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AI_text_detection

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Redfire-1234 commited on Dec 7, 2025

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7330e34

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Create app.py

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app.py +135 -0

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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>
+"""
+@app.route('/')
+def home():
+    return render_template_string(HTML_TEMPLATE)
+@app.route('/predict', methods=['POST'])
+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
+@app.route('/health')
+def health():
+    return jsonify({'status': 'healthy'})
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)