changed usage

app.py

@@ -37,8 +37,8 @@ HTML_TEMPLATE = '''
         </select>
       </div>
       <div class="mb-4">
-        <label class="block text-gray-700 dark:text-gray-300 text-sm font-bold mb-2" for="text">Text to Analyze:</label>
-        <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>
+        <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" 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" required></textarea>
       </div>
       <div class="flex items-center justify-between">
         <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>
@@ -50,7 +50,7 @@ HTML_TEMPLATE = '''
     document.getElementById('testForm').addEventListener('submit', async function(event) {
       event.preventDefault();
       const model = document.getElementById('model').value;
-      const text = document.getElementById('text').value;
+      const input = document.getElementById('input').value;
       try {
         const response = await fetch('/v1/moderations', {
           method: 'POST',
@@ -58,7 +58,7 @@ HTML_TEMPLATE = '''
               'Content-Type': 'application/json',
               'Authorization': 'Bearer YOUR_API_KEY'  // Değiştir!
           },
-          body: JSON.stringify({ model: model, texts: [text] })
+          body: JSON.stringify({ model: model, input: input })
         });
         const data = await response.json();
         const resultsDiv = document.getElementById('results');
@@ -107,23 +107,19 @@ def transform_predictions(model_choice, prediction_dict):
         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))
-        # Diğer kategoriler için varsayılan 0 değeri
         for key in category_keys:
             if key not in scores:
                 scores[key] = 0.0
     else:
-        # Koala modelinden gelen tahminlerde, label isimleri doğrudan uyumlu olabilir;
-        # uyumlu değilse varsayılan 0 değeri ver.
         for key in category_keys:
             scores[key] = prediction_dict.get(key, 0.0)
     
-    # Kategori boolean değerleri; eşik değeri: 0.7
+    # Eşik değeri: 0.7
     threshold = 0.7
     bool_categories = {key: (scores[key] > threshold) for key in category_keys}
-    # category_applied_input_types: biz text ile çalıştığımız için, skor > 0 ise ["text"] değilse boş liste
+    # category_applied_input_types: text ile çalışıldığı için
     cat_applied_input_types = {key: (["text"] if scores[key] > 0 else []) for key in category_keys}
     
-    # Flagged: herhangi bir kategori eşik değerinin üzerinde ise True
     flagged = any(bool_categories.values())
     
     return flagged, bool_categories, scores, cat_applied_input_types
@@ -134,7 +130,7 @@ def home():
 
 @app.route('/v1/moderations', methods=['POST'])
 def moderations():
-    # Authorization header'dan API key kontrolü
+    # API key doğrulaması (Bearer token)
     auth_header = request.headers.get('Authorization')
     if not auth_header or not auth_header.startswith("Bearer "):
         return jsonify({"error": "Unauthorized"}), 401
@@ -143,21 +139,39 @@ def moderations():
         return jsonify({"error": "Unauthorized"}), 401
 
     data = request.get_json()
-    texts = data.get('texts')
-    model_choice = data.get('model', 'unitaryai/detoxify-multilingual')
 
-    if not texts or not isinstance(texts, list):
-        return jsonify({"error": "Invalid input, expected a list of texts"}), 400
+    # OpenAI API formatında "input" ya da "texts" kabul edilsin
+    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
+
+    # Eğer string ise listeye çevir
+    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
+
+    # Maksimum 10 öğe
+    if len(texts) > 10:
+        return jsonify({"error": "Too many input items. Maximum 10 allowed."}), 400
+
+    # Her bir öğe maksimum 100k karakter olmalı
+    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 = []
-    # Her bir metin için tahmin ve transform işlemi
+    model_choice = data.get('model', 'unitaryai/detoxify-multilingual')
+
+    # Tahmin ve transform işlemi
     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()
-            # Eğer tek değer ise listeye çevir
             if isinstance(probabilities, float):
                 probabilities = [probabilities]
             labels = [koala_model.config.id2label[idx] for idx in range(len(probabilities))]
@@ -173,7 +187,6 @@ def moderations():
     else:
         for text in texts:
             pred = detoxify_model.predict([text])
-            # Detoxify sonuçları liste formatında, tek değer alıyoruz
             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({