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from flask import Flask, request, jsonify, render_template_string |
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from detoxify import Detoxify |
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import os |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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app = Flask(__name__) |
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detoxify_model = Detoxify('multilingual') |
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koala_model = AutoModelForSequenceClassification.from_pretrained("KoalaAI/Text-Moderation") |
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koala_tokenizer = AutoTokenizer.from_pretrained("KoalaAI/Text-Moderation") |
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API_KEY = os.getenv('API_KEY') |
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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>Modern Detoxify & Moderation API Test</title> |
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<script src="https://cdn.tailwindcss.com"></script> |
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</head> |
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<body class="bg-gray-100 dark:bg-gray-900 text-gray-900 dark:text-gray-100"> |
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<div class="container mx-auto px-4 py-8"> |
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<h1 class="text-4xl font-bold mb-6 text-center">Modern Detoxify & Moderation API Test</h1> |
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<form id="testForm" class="bg-white dark:bg-gray-800 shadow-md rounded px-8 pt-6 pb-8 mb-4"> |
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<div class="mb-4"> |
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<label class="block text-gray-700 dark:text-gray-300 text-sm font-bold mb-2" for="api_key">API Key:</label> |
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<input class="shadow appearance-none border rounded w-full py-2 px-3 text-gray-700 dark:text-gray-900 leading-tight focus:outline-none focus:shadow-outline" type="text" id="api_key" name="api_key" required> |
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</div> |
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<div class="mb-4"> |
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<label class="block text-gray-700 dark:text-gray-300 text-sm font-bold mb-2" for="model">Select Model:</label> |
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<select class="shadow appearance-none border rounded w-full py-2 px-3 text-gray-700 dark:text-gray-900 leading-tight focus:outline-none focus:shadow-outline" id="model" name="model"> |
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<option value="unitaryai/detoxify-multilingual" selected>unitaryai/detoxify-multilingual</option> |
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<option value="koalaai/text-moderation">koalaai/text-moderation</option> |
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</select> |
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</div> |
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<div class="mb-4"> |
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<label class="block text-gray-700 dark:text-gray-300 text-sm font-bold mb-2" for="text">Text to Analyze:</label> |
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<input class="shadow appearance-none border rounded w-full py-2 px-3 text-gray-700 dark:text-gray-900 leading-tight focus:outline-none focus:shadow-outline" type="text" id="text" name="text" required> |
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</div> |
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<div class="flex items-center justify-between"> |
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<button class="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded focus:outline-none focus:shadow-outline" type="submit">Analyze</button> |
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</div> |
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</form> |
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<div id="results" class="mt-6"></div> |
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</div> |
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<script> |
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document.getElementById('testForm').addEventListener('submit', async function(event) { |
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event.preventDefault(); |
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const apiKey = document.getElementById('api_key').value; |
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const model = document.getElementById('model').value; |
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const text = document.getElementById('text').value; |
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try { |
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const response = await fetch('/v1/moderations', { |
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method: 'POST', |
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headers: { 'Content-Type': 'application/json' }, |
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body: JSON.stringify({ api_key: apiKey, model: model, texts: [text] }) |
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}); |
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const data = await response.json(); |
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const resultsDiv = document.getElementById('results'); |
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if (data.error) { |
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resultsDiv.innerHTML = `<p class="text-red-500 font-bold">Error: ${data.error}</p>`; |
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} else { |
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let html = '<h2 class="text-2xl font-bold mb-4">Results:</h2>'; |
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data.results.forEach(item => { |
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html += `<div class="mb-4 p-4 bg-gray-200 dark:bg-gray-700 rounded"> |
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<p class="font-semibold">Input: ${item.input}</p> |
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<ul>`; |
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for (const [key, value] of Object.entries(item.predictions)) { |
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html += `<li>${key}: ${value.toFixed(5)}</li>`; |
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} |
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html += ` </ul> |
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</div>`; |
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}); |
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resultsDiv.innerHTML = html; |
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} |
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} catch (error) { |
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console.error('Error:', error); |
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} |
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}); |
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</script> |
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</body> |
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</html> |
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''' |
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@app.route('/v1/moderations', methods=['POST']) |
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def moderations(): |
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data = request.get_json() |
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api_key = data.get('api_key') |
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texts = data.get('texts') |
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model_choice = data.get('model', 'unitaryai/detoxify-multilingual') |
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if api_key != API_KEY: |
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return jsonify({"error": "Geçersiz API anahtarı"}), 401 |
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if not texts or not isinstance(texts, list): |
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return jsonify({"error": "Geçersiz giriş, metin listesi bekleniyor"}), 400 |
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results = [] |
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if model_choice == "koalaai/text-moderation": |
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for text in texts: |
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inputs = koala_tokenizer(text, return_tensors="pt") |
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outputs = koala_model(**inputs) |
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logits = outputs.logits |
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probabilities = torch.softmax(logits, dim=-1).squeeze().tolist() |
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if isinstance(probabilities, float): |
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probabilities = [probabilities] |
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labels = [koala_model.config.id2label[idx] for idx in range(len(probabilities))] |
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prediction = {label: prob for label, prob in zip(labels, probabilities)} |
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results.append({"input": text, "predictions": prediction}) |
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response_model = "koalaai/text-moderation" |
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else: |
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for text in texts: |
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pred = detoxify_model.predict([text]) |
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prediction = {k: v[0] for k, v in pred.items()} |
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results.append({"input": text, "predictions": prediction}) |
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response_model = "unitaryai/detoxify-multilingual" |
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response_data = { |
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"object": "moderation", |
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"model": response_model, |
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"results": results |
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} |
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return jsonify(response_data) |
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@app.route('/') |
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def home(): |
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return render_template_string(HTML_TEMPLATE) |
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if __name__ == '__main__': |
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port = int(os.getenv('PORT', 7860)) |
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app.run(host='0.0.0.0', port=port, debug=True) |
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