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| import gradio as gr | |
| from transformers import pipeline | |
| import time | |
| # ========================================== | |
| # MODEL CONFIGURATION | |
| # ========================================== | |
| MODEL_NAME = "ENTUM-AI/FinBERT-Multi" | |
| print(f"Loading model: {MODEL_NAME}...") | |
| try: | |
| classifier = pipeline("text-classification", model=MODEL_NAME, top_k=3) | |
| print("Model loaded successfully!") | |
| except Exception as e: | |
| print(f"Error loading model: {e}") | |
| classifier = None | |
| # ========================================== | |
| # PREDICTION LOGIC | |
| # ========================================== | |
| SENTIMENT_CONFIG = { | |
| "Positive": {"color": "#16a34a", "bg": "#f0fdf4", "icon": "📈", "bar": "#22c55e"}, | |
| "Negative": {"color": "#dc2626", "bg": "#fef2f2", "icon": "📉", "bar": "#ef4444"}, | |
| "Neutral": {"color": "#2563eb", "bg": "#eff6ff", "icon": "➖", "bar": "#3b82f6"}, | |
| } | |
| def predict_single(text): | |
| """Classify a single financial text.""" | |
| if not text or not text.strip(): | |
| return create_empty_result() | |
| if classifier is None: | |
| return create_error_result() | |
| start = time.time() | |
| results = classifier(text.strip())[0] | |
| elapsed = (time.time() - start) * 1000 | |
| top = results[0] | |
| return create_result_html(text.strip(), top["label"], results, elapsed) | |
| def predict_batch(texts): | |
| """Classify multiple financial texts (one per line).""" | |
| if not texts or not texts.strip(): | |
| return "<p style='color:#94a3b8; text-align:center;'>Enter financial texts, one per line.</p>" | |
| if classifier is None: | |
| return create_error_result() | |
| lines = [line.strip() for line in texts.strip().split("\n") if line.strip()] | |
| if not lines: | |
| return "<p style='color:#94a3b8; text-align:center;'>No valid texts found.</p>" | |
| start = time.time() | |
| all_results = classifier(lines) | |
| elapsed = (time.time() - start) * 1000 | |
| counts = {"Positive": 0, "Negative": 0, "Neutral": 0} | |
| html_parts = [] | |
| for text, results in zip(lines, all_results): | |
| top = results[0] | |
| label = top["label"] | |
| score = top["score"] | |
| counts[label] = counts.get(label, 0) + 1 | |
| cfg = SENTIMENT_CONFIG.get(label, SENTIMENT_CONFIG["Neutral"]) | |
| bar_width = int(score * 100) | |
| html_parts.append(f""" | |
| <div style=" | |
| background: {cfg['bg']}; | |
| border: 1px solid {cfg['color']}22; | |
| border-left: 4px solid {cfg['color']}; | |
| border-radius: 12px; | |
| padding: 16px 20px; | |
| margin-bottom: 10px; | |
| "> | |
| <div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:8px;"> | |
| <span style="color:#1e293b; font-size:14px; flex:1; margin-right:12px;">{cfg['icon']} {text}</span> | |
| <span style=" | |
| background: {cfg['color']}15; | |
| color: {cfg['color']}; | |
| padding: 4px 12px; | |
| border-radius: 20px; | |
| font-size: 12px; | |
| font-weight: 700; | |
| letter-spacing: 0.5px; | |
| white-space: nowrap; | |
| ">{label.upper()} {score:.0%}</span> | |
| </div> | |
| <div style="background:#e2e8f0; border-radius:6px; height:6px; overflow:hidden;"> | |
| <div style="width:{bar_width}%; height:100%; background:linear-gradient(90deg, {cfg['bar']}aa, {cfg['bar']}); border-radius:6px;"></div> | |
| </div> | |
| </div> | |
| """) | |
| total = len(lines) | |
| summary = f""" | |
| <div style=" | |
| background: #ffffff; | |
| border: 1px solid #e2e8f0; | |
| border-radius: 14px; | |
| padding: 20px 24px; | |
| margin-bottom: 16px; | |
| text-align: center; | |
| box-shadow: 0 1px 3px rgba(0,0,0,0.06); | |
| "> | |
| <span style="color:#64748b; font-size:12px; text-transform:uppercase; letter-spacing:1px;">Batch Sentiment Analysis</span> | |
