].to(state.device), encoding["attention_mask"].to(state.device))
-            probs = sigmoid(logits.cpu().numpy())[0]
-            preds = (probs > np.array(BEST_THRESHOLDS)).astype(int)
+            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)
         
-        state.predictions_count += 1
-        for i, emo in enumerate(EMOTIONS):
-            if preds[i]: state.emotion_stats[emo] += 1
+        app_state.total_analyses += 1
+        for idx, sentiment_name in enumerate(SENTIMENTS):
+            if predictions[idx]: 
+                app_state.sentiment_counts[sentiment_name] += 1
         
-        state.history.append({"text": text[:100], "timestamp": datetime.now().isoformat(), "detected": sum(preds)})
+        app_state.analysis_log.append({
+            "snippet": text_input[:100], 
+            "time": datetime.now().isoformat(), 
+            "active": sum(predictions)
+        })
         
-        ranked = np.argsort(probs)[::-1]
-        summary = _create_summary_header(probs, preds, text)
+        ranked_indices = np.argsort(probabilities)[::-1]
+        overview = _render_overview_panel(probabilities, predictions, text_input)
         
-        cards = "<div style='background:#111827;padding:20px;border-radius:12px;border:2px solid #374151;'><h3 style='color:#f9fafb;margin-top:0;'>🎯 Detailed Analysis</h3>"
-        for r, i in enumerate(ranked):
-            cards += _create_emotion_card(EMOTIONS[i], probs[i], preds[i]==1, BEST_THRESHOLDS[i], r+1)
-        cards += "</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>"
         
-        table = _create_comparison_table(probs, preds) if show_table else ""
+        matrix = _render_data_matrix(probabilities, predictions) if display_matrix else ""
         
-        json_out = json.dumps({
-            "emotions": {EMOTIONS[i]: {
-                "probability": round(float(probs[i]), 4),
-                "detected": bool(preds[i]),
-                "rank": int(np.where(ranked == i)[0][0] + 1),
-                "intensity": get_emotion_intensity_label(EMOTIONS[i], probs[i])
-            } for i in range(5)},
-            "summary": {
-                "detected": int(sum(preds)),
-                "dominant": EMOTIONS[np.argmax(probs)],
-                "dominant_prob": round(float(probs[np.argmax(probs)]), 4),
-                "word_count": len(text.split())
+        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, cards, table, json_out, _create_controls_panel()
+        return overview, pulses, matrix, json_output, _render_stats_widget()
     
-    except Exception as e:
-        return f"<div style='background:#7f1d1d;padding:20px;border-radius:12px;border:2px solid #ef4444;'><h3 style='color:#fecaca;'>Error: {str(e)}</h3></div>", "", "", "{}", _create_controls_panel()
+    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()
 
-def batch_predict(texts):
-    if not state.ready:
-        return "<div style='padding:20px;background:#7f1d1d;border-radius:12px;border:2px solid #ef4444;color:#fecaca;'>⚠️ Model not loaded</div>"
+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>"
     
-    lines = [l.strip() for l in texts.split('\n') if l.strip()]
-    if not lines:
-        return "<div style='padding:20px;background:#78350f;border-radius:12px;border:2px solid #f59e0b;color:#fef3c7;'>⚠️ No text provided</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>"
     
-    html = f"<div style='background:linear-gradient(135deg,#4c1d95 0%,#5b21b6 100%);padding:20px;border-radius:12px;color:white;margin-bottom:20px;box-shadow:0 0 30px rgba(139,92,246,0.4);'><h2 style='margin:0;'>📊 Batch Results</h2><p style='margin:8px 0 0;opacity:0.9;'>Analyzed {len(lines)} samples</p></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 i, txt in enumerate(lines, 1):
-        s, _, _, j, _ = predict_emotion(txt, False, False)
-        data = json.loads(j)
-        dom = data['summary']['dominant']
-        dom_prob = data['summary']['dominant_prob']
-        det_count = data['summary']['detected']
+    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']
         
-        txt_preview = txt[:100]
-        if len(txt) > 100:
-            txt_preview += "..."
+        preview = text[:80]
+        if len(text) > 80:
+            preview += "..."
         
