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| from flask import Flask, request, jsonify, render_template_string | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| app = Flask(__name__) | |
| print("Loading model...") | |
| tokenizer = AutoTokenizer.from_pretrained("Redfire-1234/bert-ai-human-model") | |
| model = AutoModelForSequenceClassification.from_pretrained("Redfire-1234/bert-ai-human-model") | |
| model.eval() | |
| print("Model loaded!") | |
| def predict_text(text): | |
| """Predict whether text is AI or Human generated""" | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| probs = torch.softmax(logits, dim=1).numpy()[0] | |
| predicted_class = int(torch.argmax(logits, dim=1)) | |
| label_map = {0: "Human", 1: "AI"} | |
| return { | |
| "label": label_map[predicted_class], | |
| "confidence": float(probs[predicted_class]), | |
| "probabilities": {"human": float(probs[0]), "ai": float(probs[1])} | |
| } | |
| HTML_TEMPLATE = """ | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>AI vs Human Text Classifier</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; max-width: 800px; margin: 50px auto; padding: 20px; background-color: #f5f5f5; } | |
| .container { background-color: white; padding: 30px; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); } | |
| h1 { color: #333; text-align: center; } | |
| textarea { width: 100%; height: 150px; padding: 10px; border: 1px solid #ddd; border-radius: 5px; font-size: 14px; margin-bottom: 20px; box-sizing: border-box; } | |
| button { background-color: #4CAF50; color: white; padding: 12px 30px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px; width: 100%; } | |
| button:hover { background-color: #45a049; } | |
| button:disabled { background-color: #cccccc; cursor: not-allowed; } | |
| .result { margin-top: 20px; padding: 20px; background-color: #f9f9f9; border-radius: 5px; display: none; } | |
| .result.show { display: block; } | |
| .prediction { font-size: 24px; font-weight: bold; margin-bottom: 10px; } | |
| .human { color: #2196F3; } | |
| .ai { color: #FF5722; } | |
| .confidence-bar { width: 100%; height: 30px; background-color: #e0e0e0; border-radius: 15px; overflow: hidden; margin: 10px 0; } | |
| .confidence-fill { height: 100%; background-color: #4CAF50; transition: width 0.3s ease; } | |
| .loading { text-align: center; color: #666; margin-top: 10px; display: none; } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <h1>🤖 AI vs Human Text Classifier</h1> | |
| <p style="text-align: center; color: #666;">Enter text below to check if it was written by a human or AI</p> | |
| <textarea id="textInput" placeholder="Enter your text here..."></textarea> | |
| <button id="classifyBtn" onclick="classifyText()">Classify Text</button> | |
| <div id="loading" class="loading">Analyzing...</div> | |
| <div id="result" class="result"> | |
| <div class="prediction" id="prediction"></div> | |
| <p><strong>Confidence:</strong> <span id="confidence"></span></p> | |
| <div class="confidence-bar"><div class="confidence-fill" id="confidenceBar"></div></div> | |
| <p><strong>Probabilities:</strong></p> | |
| <p>Human: <span id="humanProb"></span></p> | |
| <p>AI: <span id="aiProb"></span></p> | |
| </div> | |
| </div> | |
| <script> | |
| async function classifyText() { | |
| const text = document.getElementById('textInput').value; | |
| const btn = document.getElementById('classifyBtn'); | |
| const loading = document.getElementById('loading'); | |
| const resultDiv = document.getElementById('result'); | |
| if (!text.trim()) { alert('Please enter some text!'); return; } | |
| btn.disabled = true; | |
| loading.style.display = 'block'; | |
| resultDiv.classList.remove('show'); | |
| try { | |
| const response = await fetch('/predict', { | |
| method: 'POST', | |
| headers: {'Content-Type': 'application/json'}, | |
| body: JSON.stringify({text: text}) | |
| }); | |
| const data = await response.json(); | |
| if (data.error) { alert('Error: ' + data.error); return; } | |
| document.getElementById('prediction').textContent = 'Prediction: ' + data.label; | |
| document.getElementById('prediction').className = 'prediction ' + data.label.toLowerCase(); | |
| document.getElementById('confidence').textContent = (data.confidence * 100).toFixed(2) + '%'; | |
| document.getElementById('confidenceBar').style.width = (data.confidence * 100) + '%'; | |
| document.getElementById('humanProb').textContent = (data.probabilities.human * 100).toFixed(2) + '%'; | |
| document.getElementById('aiProb').textContent = (data.probabilities.ai * 100).toFixed(2) + '%'; | |
| resultDiv.classList.add('show'); | |
| } catch (error) { | |
| alert('Error: ' + error.message); | |
| } finally { | |
| btn.disabled = false; | |
| loading.style.display = 'none'; | |
| } | |
| } | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| def home(): | |
| return render_template_string(HTML_TEMPLATE) | |
| def predict(): | |
| try: | |
| data = request.get_json() | |
| if not data or 'text' not in data: | |
| return jsonify({'error': 'No text provided'}), 400 | |
| text = data['text'] | |
| if not text.strip(): | |
| return jsonify({'error': 'Text cannot be empty'}), 400 | |
| result = predict_text(text) | |
| return jsonify(result) | |
| except Exception as e: | |
| print(f"Error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| def health(): | |
| return jsonify({'status': 'healthy'}) | |
| if __name__ == '__main__': | |
| app.run(host='0.0.0.0', port=7860) |