| <div style="display:flex; justify-content:center; gap:24px; margin-top:12px;"> | |
| <div> | |
| <div style="color:#16a34a; font-size:24px; font-weight:800;">📈 {counts.get('Positive', 0)}</div> | |
| <div style="color:#64748b; font-size:12px;">Positive</div> | |
| </div> | |
| <div> | |
| <div style="color:#2563eb; font-size:24px; font-weight:800;">➖ {counts.get('Neutral', 0)}</div> | |
| <div style="color:#64748b; font-size:12px;">Neutral</div> | |
| </div> | |
| <div> | |
| <div style="color:#dc2626; font-size:24px; font-weight:800;">📉 {counts.get('Negative', 0)}</div> | |
| <div style="color:#64748b; font-size:12px;">Negative</div> | |
| </div> | |
| </div> | |
| <div style="color:#94a3b8; font-size:12px; margin-top:10px;">{total} texts analyzed in {elapsed:.0f}ms</div> | |
| </div> | |
| """ | |
| return summary + "\n".join(html_parts) | |
| # ========================================== | |
| # HTML RESULT BUILDERS | |
| # ========================================== | |
| def create_result_html(text, top_label, all_scores, elapsed_ms): | |
| cfg = SENTIMENT_CONFIG.get(top_label, SENTIMENT_CONFIG["Neutral"]) | |
| bars_html = "" | |
| for item in all_scores: | |
| lbl = item["label"] | |
| sc = item["score"] | |
| c = SENTIMENT_CONFIG.get(lbl, SENTIMENT_CONFIG["Neutral"]) | |
| pct = int(sc * 100) | |
| bars_html += f""" | |
| <div style="margin-bottom:10px;"> | |
| <div style="display:flex; justify-content:space-between; margin-bottom:4px;"> | |
| <span style="color:#475569; font-size:13px; font-weight:600;">{c['icon']} {lbl}</span> | |
| <span style="color:{c['color']}; font-weight:700; font-size:14px;">{sc:.1%}</span> | |
| </div> | |
| <div style="background:#f1f5f9; border-radius:6px; height:8px; overflow:hidden;"> | |
| <div style="width:{pct}%; height:100%; background:linear-gradient(90deg, {c['bar']}aa, {c['bar']}); border-radius:6px;"></div> | |
| </div> | |
| </div> | |
| """ | |
| if top_label == "Positive": | |
| gradient = "linear-gradient(135deg, #dcfce7, #bbf7d0, #86efac)" | |
| text_color = "#166534" | |
| elif top_label == "Negative": | |
| gradient = "linear-gradient(135deg, #fee2e2, #fecaca, #fca5a5)" | |
| text_color = "#991b1b" | |
| else: | |
| gradient = "linear-gradient(135deg, #dbeafe, #bfdbfe, #93c5fd)" | |
| text_color = "#1e40af" | |
| top_score = all_scores[0]["score"] | |
| return f""" | |
| <div style="font-family: 'Inter', 'Segoe UI', sans-serif;"> | |
| <div style=" | |
| background: {gradient}; | |
| border-radius: 20px; | |
| padding: 32px; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| border: 1px solid {cfg['color']}22; | |
| box-shadow: 0 4px 24px {cfg['color']}15; | |
| "> | |
| <div style="font-size: 48px; margin-bottom: 8px;">{cfg['icon']}</div> | |
| <div style=" | |
| color: {text_color}; | |
| font-size: 22px; | |
| font-weight: 800; | |
| letter-spacing: 2px; | |
| margin-bottom: 6px; | |
| ">{top_label.upper()} SENTIMENT</div> | |
| <div style="color: {text_color}99; font-size: 14px;">Confidence: {top_score:.1%}</div> | |
| </div> | |
| <div style=" | |
| background: #ffffff; | |
| border: 1px solid #e2e8f0; | |
| border-radius: 16px; | |
| padding: 24px; | |
| box-shadow: 0 1px 3px rgba(0,0,0,0.06); | |
| "> | |
| <div style="margin-bottom: 20px;"> | |
| <span style="color: #64748b; font-size: 11px; text-transform: uppercase; letter-spacing: 1px;">Analyzed Text</span> | |
| <div style="color: #1e293b; font-size: 15px; margin-top: 6px; font-style: italic;">"{text}"</div> | |
| </div> | |
| <div style="margin-bottom: 16px;"> | |
| <span style="color: #64748b; font-size: 11px; text-transform: uppercase; letter-spacing: 1px; margin-bottom: 10px; display:block;">Score Breakdown</span> | |
| {bars_html} | |
| </div> | |
| <div style=" | |
| display: flex; | |
| justify-content: center; | |
| gap: 24px; | |
| padding-top: 12px; | |
| border-top: 1px solid #f1f5f9; | |