-        color = EMOTION_META[dom]['color']
-        emoji = EMOTION_META[dom]['emoji']
+        config = SENTIMENT_CONFIG[primary]
         
-        html += f"""<div style='background:#1f2937;padding:15px;margin:10px 0;border-radius:10px;border-left:4px solid {color};'>
-            <div style='display:flex;justify-content:space-between;align-items:start;gap:10px;'>
-                <div style='font-size:24px;'>{emoji}</div>
+        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;'>
-                    <strong style='color:{color};'>Sample #{i}</strong>
-                    <div style='color:#d1d5db;font-style:italic;margin:5px 0;'>"{txt_preview}"</div>
-                    <div style='display:flex;gap:10px;flex-wrap:wrap;margin-top:8px;'>
-                        <span style='background:#111827;padding:4px 10px;border-radius:6px;font-size:13px;color:#9ca3af;'>
-                            Dominant: {dom.capitalize()} ({dom_prob:.0%})
+                    <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:#111827;padding:4px 10px;border-radius:6px;font-size:13px;color:#9ca3af;'>
-                            Detected: {det_count}/5
+                        <span style='background:#1a1f2e;padding:6px 14px;border-radius:8px;font-size:13px;color:#95a5a6;'>
+                            Active: {active}/5
                         </span>
                     </div>
                 </div>
             </div>
-        </div>"""
+        </div>
+        """
     
-    return html
+    return output_html
 
-def get_history():
-    if not state.history:
-        return "<div style='padding:20px;color:#9ca3af;text-align:center;background:#1f2937;border-radius:12px;border:2px solid #374151;'>No predictions yet</div>"
+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>"
     
-    html = f"<div style='background:#1f2937;padding:20px;border-radius:12px;border:2px solid #374151;'><h3 style='color:#f9fafb;margin-top:0;'>📜 Recent Predictions ({len(state.history)})</h3>"
+    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>"
     
-    for i, rec in enumerate(reversed(state.history[-10:]), 1):
-        time = datetime.fromisoformat(rec['timestamp']).strftime('%H:%M:%S')
-        txt_preview = rec['text']
-        if len(rec['text']) >= 100:
-            txt_preview += "..."
+    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 += "..."
         
-        html += f"""<div style='padding:12px;margin:8px 0;background:#111827;border-radius:8px;border-left:3px solid #8b5cf6;'>
+        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='color:#d1d5db;flex:1;'>
-                    <span style='color:#9ca3af;font-size:12px;'>{time}</span> - "{txt_preview}"
+                <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:#7c3aed;color:white;padding:2px 8px;border-radius:12px;font-size:12px;font-weight:bold;margin-left:10px;'>
-                    {rec['detected']}/5
+                <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>
-        </div>"""
+        </div>
+        """
     
-    html += "</div>"
-    return html
+    history_html += "</div>"
+    return history_html
 
-def create_interface():
-    demo = gr.Blocks(title="🎭 Emotion Classifier")
+def build_interface():
+    app = gr.Blocks(title="🌊 Sentiment Pulse Analyzer")
     
-    with demo:
-        gr.HTML("<div style='text-align:center;padding:40px 20px;background:linear-gradient(135deg,#4c1d95 0%,#5b21b6 100%);border-radius:20px;margin-bottom:25px;color:white;box-shadow:0 0 40px rgba(139,92,246,0.5);'><div style='font-size:72px;margin-bottom:10px;'>🎭</div><h1 style='font-size:48px;margin:0;font-weight:800;'>Emotion Classifier</h1><p style='font-size:20px;margin:15px 0;opacity:0.95;'>Advanced Multi-Label Emotion Detection</p><p style='font-size:15px;opacity:0.85;'>RoBERTa-base • F1: 0.872 • 5 Classes</p><div style='margin-top:20px;display:flex;justify-content:center;gap:15px;flex-wrap:wrap;'><span style='background:rgba(0,0,0,0.3);padding:8px 16px;border-radius:20px;backdrop-filter:blur(10px);'>😠 Anger</span><span style='background:rgba(0,0,0,0.3);padding:8px 16px;border-radius:20px;backdrop-filter:blur(10px);'>😨 Fear</span><span style='background:rgba(0,0,0,0.3);padding:8px 16px;border-radius:20px;backdrop-filter:blur(10px);'>😊 Joy</span><span style='background:rgba(0,0,0,0.3);padding:8px 16px;border-radius:20px;backdrop-filter:blur(10px);'>😢 Sadness</span><span style='background:rgba(0,0,0,0.3);padding:8px 16px;border-radius:20px;backdrop-filter:blur(10px);'>😲 Surprise</span></div></div>")
+    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>
+        </div>
+        """)
         
         with gr.Row():
             with gr.Column(scale=3):
-                status = gr.HTML("<div style='background:#78350f;padding:20px;border-radius:12px;border:2px solid #f59e0b;'><h3 style='color:#fef3c7;margin:0;'>⏳ Model Not Loaded</h3><p style='color:#fde68a;margin:8px 0 0;'>Click Load Model to start</p></div>")
+                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):
-                load_btn = gr.Button("🚀 Load Model", variant="primary", size="lg")
+                init_button = gr.Button("🚀 Initialize Model", variant="primary", size="lg")
         
