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Multi-Labelled-Review-Categorization-Model

Joblib

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adeyemi001 commited on Sep 11, 2025

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+import os
+import json
+import joblib
+import torch
+import numpy as np
+from flask import Flask, request, render_template_string, jsonify
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+app = Flask(__name__)
+# -----------------------
+# Load artifacts
+# -----------------------
+SAVE_DIR = "./model"
+try:
+    # Load model & tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(SAVE_DIR)
+    model = AutoModelForSequenceClassification.from_pretrained(SAVE_DIR)
+    model.eval()
+    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(DEVICE)
+    # Load MultiLabelBinarizer and labels
+    mlb = joblib.load(os.path.join(SAVE_DIR, "mlb.joblib"))
+    with open(os.path.join(SAVE_DIR, "labels.json"), "r", encoding="utf-8") as f:
+        labels = json.load(f)
+    MODEL_LOADED = True
+    print(f"Model loaded successfully on device: {DEVICE}")
+    print(f"Available labels: {labels}")
+except Exception as e:
+    MODEL_LOADED = False
+    print(f"Error loading model: {e}")
+    tokenizer = None
+    model = None
+    mlb = None
+    labels = []
+# Sigmoid for probabilities
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+# -----------------------
+# Prediction function (single text only)
+# -----------------------
+def predict_single(text, threshold=0.5):
+    """Predict categories for a single text."""
+    if not MODEL_LOADED:
+        return [], []
+    # Tokenize
+    encodings = tokenizer(
+        [text],  # Wrap in list since model expects batch
+        truncation=True,
+        padding=True,
+        max_length=256,
+        return_tensors="pt"
+    ).to(DEVICE)
+    # Forward pass
+    with torch.no_grad():
+        outputs = model(**encodings)
+        logits = outputs.logits.cpu().numpy()
+    # Convert to probabilities
+    probs = sigmoid(logits)
+    # Apply fixed threshold (0.5)
+    pred_bin = (probs >= threshold).astype(int)
+    # Decode to label names
+    row_2d = np.array([pred_bin[0]])
+    categories = mlb.inverse_transform(row_2d)[0]
+    return list(categories), probs[0]
+# HTML Template with embedded CSS + LinkedIn Footer
+HTML_TEMPLATE = """
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Fintech Review Category Classifier</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+            padding: 20px;
+            display: flex;
+            flex-direction: column;
+        }
+        .container {
+            max-width: 1000px;
+            margin: 0 auto;
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 20px;
+            box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
+            backdrop-filter: blur(10px);
+            overflow: hidden;
+            flex: 1;
+        }
+        .header {
+            background: linear-gradient(45deg, #2c3e50, #4a6741);
+            color: white;
+            padding: 30px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+            text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3);
+        }
+        .header p {
+            font-size: 1.2em;
+            opacity: 0.9;
+        }
+        .main-content {
+            padding: 40px;
+        }
+        .input-section {
+            margin-bottom: 30px;
+        }
+        .form-group {
+            margin-bottom: 20px;
+        }
+        label {
+            display: block;
+            margin-bottom: 10px;
+            font-weight: 600;
+            color: #333;
+            font-size: 1.1em;
+        }
+        textarea {
+            width: 100%;
+            min-height: 120px;
+            padding: 15px;
+            border: 2px solid #e0e0e0;
+            border-radius: 10px;
+            font-size: 16px;
+            font-family: inherit;
+            resize: vertical;
+            transition: all 0.3s ease;
+        }
+        textarea:focus {
+            border-color: #667eea;
+            outline: none;
+            box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1);
+        }
+        .controls {
+            display: flex;