| "> | |
| <div style="text-align: center;"> | |
| <span style="color: #94a3b8; font-size: 11px; text-transform: uppercase; letter-spacing: 0.5px;">Model</span> | |
| <div style="color: #6366f1; font-size: 13px; font-weight: 600; margin-top: 2px;">FinBERT-Multi</div> | |
| </div> | |
| <div style="text-align: center;"> | |
| <span style="color: #94a3b8; font-size: 11px; text-transform: uppercase; letter-spacing: 0.5px;">Latency</span> | |
| <div style="color: #0891b2; font-size: 13px; font-weight: 600; margin-top: 2px;">{elapsed_ms:.0f}ms</div> | |
| </div> | |
| <div style="text-align: center;"> | |
| <span style="color: #94a3b8; font-size: 11px; text-transform: uppercase; letter-spacing: 0.5px;">Training Data</span> | |
| <div style="color: #d97706; font-size: 13px; font-weight: 600; margin-top: 2px;">143K+</div> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| """ | |
| def create_empty_result(): | |
| return """ | |
| <div style=" | |
| text-align: center; | |
| padding: 60px 24px; | |
| color: #94a3b8; | |
| "> | |
| <div style="font-size: 48px; margin-bottom: 12px;">💹</div> | |
| <div style="font-size: 16px; font-weight: 600; color: #475569;">Awaiting Input</div> | |
| <div style="font-size: 13px; margin-top: 4px;">Enter a financial text above and click <b>Analyze</b></div> | |
| </div> | |
| """ | |
| def create_error_result(): | |
| return """ | |
| <div style=" | |
| text-align: center; | |
| padding: 40px 24px; | |
| background: #fef2f2; | |
| border-radius: 16px; | |
| border: 1px solid #fecaca; | |
| "> | |
| <div style="font-size: 36px; margin-bottom: 8px;">⚠️</div> | |
| <div style="color: #dc2626; font-size: 15px; font-weight: 600;">Model Not Available</div> | |
| <div style="color: #64748b; font-size: 13px; margin-top: 4px;">Please wait while the model loads or try refreshing.</div> | |
| </div> | |
| """ | |
| # ========================================== | |
| # CUSTOM CSS | |
| # ========================================== | |
| CUSTOM_CSS = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap'); | |
| * { font-family: 'Inter', 'Segoe UI', sans-serif !important; } | |
| .gradio-container { | |
| max-width: 960px !important; | |
| margin: 0 auto !important; | |
| background: linear-gradient(180deg, #f8fafc 0%, #f1f5f9 50%, #e2e8f0 100%) !important; | |
| } | |
| .main-header { | |
| text-align: center; | |
| padding: 40px 20px 20px; | |
| } | |
| .main-header h1 { | |
| background: linear-gradient(135deg, #059669, #0891b2, #2563eb); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| font-size: 2.5rem !important; | |
| font-weight: 800 !important; | |
| margin-bottom: 8px !important; | |
| letter-spacing: -0.5px; | |
| } | |
| .main-header p { | |
| color: #64748b !important; | |
| font-size: 15px !important; | |
| } | |
| .model-badge { | |
| display: inline-block; | |
| background: linear-gradient(135deg, #ecfdf5, #e0f2fe); | |
| border: 1px solid #a7f3d0; | |
| color: #047857 !important; | |
| padding: 6px 16px; | |
| border-radius: 24px; | |
| font-size: 13px !important; | |
| font-weight: 600; | |
| letter-spacing: 0.5px; | |
| margin-top: 12px; | |
| } | |
| .data-badge { | |
| display: inline-block; | |
| background: linear-gradient(135deg, #fef3c7, #fde68a); | |
| border: 1px solid #fbbf24; | |
| color: #92400e !important; | |
| padding: 4px 12px; | |
| border-radius: 16px; | |
| font-size: 12px !important; | |
| font-weight: 600; | |
| margin-left: 8px; | |
| } | |
| footer { display: none !important; } | |
| .tab-nav button { | |
| color: #64748b !important; | |
| font-weight: 600 !important; | |
| font-size: 14px !important; | |
| } | |
| .tab-nav button.selected { | |
| color: #059669 !important; | |
| border-color: #059669 !important; | |
| } | |
| """ | |
| # ========================================== | |
| # GRADIO UI | |
