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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install --upgrade pip
+RUN pip install flask torch
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, render_template, request, jsonify
+import torch
+import torch.nn.functional as F
+import re
+import csv
+
+
+class TransformerClassifier(torch.nn.Module):
+    def __init__(self, vocab_size, num_classes, d_model=64, nhead=4, num_layers=2, dim_feedforward=128):
+        super().__init__()
+        self.embedding = torch.nn.Embedding(vocab_size, d_model)
+        encoder_layer = torch.nn.TransformerEncoderLayer(
+            d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward
+        )
+        self.encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.classifier = torch.nn.Linear(d_model, num_classes)
+
+    def forward(self, x):
+        emb = self.embedding(x)
+        emb = emb.permute(1, 0, 2)
+        encoded = self.encoder(emb)
+        cls = encoded[0]
+        return self.classifier(cls)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+checkpoint = torch.load("model/transformer_classifier.pt", map_location=device)
+vocab = checkpoint['vocab']
+class_to_idx = checkpoint['class_to_idx']
+idx_to_class = {i: name for name, i in class_to_idx.items()}
+
+model = TransformerClassifier(len(vocab), len(class_to_idx))
+model.load_state_dict(checkpoint['model_state_dict'])
+model.to(device)
+model.eval()
+
+def tokenize(text):
+    text = text.lower()
+    text = re.sub(r"[^a-z0-9\s]", "", text)
+    return text.split()
+
+def encode(text, max_len=32):
+    tokens = ['<CLS>'] + tokenize(text)
+    ids = [vocab.get(t, vocab['<UNK>']) for t in tokens]
+    if len(ids) < max_len:
+        ids += [vocab['<PAD>']] * (max_len - len(ids))
+    else:
+        ids = ids[:max_len]
+    return ids
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.get_json()
+    text = data['text']
+    encoded = encode(text)
+    x = torch.tensor(encoded).unsqueeze(0).to(device)
+    with torch.no_grad():
+        logits = model(x)
+        probs = F.softmax(logits, dim=1)
+        pred_idx = torch.argmax(probs, dim=1).item()
+        confidence = probs[0, pred_idx].item()
+    return jsonify({
+        'label': idx_to_class[pred_idx],
+        'confidence': round(confidence, 3),
+        'probs': {idx_to_class[i]: round(p.item(), 3) for i, p in enumerate(probs[0])}
+    })
+@app.route('/feedback', methods=['POST'])
+def feedback():
+    data = request.get_json()
+    text = data.get('text')
+    predicted_label = data.get('predicted_label')
+    correct = data.get('correct')
+
+    with open('feedback.txt', 'a') as f:
+        f.write(f'Text: {text}\nPredicted Label: {predicted_label}\nCorrect: {correct}\n\n')
+
+    return jsonify({'status': 'Feedback received'})
+
+@app.route('/correction', methods=['POST'])
+def correction():
+    data = request.get_json()
+    text = data.get('text')
+    predicted_label = data.get('predicted_label')
+    true_label = data.get('true_label')
+
+    with open('corrections.csv', mode='a', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        if file.tell() == 0:
+            writer.writerow(['Text', 'Predicted Label', 'True Label'])
+        writer.writerow([text, predicted_label, true_label])
+
+    return jsonify({'status': 'received'})
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

model/transformer_classifier.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:60667a05c642292a96edf702830ea85be1fb49af56bf606dddeabe594020ff5c
+size 3036310

