| 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__)
|
|
|
|
|
|
|
|
|
| SAVE_DIR = "./model"
|
|
|
| try:
|
|
|
| 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)
|
|
|
|
|
| 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 = []
|
|
|
|
|
| def sigmoid(x):
|
| return 1 / (1 + np.exp(-x))
|
|
|
|
|
|
|
|
|
| def predict_single(text, threshold=0.5):
|
| """Predict categories for a single text."""
|
| if not MODEL_LOADED:
|
| return [], []
|
|
|
|
|
| encodings = tokenizer(
|
| [text],
|
| truncation=True,
|
| padding=True,
|
| max_length=256,
|
| return_tensors="pt"
|
| ).to(DEVICE)
|
|
|
|
|
| with torch.no_grad():
|
| outputs = model(**encodings)
|
| logits = outputs.logits.cpu().numpy()
|
|
|
|
|
| probs = sigmoid(logits)
|
|
|
|
|
| pred_bin = (probs >= threshold).astype(int)
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|
|
|
|
| row_2d = np.array([pred_bin[0]])
|
| categories = mlb.inverse_transform(row_2d)[0]
|
|
|
| return list(categories), probs[0]
|
|
|
|
|
| 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 {
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| max-width: 1000px;
|
| margin: 0 auto;
|
| background: rgba(255, 255, 255, 0.95);
|
| border-radius: 20px;
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| box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
|
| backdrop-filter: blur(10px);
|
| overflow: hidden;
|
| flex: 1;
|
| }
|
|
|
| .header {
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| background: linear-gradient(45deg, #2c3e50, #4a6741);
|
| color: white;
|
| padding: 30px;
|
| text-align: center;
|
| }
|
|
|
| .header h1 {
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| font-size: 2.5em;
|
| margin-bottom: 10px;
|
| text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3);
|
| }
|
|
|
| .header p {
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| 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
|
|
|
|
|
| categories, _ = predict_single(review_text, threshold=0.5)
|
|
|
|
|
| result = {
|
| 'text': review_text,
|
| 'categories': categories
|
| }
|
|
|
| return jsonify({
|
| 'success': True,
|
| 'results': [result],
|
| '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) |