+            gap: 20px;
+            align-items: center;
+            flex-wrap: wrap;
+            margin-bottom: 20px;
+        }
+        .btn {
+            background: linear-gradient(45deg, #667eea, #764ba2);
+            color: white;
+            border: none;
+            padding: 15px 30px;
+            font-size: 16px;
+            font-weight: 600;
+            border-radius: 25px;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3);
+        }
+        .btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4);
+        }
+        .btn:active {
+            transform: translateY(0);
+        }
+        .btn:disabled {
+            opacity: 0.6;
+            cursor: not-allowed;
+            transform: none;
+        }
+        .results-section {
+            margin-top: 30px;
+        }
+        .result-card {
+            background: #f8f9ff;
+            border: 1px solid #e0e8ff;
+            border-radius: 15px;
+            padding: 25px;
+            margin-bottom: 20px;
+            box-shadow: 0 5px 15px rgba(0, 0, 0, 0.05);
+        }
+        .original-text {
+            background: #fff;
+            padding: 15px;
+            border-radius: 8px;
+            border-left: 4px solid #667eea;
+            margin-bottom: 20px;
+            font-style: italic;
+            color: #555;
+        }
+        .categories {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 10px;
+            margin-bottom: 15px;
+        }
+        .category-tag {
+            background: linear-gradient(45deg, #48bb78, #38a169);
+            color: white;
+            padding: 8px 15px;
+            border-radius: 20px;
+            font-size: 14px;
+            font-weight: 500;
+            box-shadow: 0 2px 5px rgba(72, 187, 120, 0.3);
+        }
+        .no-categories {
+            color: #666;
+            font-style: italic;
+            padding: 10px;
+            background: #f0f0f0;
+            border-radius: 8px;
+        }
+        .loading {
+            display: none;
+            text-align: center;
+            padding: 20px;
+            color: #667eea;
+            font-size: 18px;
+        }
+        .loading.show {
+            display: block;
+        }
+        .error {
+            background: #fed7d7;
+            color: #c53030;
+            padding: 15px;
+            border-radius: 8px;
+            margin: 20px 0;
+            border-left: 4px solid #c53030;
+        }
+        .model-status {
+            padding: 15px;
+            border-radius: 8px;
+            margin-bottom: 20px;
+            font-weight: 500;
+        }
+        .model-status.loaded {
+            background: #c6f6d5;
+            color: #22543d;
+            border-left: 4px solid #38a169;
+        }
+        .model-status.error {
+            background: #fed7d7;
+            color: #c53030;
+            border-left: 4px solid #c53030;
+        }
+        footer {
+            text-align: center;
+            padding: 20px;
+            background: #2c3e50;
+            color: #ecf0f1;
+            font-size: 14px;
+            margin-top: auto;
+        }
+        footer a {
+            color: #667eea;
+            text-decoration: none;
+            font-weight: 600;
+        }
+        footer a:hover {
+            text-decoration: underline;
+        }
+        @media (max-width: 768px) {
+            .header h1 {
+                font-size: 2em;
+            }
+            .main-content {
+                padding: 20px;
+            }
+            .controls {
+                flex-direction: column;
+                align-items: stretch;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🏦 Fintech Review Classifier</h1>
+            <p>Classify your customer review into relevant categories</p>
+        </div>
+        <div class="main-content">
+            {% if model_loaded %}
+                <div class="model-status loaded">
+                    ✅ Model loaded successfully! Available categories: {{ labels|length }}
+                </div>
+            {% else %}
+                <div class="model-status error">
+                    ❌ Model could not be loaded. Please check if the model files exist in './model' directory.