static/script.js

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+document.addEventListener('DOMContentLoaded', () => {
+      const btn = document.getElementById('submit-btn');
+      const input = document.getElementById('text-input');
+      const resultBox = document.getElementById('result-box');
+
+      async function sendFeedback(isCorrect) {
+        const text = input.value.trim();
+        const predictedLabel = resultBox.textContent.split(":")[1].split(",")[0].trim();
+        await fetch('/feedback', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ text, predicted_label: predictedLabel, correct: isCorrect })
+        });
+      }
+
+      btn.addEventListener('click', async () => {
+        const text = input.value.trim();
+        if (!text) {
+          resultBox.textContent = "Введите текст";
+          resultBox.className = "alert alert-warning mt-4";
+          return;
+        }
+
+        resultBox.textContent = "⏳ Анализ...";
+        resultBox.className = "alert alert-info mt-4";
+
+        try {
+          const response = await fetch('/predict', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ text })
+          });
+
+          const data = await response.json();
+          resultBox.textContent = `🏷 Класс: ${data.label}, уверенность: ${data.confidence}`;
+          resultBox.className = "alert alert-success mt-4";
+
+          document.getElementById("feedback-section").classList.remove("d-none");
+          document.getElementById("probs-container").style.display = 'block';
+          const probsBars = document.getElementById('probs-bars');
+          probsBars.innerHTML = '';
+          for (const [label, prob] of Object.entries(data.probs)) {
+            const bar = document.createElement('div');
+            bar.className = 'progress my-2';
+
+            const inner = document.createElement('div');
+          inner.className = 'progress-bar';
+          inner.style.width = `${prob * 100}%`;
+          inner.style.color = 'black';
+           inner.innerText = `${label}: ${(prob * 100).toFixed(1)}%`;
+
+
+            bar.appendChild(inner);
+            probsBars.appendChild(bar);
+          }
+        } catch (err) {
+          resultBox.textContent = "Ошибка при анализе текста";
+          resultBox.className = "alert alert-danger mt-4";
+        }
+      });
+
+      document.getElementById("btn-correct").addEventListener("click", async () => {
+        await sendFeedback(true);
+        document.getElementById("feedback-section").classList.add("d-none");
+        resultBox.innerHTML += "<br>Спасибо за подтверждение!";
+      });
+
+      document.getElementById("btn-incorrect").addEventListener("click", () => {
+        sendFeedback(false);
+        document.getElementById("correction-form").classList.remove("d-none");
+      });
+
+      document.getElementById("send-correction").addEventListener("click", () => {
+        const correctLabel = document.getElementById("correct-label").value;
+        const text = document.getElementById("text-input").value.trim();
+        const predictedLabel = resultBox.textContent.split(":")[1].split(",")[0].trim();
+
+        if (!correctLabel) {
+          alert("Выберите правильный класс");
+          return;
+        }
+
+        fetch('/correction', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ text, predicted_label: predictedLabel, true_label: correctLabel })
+        }).then(() => {
+          alert("Спасибо, исправление отправлено");
+          document.getElementById("correction-form").classList.add("d-none");
+        });
+      });
+
+      const themeToggle = document.getElementById("theme-toggle");
+      themeToggle.addEventListener("change", () => {
+        document.body.classList.toggle("bg-dark");
+        document.body.classList.toggle("text-white");
+        document.querySelector(".container").classList.toggle("bg-dark");
+        document.querySelector(".container").classList.toggle("text-white");
+        document.body.classList.toggle("dark-theme");
+        document.querySelectorAll('h1, .form-label').forEach(el => {
+        el.classList.toggle('text-dark');
+    })
+      });
+    });

templates/index.html

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+<!doctype html>
+<html lang="ru">
+<head>
+  <meta charset="utf-8">
+  <title>Классификация кибербуллинга</title>
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+
+  <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet">
+  <script defer src="/static/script.js"></script>
+
+  <style>
+    body {
+      background-color: #f8f9fa;
+    }
+    .container {
+      max-width: 700px;
+      margin-top: 5%;
+    }
+    .result-box {
+      margin-top: 1rem;
+      font-weight: bold;
+    }
+  </style>
+</head>
+<body>
+    <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
+      <div class="container-fluid">
+        <a class="navbar-brand" href="#">Cyberbullying AI</a>
+        <div class="form-check form-switch ms-auto me-3 text-white">
+          <input class="form-check-input" type="checkbox" id="theme-toggle">
+          <label class="form-check-label" for="theme-toggle">🌙 Тёмная тема</label>
+        </div>
+      </div>
+    </nav>
+
+  <div class="container bg-white p-5 rounded shadow-sm">
+    <h1 class="mb-4 text-center">Классификация кибербуллинга</h1>
+
+    <div class="mb-3">
+      <label for="text-input" class="form-label">Введите текст</label>
+      <textarea class="form-control" id="text-input" rows="4" placeholder="Напишите сообщение..."></textarea>
+    </div>
+
+    <div class="d-grid">
+      <button class="btn btn-primary" id="submit-btn">Анализировать</button>
+    </div>
+
+    <div class="alert alert-info mt-4 d-none" id="result-box"></div>
+  </div>
+
+    <div id="feedback-section" class="mt-3 d-none">
+      <p>Был ли результат точным?</p>
+      <button class="btn btn-sm btn-success me-2" id="btn-correct">✅ Да</button>
+      <button class="btn btn-sm btn-danger" id="btn-incorrect">❌ Нет</button>
+    </div>
+
+    <div id="correction-form" class="mt-3 d-none">
+      <label for="correct-label" class="form-label">Выберите правильный класс:</label>
+      <select id="correct-label" class="form-select mb-2">
+        <option value="" disabled selected>-- Выберите класс --</option>
+        <option value="age">age</option>
+        <option value="ethnicity">ethnicity</option>
+        <option value="gender">gender</option>
+        <option value="not_cyberbullying">not_cyberbullying</option>
+        <option value="other_cyberbullying">other_cyberbullying</option>
+        <option value="religion">religion</option>
+      </select>
+      <button class="btn btn-primary btn-sm" id="send-correction">Отправить</button>
+    </div>
+
+  <div class="mt-4" id="probs-container" style="display: none;">
+    <h5>Вероятности по классам:</h5>
+    <div id="probs-bars"></div>
+  </div>
+
+
+  <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/js/bootstrap.bundle.min.js"></script>
+</body>
+</html>