| # ========================================== | |
| with gr.Blocks( | |
| css=CUSTOM_CSS, | |
| title="FinBERT-Multi — Financial Sentiment Analyzer", | |
| theme=gr.themes.Soft( | |
| primary_hue="emerald", | |
| secondary_hue="cyan", | |
| neutral_hue="slate", | |
| ), | |
| ) as demo: | |
| # Header | |
| gr.HTML(""" | |
| <div class="main-header"> | |
| <h1>💹 FinBERT-Multi</h1> | |
| <p>Financial sentiment analysis powered by <b>FinBERT</b>, fine-tuned on <b>143K+ samples</b> from 5 expert datasets</p> | |
| <span class="model-badge">🧠 ENTUM-AI / FinBERT-Multi</span> | |
| <span class="data-badge">📊 143K+ training samples</span> | |
| </div> | |
| """) | |
| with gr.Tabs(): | |
| # --- Tab 1: Single Analysis --- | |
| with gr.Tab("🔍 Single Analysis"): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| single_input = gr.Textbox( | |
| label="Financial Text", | |
| placeholder="e.g. Stock price soars on record-breaking earnings report", | |
| lines=2, | |
| max_lines=4, | |
| ) | |
| single_btn = gr.Button("⚡ Analyze Sentiment", variant="primary", size="lg") | |
| with gr.Column(scale=4): | |
| single_output = gr.HTML(value=create_empty_result()) | |
| gr.Examples( | |
| examples=[ | |
| ["Stock price soars on record-breaking earnings report"], | |
| ["Revenue decline signals weakening market position"], | |
| ["Company announces quarterly earnings results"], | |
| ["Shares surge 15% after strong Q3 revenue growth"], | |
| ["Major layoffs expected as company restructures operations"], | |
| ["The board of directors met to discuss routine operations"], | |
| ["Bankruptcy filing raises concerns about long-term viability"], | |
| ["Profit margins improved significantly driven by cost optimization"], | |
| ], | |
| inputs=single_input, | |
| label="📋 Try these examples", | |
| ) | |
| single_btn.click(fn=predict_single, inputs=single_input, outputs=single_output) | |
| single_input.submit(fn=predict_single, inputs=single_input, outputs=single_output) | |
| # --- Tab 2: Batch Analysis --- | |
| with gr.Tab("📊 Batch Analysis"): | |
| gr.Markdown("Paste multiple financial texts — **one per line** — for batch sentiment classification.") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| batch_input = gr.Textbox( | |
| label="Financial Texts (one per line)", | |
| placeholder="Headline 1\nHeadline 2\nHeadline 3", | |
| lines=8, | |
| max_lines=20, | |
| ) | |
| batch_btn = gr.Button("⚡ Analyze All", variant="primary", size="lg") | |
| with gr.Column(scale=3): | |
| batch_output = gr.HTML( | |
| value="<p style='color:#94a3b8; text-align:center; padding:40px;'>Results will appear here.</p>" | |
| ) | |
| batch_btn.click(fn=predict_batch, inputs=batch_input, outputs=batch_output) | |
| # --- Tab 3: About --- | |
| with gr.Tab("ℹ️ About"): | |
| gr.HTML(""" | |
| <div style=" | |
| background: #ffffff; | |
| border: 1px solid #e2e8f0; | |
| border-radius: 20px; | |
| padding: 36px; | |
| color: #1e293b; | |
| box-shadow: 0 1px 3px rgba(0,0,0,0.06); | |
| "> | |
| <h2 style=" | |
| background: linear-gradient(135deg, #059669, #0891b2); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| font-size: 24px; | |
| margin-bottom: 24px; | |
| ">About FinBERT-Multi</h2> | |
| <p style="color:#475569; font-size:14px; line-height:1.7; margin-bottom:20px;"> | |
| A financial sentiment model built on | |
| <a href="https://huggingface.co/ProsusAI/finbert" style="color:#059669; text-decoration:none; font-weight:600;">ProsusAI/FinBERT</a>. | |
| Fine-tuned on <b>143K+ samples</b> from 5 combined financial datasets for maximum coverage and robustness. | |
| </p> | |
| <table style="width:100%; border-collapse:separate; border-spacing:0 8px;"> | |
| <tr> | |
| <td style="color:#64748b; padding:8px 16px; font-size:13px; width:35%;">Base Model</td> | |
| <td style="color:#1e293b; padding:8px 16px; font-size:14px; font-weight:600;">ProsusAI/FinBERT (BERT-based)</td> | |