+                </div>
+            {% endif %}
+            <form id="classifyForm" {% if not model_loaded %}style="opacity: 0.5; pointer-events: none;"{% endif %}>
+                <div class="input-section">
+                    <div class="form-group">
+                        <label for="review_text">Enter Customer Review:</label>
+                        <textarea id="review_text" name="review_text" placeholder="Type a single customer review here..." required>{{ sample_text if sample_text else 'The app crashes every time I try to open it.' }}</textarea>
+                    </div>
+                    <div class="controls">
+                        <button type="submit" class="btn" {% if not model_loaded %}disabled{% endif %}>
+                            🔍 Classify Review
+                        </button>
+                    </div>
+                </div>
+            </form>
+            <div class="loading" id="loading">
+                <div>🤖 Analyzing review...</div>
+            </div>
+            <div class="results-section" id="results" style="display: none;">
+                <!-- Results will be inserted here -->
+            </div>
+        </div>
+    </div>
+    <footer>
+        Made with ❤️ by Adediran Adeyemi — <a href="https://www.linkedin.com/in/adediran-adeyemi-17103b114/" target="_blank">Connect with me on LinkedIn</a>
+    </footer>
+    <script>
+        // Handle form submission
+        document.getElementById('classifyForm').addEventListener('submit', async function(e) {
+            e.preventDefault();
+            const formData = new FormData(this);
+            const loading = document.getElementById('loading');
+            const results = document.getElementById('results');
+            // Show loading, hide results
+            loading.classList.add('show');
+            results.style.display = 'none';
+            try {
+                const response = await fetch('/predict', {
+                    method: 'POST',
+                    body: formData
+                });
+                const data = await response.json();
+                if (data.error) {
+                    throw new Error(data.error);
+                }
+                displayResults(data);
+            } catch (error) {
+                results.innerHTML = '<div class="error">❌ Error: ' + error.message + '</div>';
+                results.style.display = 'block';
+            } finally {
+                loading.classList.remove('show');
+            }
+        });
+        function displayResults(data) {
+            const results = document.getElementById('results');
+            // Clear any existing content completely
+            results.innerHTML = '';
+            // Create results header
+            const header = document.createElement('h2');
+            header.textContent = '🎯 Classification Result';
+            results.appendChild(header);
+            // Only one result expected
+            const result = data.results[0];
+            const card = document.createElement('div');
+            card.className = 'result-card';
+            // Original text section
+            const textDiv = document.createElement('div');
+            textDiv.className = 'original-text';
+            textDiv.innerHTML = `<strong>Review:</strong> "${result.text}"`;
+            card.appendChild(textDiv);
+            // Categories section
+            const categoriesDiv = document.createElement('div');
+            categoriesDiv.className = 'categories';
+            if (result.categories.length > 0) {
+                result.categories.forEach(cat => {
+                    const tag = document.createElement('span');
+                    tag.className = 'category-tag';
+                    tag.textContent = cat;
+                    categoriesDiv.appendChild(tag);
+                });
+            } else {
+                const noCategories = document.createElement('div');
+                noCategories.className = 'no-categories';
+                noCategories.textContent = 'No categories above threshold';
+                categoriesDiv.appendChild(noCategories);
+            }
+            card.appendChild(categoriesDiv);
+            results.appendChild(card);
+            results.style.display = 'block';
+        }
+    </script>
+</body>
+</html>
+"""
+@app.route('/')
+def index():
+    return render_template_string(
+        HTML_TEMPLATE,
+        model_loaded=MODEL_LOADED,
+        labels=labels,
+        sample_text=""
+    )
+@app.route('/predict', methods=['POST'])
+def predict_route():
+    if not MODEL_LOADED:
+        return jsonify({'error': 'Model not loaded. Please check model files.'}), 500
+    try:
+        review_text = request.form.get('review_text', '').strip()
+        if not review_text:
+            return jsonify({'error': 'Please enter a review.'}), 400
+        # Predict for SINGLE review only
+        categories, _ = predict_single(review_text, threshold=0.5)
+        # Format result (only one result object)
+        result = {
+            'text': review_text,
+            'categories': categories
+        }
+        return jsonify({
+            'success': True,
+            'results': [result],  # Still wrapped in list for frontend compatibility
+            'threshold': 0.5
+        })
+    except Exception as e:
+        return jsonify({'error': f'Prediction error: {str(e)}'}), 500
+@app.route('/health')
+def health():
+    return jsonify({
+        'status': 'healthy',
+        'model_loaded': MODEL_LOADED,
+        'device': DEVICE if MODEL_LOADED else 'N/A',
+        'labels_count': len(labels) if labels else 0
+    })
+if __name__ == '__main__':
+    print("="*50)
+    print("🚀 Starting Fintech Review Classification App")
+    print("="*50)
+    if MODEL_LOADED:
+        print(f"✅ Model loaded successfully on {DEVICE}")
+        print(f"📋 Available categories: {len(labels)}")
+        print(f"🏷️  Categories: {', '.join(labels[:5])}{'...' if len(labels) > 5 else ''}")
+    else:
+        print("❌ Model failed to load - app will run in demo mode")
+    print("🌐 Open your browser to: http://localhost:5000")
+    print("="*50)
+    app.run(host='0.0.0.0', port=5000)