-        controls = gr.HTML("")
+        stats_panel = gr.HTML("")
         
         with gr.Tabs():
-            with gr.Tab("📝 Single Analysis"):
+            with gr.Tab("🔍 Single Analysis"):
                 with gr.Row():
                     with gr.Column():
-                        inp = gr.Textbox(label="💬 Enter Text to Analyze", placeholder="Type your text here...", lines=10)
+                        text_input = gr.Textbox(label="💬 Input Text for Analysis", placeholder="Enter your text here for sentiment analysis...", lines=10)
                         with gr.Row():
-                            analyze = gr.Button("🔍 Analyze Emotions", variant="primary", size="lg")
-                            clear = gr.ClearButton([inp], value="🗑️ Clear", size="lg")
+                            analyze_btn = gr.Button("⚡ Analyze Sentiment", variant="primary", size="lg")
+                            clear_btn = gr.ClearButton([text_input], value="🗑️ Clear Input", size="lg")
                         with gr.Row():
-                            table = gr.Checkbox(label="Show Table", value=True)
+                            show_matrix = gr.Checkbox(label="Display Data Matrix", value=True)
                         gr.Examples([
-                            ["I'm absolutely thrilled and overjoyed! This is amazing!"],
-                            ["This is unacceptable! I'm furious about this situation!"],
-                            ["I'm terrified about the future. So much uncertainty."],
-                            ["Oh my god! I cannot believe this just happened!"],
-                            ["I feel so empty and sad. Nothing matters anymore."]
-                        ], inputs=[inp], label="💡 Try These 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 Results")
-                        clear_res = gr.Button("🗑️ Clear Results", variant="secondary", size="sm")
-                        summ = gr.HTML()
-                        details = gr.HTML()
-                        tbl = gr.HTML()
+                        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 Analysis"):
-                gr.Markdown("### Analyze Multiple Texts\nEnter one text per line")
-                batch_inp = gr.Textbox(label="Multiple Texts", placeholder="I love this!\nThis is terrible.\nWhat a surprise!", lines=12)
-                batch_btn = gr.Button("🔍 Analyze All", variant="primary", size="lg")
-                batch_out = 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 Export"):
-                gr.Markdown("### Developer-Friendly JSON Output")
-                json_out = gr.Code(label="JSON Results", language="json", lines=25)
+            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"):
-                gr.Markdown("### Recent Predictions")
-                hist_btn = gr.Button("🔄 Refresh History", variant="secondary")
-                hist_out = gr.HTML()
+            with gr.Tab("📚 History Log"):
+                gr.Markdown("### Analysis History")
+                refresh_history_btn = gr.Button("🔄 Refresh History", variant="secondary")
+                history_display = gr.HTML()
         
-        gr.HTML("<div style='margin-top:30px;padding:20px;background:#1f2937;border-radius:12px;border:2px solid #374151;'><h2 style='color:#f9fafb;margin-top:0;'>📚 Model Documentation</h2><p style='color:#d1d5db;line-height:1.8;'><strong>Architecture:</strong> RoBERTa-base (125M parameters) fine-tuned for multi-label emotion classification</p><p style='color:#d1d5db;line-height:1.8;'><strong>Classes:</strong> Anger, Fear, Joy, Sadness, Surprise</p><p style='color:#d1d5db;line-height:1.8;'><strong>Performance:</strong> F1 Score 0.872 on validation set</p></div>")
+        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
+            </p>
+            <p style='color:#ecf0f1;line-height:1.8;margin:10px 0;'>
+                <strong>Sentiment Categories:</strong> Anger, Fear, Joy, Sadness, Surprise
+            </p>
+            <p style='color:#ecf0f1;line-height:1.8;margin:10px 0;'>
+                <strong>Model Performance:</strong> 87.2% F1 Score on validation dataset
+            </p>
+            <p style='color:#ecf0f1;line-height:1.8;margin:10px 0;'>
+                <strong>Processing:</strong> Real-time analysis with optimized threshold detection
+            </p>
+        </div>
+        """)
         
-        load_btn.click(load_model, outputs=[status, controls])
-        analyze.click(predict_emotion, inputs=[inp, table], outputs=[summ, details, tbl, json_out, controls])
-        inp.submit(predict_emotion, inputs=[inp, table], outputs=[summ, details, tbl, json_out, controls])
-        clear_res.click(lambda: ("", "", "", "{}", _create_controls_panel()), outputs=[summ, details, tbl, json_out, controls])
-        batch_btn.click(batch_predict, inputs=[batch_inp], outputs=[batch_out])
-        hist_btn.click(get_history, outputs=[hist_out])
+        # 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 demo
+    return app
 
 if __name__ == "__main__":
-    print("🎭 Starting Gradio interface...")
-    demo = create_interface()
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, show_error=True)
+    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)