| </tr> | |
| <tr> | |
| <td style="color:#64748b; padding:8px 16px; font-size:13px;">Task</td> | |
| <td style="color:#1e293b; padding:8px 16px; font-size:14px; font-weight:600;">3-Class Sentiment (Positive / Negative / Neutral)</td> | |
| </tr> | |
| <tr> | |
| <td style="color:#64748b; padding:8px 16px; font-size:13px;">Training Data</td> | |
| <td style="color:#1e293b; padding:8px 16px; font-size:14px; font-weight:600;">143K+ samples from 5 datasets</td> | |
| </tr> | |
| <tr> | |
| <td style="color:#64748b; padding:8px 16px; font-size:13px;">Language</td> | |
| <td style="color:#1e293b; padding:8px 16px; font-size:14px; font-weight:600;">English</td> | |
| </tr> | |
| <tr> | |
| <td style="color:#64748b; padding:8px 16px; font-size:13px;">License</td> | |
| <td style="color:#1e293b; padding:8px 16px; font-size:14px; font-weight:600;">Apache 2.0</td> | |
| </tr> | |
| </table> | |
| <h3 style="color:#059669; margin-top:28px; margin-bottom:12px; font-size:16px;">📊 Training Datasets</h3> | |
| <div style="overflow-x:auto;"> | |
| <table style="width:100%; border-collapse:separate; border-spacing:0; border:1px solid #e2e8f0; border-radius:12px; overflow:hidden;"> | |
| <thead> | |
| <tr style="background:#f8fafc;"> | |
| <th style="padding:12px 16px; text-align:left; color:#475569; font-size:13px; font-weight:600; border-bottom:1px solid #e2e8f0;">Dataset</th> | |
| <th style="padding:12px 16px; text-align:right; color:#475569; font-size:13px; font-weight:600; border-bottom:1px solid #e2e8f0;">Samples</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr><td style="padding:10px 16px; font-size:13px; color:#1e293b; border-bottom:1px solid #f1f5f9;">FinanceInc/auditor_sentiment</td><td style="padding:10px 16px; font-size:13px; color:#475569; text-align:right; border-bottom:1px solid #f1f5f9;">~4.8K</td></tr> | |
| <tr><td style="padding:10px 16px; font-size:13px; color:#1e293b; border-bottom:1px solid #f1f5f9;">nickmuchi/financial-classification</td><td style="padding:10px 16px; font-size:13px; color:#475569; text-align:right; border-bottom:1px solid #f1f5f9;">~5K</td></tr> | |
| <tr><td style="padding:10px 16px; font-size:13px; color:#1e293b; border-bottom:1px solid #f1f5f9;">warwickai/financial_phrasebank_mirror</td><td style="padding:10px 16px; font-size:13px; color:#475569; text-align:right; border-bottom:1px solid #f1f5f9;">~4.8K</td></tr> | |
| <tr><td style="padding:10px 16px; font-size:13px; color:#1e293b; border-bottom:1px solid #f1f5f9;">NOSIBLE/financial-sentiment</td><td style="padding:10px 16px; font-size:13px; color:#475569; text-align:right; border-bottom:1px solid #f1f5f9;">~100K</td></tr> | |
| <tr><td style="padding:10px 16px; font-size:13px; color:#1e293b;">TimKoornstra/financial-tweets-sentiment</td><td style="padding:10px 16px; font-size:13px; color:#475569; text-align:right;">~38K</td></tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| <h3 style="color:#059669; margin-top:28px; margin-bottom:12px; font-size:16px;">🐍 Python API</h3> | |
| <pre style=" | |
| background:#f8fafc; | |
| border:1px solid #e2e8f0; | |
| border-radius:12px; | |
| padding:20px; | |
| color:#1e293b; | |
| font-size:13px; | |
| overflow-x:auto; | |
| font-family: 'Fira Code', 'Cascadia Code', monospace !important; | |
| "><span style="color:#059669">from</span> transformers <span style="color:#059669">import</span> pipeline | |
| classifier = pipeline(<span style="color:#d97706">"text-classification"</span>, | |
| model=<span style="color:#d97706">"ENTUM-AI/FinBERT-Multi"</span>) | |
| result = classifier(<span style="color:#d97706">"Stock price soars on earnings"</span>) | |
| <span style="color:#94a3b8"># [{'label': 'Positive', 'score': 0.99}]</span></pre> | |
| </div> | |
| """) | |
| # Launch | |
| demo.launch() | |