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from flask import Flask, request, jsonify, render_template_string
import os
import uuid
import torch
from detoxify import Detoxify
from transformers import AutoModelForSequenceClassification, AutoTokenizer
app = Flask(__name__)
# Modelleri yükle
detoxify_model = Detoxify('multilingual')
koala_model = AutoModelForSequenceClassification.from_pretrained("KoalaAI/Text-Moderation")
koala_tokenizer = AutoTokenizer.from_pretrained("KoalaAI/Text-Moderation")
# API key environment variable'dan
API_KEY = os.getenv('API_KEY')
# Modern, TailwindCSS destekli HTML arayüzü (dark/light)
HTML_TEMPLATE = '''
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Modern Moderation API Test</title>
<script src="https://cdn.tailwindcss.com"></script>
</head>
<body class="bg-gray-100 dark:bg-gray-900 text-gray-900 dark:text-gray-100">
<div class="container mx-auto px-4 py-8">
<h1 class="text-4xl font-bold mb-6 text-center">Modern Moderation API Test</h1>
<form id="testForm" class="bg-white dark:bg-gray-800 shadow-md rounded px-8 pt-6 pb-8 mb-4">
<div class="mb-4">
<label class="block text-gray-700 dark:text-gray-300 text-sm font-bold mb-2" for="api_key">API Key:</label>
<input type="text" id="api_key" name="api_key" required 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">
</div>
<div class="mb-4">
<label class="block text-gray-700 dark:text-gray-300 text-sm font-bold mb-2" for="model">Select Model:</label>
<select id="model" name="model" 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">
<option value="unitaryai/detoxify-multilingual" selected>unitaryai/detoxify-multilingual</option>
<option value="koalaai/text-moderation">koalaai/text-moderation</option>
</select>
</div>
<div class="mb-4">
<label class="block text-gray-700 dark:text-gray-300 text-sm font-bold mb-2" for="input">Text to Analyze:</label>
<textarea id="input" name="input" rows="4" required 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"></textarea>
</div>
<div class="flex items-center justify-between">
<button type="submit" class="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded focus:outline-none focus:shadow-outline">
Analyze
</button>
</div>
</form>
<div id="results" class="mt-6"></div>
</div>
<script>
document.getElementById('testForm').addEventListener('submit', async function(event) {
event.preventDefault();
const apiKey = document.getElementById('api_key').value;
const model = document.getElementById('model').value;
const input = document.getElementById('input').value;
try {
const response = await fetch('/v1/moderations', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer ' + apiKey
},
body: JSON.stringify({ model: model, input: input })
});
const data = await response.json();
const resultsDiv = document.getElementById('results');
if (data.error) {
resultsDiv.innerHTML = `<p class="text-red-500 font-bold">Error: ${data.error}</p>`;
} else {
let html = '<h2 class="text-2xl font-bold mb-4">Results:</h2>';
data.results.forEach(item => {
html += `<div class="mb-4 p-4 bg-gray-200 dark:bg-gray-700 rounded">
<p class="font-semibold">Flagged: ${item.flagged}</p>
<p class="font-semibold">Categories:</p>
<ul>`;
for (const [key, value] of Object.entries(item.categories)) {
html += `<li>${key}: ${value} (score: ${item.category_scores[key].toFixed(5)})</li>`;
}
html += ` </ul>
</div>`;
});
resultsDiv.innerHTML = html;
}
} catch (error) {
console.error('Error:', error);
}
});
</script>
</body>
</html>
'''
def transform_predictions(model_choice, prediction_dict):
"""
OpenAI tarzı response formatı için tahminleri dönüştür.
Dönüştürülen alanlar: flagged, categories, category_scores, category_applied_input_types
"""
category_keys = [
"sexual", "sexual/minors", "harassment", "harassment/threatening",
"hate", "hate/threatening", "illicit", "illicit/violent",
"self-harm", "self-harm/intent", "self-harm/instructions",
"violence", "violence/graphic"
]
scores = {}
if model_choice == "unitaryai/detoxify-multilingual":
scores["sexual"] = prediction_dict.get("sexual_explicit", 0.0)
scores["harassment"] = max(prediction_dict.get("identity_attack", 0.0), prediction_dict.get("insult", 0.0))
scores["hate"] = prediction_dict.get("toxicity", 0.0)
scores["violence"] = max(prediction_dict.get("severe_toxicity", 0.0), prediction_dict.get("threat", 0.0))
for key in category_keys:
if key not in scores:
scores[key] = 0.0
else:
for key in category_keys:
scores[key] = prediction_dict.get(key, 0.0)
threshold = 0.5
bool_categories = {key: (scores[key] > threshold) for key in category_keys}
cat_applied_input_types = {key: (["text"] if scores[key] > 0 else []) for key in category_keys}
flagged = any(bool_categories.values())
return flagged, bool_categories, scores, cat_applied_input_types
@app.route('/')
def home():
return render_template_string(HTML_TEMPLATE)
@app.route('/v1/moderations', methods=['POST'])
def moderations():
auth_header = request.headers.get('Authorization')
if not auth_header or not auth_header.startswith("Bearer "):
return jsonify({"error": "Unauthorized"}), 401
provided_api_key = auth_header.split(" ")[1]
if provided_api_key != API_KEY:
return jsonify({"error": "Unauthorized"}), 401
data = request.get_json()
raw_input = data.get('input') or data.get('texts')
if raw_input is None:
return jsonify({"error": "Invalid input, expected 'input' or 'texts' field"}), 400
if isinstance(raw_input, str):
texts = [raw_input]
elif isinstance(raw_input, list):
texts = raw_input
else:
return jsonify({"error": "Invalid input format, expected string or list of strings"}), 400
if len(texts) > 10:
return jsonify({"error": "Too many input items. Maximum 10 allowed."}), 400
for text in texts:
if not isinstance(text, str) or len(text) > 100000:
return jsonify({"error": "Each input item must be a string with a maximum of 100k characters."}), 400
results = []
model_choice = data.get('model', 'unitaryai/detoxify-multilingual')
if model_choice == "koalaai/text-moderation":
for text in texts:
inputs = koala_tokenizer(text, return_tensors="pt")
outputs = koala_model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1).squeeze().tolist()
if isinstance(probabilities, float):
probabilities = [probabilities]
labels = [koala_model.config.id2label[idx] for idx in range(len(probabilities))]
prediction = {label: prob for label, prob in zip(labels, probabilities)}
flagged, bool_categories, scores, cat_applied_input_types = transform_predictions(model_choice, prediction)
results.append({
"flagged": flagged,
"categories": bool_categories,
"category_scores": scores,
"category_applied_input_types": cat_applied_input_types
})
response_model = "koalaai/text-moderation"
else:
for text in texts:
pred = detoxify_model.predict([text])
prediction = {k: v[0] for k, v in pred.items()}
flagged, bool_categories, scores, cat_applied_input_types = transform_predictions(model_choice, prediction)
results.append({
"flagged": flagged,
"categories": bool_categories,
"category_scores": scores,
"category_applied_input_types": cat_applied_input_types
})
response_model = "unitaryai/detoxify-multilingual"
response_data = {
"id": "modr-" + uuid.uuid4().hex[:24],
"model": response_model,
"results": results,
"object": "moderation"
}
return jsonify(response_data)
if __name__ == '__main__':
port = int(os.getenv('PORT', 7860))
app.run(host='0.0.0.0', port=port, debug=True)
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