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