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detoxify

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Utiric commited on Aug 21

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4ad9ac6

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1 Parent(s): f192c7a

Update index.html

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index.html +222 -3

index.html CHANGED Viewed

@@ -1,4 +1,216 @@
-<!DOCTYPE html>
 <html lang="en">
 <head>
     <meta charset="UTF-8">
@@ -613,7 +825,7 @@
                         ${flaggedBadge}
                     </div>
                     <div class="mb-4 p-3 bg-gray-50 dark:bg-gray-700 rounded-lg text-sm">
-                        ${data.input ? data.input[index] : 'Text content not available'}
                     </div>
                     <div class="category-card">
                         <h5 class="font-medium mb-2">Categories</h5>
@@ -645,4 +857,11 @@
         });
     </script>
 </body>
-</html>

+from flask import Flask, request, jsonify, render_template, send_from_directory
+import os
+import uuid
+import time
+import threading
+import tiktoken
+from datetime import datetime, timedelta
+from collections import defaultdict, deque
+from detoxify import Detoxify
+app = Flask(__name__, static_folder='static', template_folder='templates')
+# Load the detoxify model
+detoxify_model = Detoxify('multilingual')
+# API key from environment variable
+API_KEY = os.getenv('API_KEY', 'your-api-key-here')
+# Performance metrics tracking
+request_times = deque(maxlen=1000)  # Store last 1000 request times
+daily_requests = defaultdict(int)
+daily_tokens = defaultdict(int)
+concurrent_requests = 0
+concurrent_requests_lock = threading.Lock()
+# Token encoding for counting tokens
+encoding = tiktoken.get_encoding("cl100k_base")
+def count_tokens(text):
+    """Count tokens in the given text using tiktoken."""
+    return len(encoding.encode(text))
+def transform_predictions(prediction_dict):
+    """
+    Transform predictions to OpenAI-style format.
+    Returns: flagged, categories, category_scores, category_applied_input_types
+    """
+    # Define the categories we want to track
+    category_keys = [
+        "toxicity", "severe_toxicity", "obscene", "threat",
+        "insult", "identity_attack", "sexual_explicit"
+    ]
+    # Map detoxify output to our categories
+    scores = {}
+    for key in category_keys:
+        scores[key] = prediction_dict.get(key, 0.0)
+    # Set threshold for flagging content
+    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
+def track_request_metrics(start_time, tokens_count):
+    """Track performance metrics for requests."""
+    end_time = time.time()
+    request_time = end_time - start_time
+    request_times.append(request_time)
+    today = datetime.now().strftime("%Y-%m-%d")
+    daily_requests[today] += 1
+    daily_tokens[today] += tokens_count
+def get_performance_metrics():
+    """Get current performance metrics."""
+    global concurrent_requests
+    with concurrent_requests_lock:
+        current_concurrent = concurrent_requests
+    # Calculate average request time
+    avg_request_time = sum(request_times) / len(request_times) if request_times else 0
+    # Get today's date
+    today = datetime.now().strftime("%Y-%m-%d")
+    # Calculate requests per second (based on last 100 requests)
+    recent_requests = list(request_times)[-100:] if len(request_times) >= 100 else list(request_times)
+    requests_per_second = len(recent_requests) / sum(recent_requests) if recent_requests and sum(recent_requests) > 0 else 0
+    # Get daily stats
+    today_requests = daily_requests.get(today, 0)
+    today_tokens = daily_tokens.get(today, 0)
+    # Get last 7 days stats
+    last_7_days = []
+    for i in range(7):
+        date = (datetime.now() - timedelta(days=i)).strftime("%Y-%m-%d")
+        last_7_days.append({
+            "date": date,
+            "requests": daily_requests.get(date, 0),
+            "tokens": daily_tokens.get(date, 0)
+        })
+    return {
+        "avg_request_time": avg_request_time,
+        "requests_per_second": requests_per_second,
+        "concurrent_requests": current_concurrent,
+        "today_requests": today_requests,
+        "today_tokens": today_tokens,
+        "last_7_days": last_7_days
+    }
+@app.route('/')
+def home():
+    return render_template('index.html')
+@app.route('/v1/moderations', methods=['POST'])
+def moderations():
+    global concurrent_requests
+    # Track concurrent requests
+    with concurrent_requests_lock:
+        concurrent_requests += 1
+    start_time = time.time()
+    total_tokens = 0
+    try:
+        # Check authorization
+        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
+        # Get input data
+        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
+        # Handle both string and list inputs
+        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
+        # Validate input size
+        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
+            total_tokens += count_tokens(text)
+        # Process each text
+        results = []
+        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(prediction)
+            results.append({
+                "flagged": flagged,
+                "categories": bool_categories,
+                "category_scores": scores,
+                "category_applied_input_types": cat_applied_input_types
+            })
+        # Track metrics
+        track_request_metrics(start_time, total_tokens)
+        # Prepare response
+        response_data = {
+            "id": "modr-" + uuid.uuid4().hex[:24],
+            "model": "unitaryai/detoxify-multilingual",
+            "results": results,
+            "object": "moderation",
+            "usage": {
+                "total_tokens": total_tokens
+            }
+        }
+        return jsonify(response_data)
+    finally:
+        # Decrement concurrent requests counter
+        with concurrent_requests_lock:
+            concurrent_requests -= 1
+@app.route('/v1/metrics', methods=['GET'])
+def metrics():
+    """Endpoint to get performance metrics."""
+    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
+    return jsonify(get_performance_metrics())
+# Create a simple file-based approach for the HTML template
+def create_directories_and_files():
+    """Create necessary directories and files if they don't exist."""
+    os.makedirs('templates', exist_ok=True)
+    os.makedirs('static', exist_ok=True)
+    # Create index.html if it doesn't exist
+    index_path = os.path.join('templates', 'index.html')
+    if not os.path.exists(index_path):
+        with open(index_path, 'w') as f:
+            f.write('''<!DOCTYPE html>
 <html lang="en">
 <head>
     <meta charset="UTF-8">
                         ${flaggedBadge}
                     </div>
                     <div class="mb-4 p-3 bg-gray-50 dark:bg-gray-700 rounded-lg text-sm">
+                        ${texts[index]}
                     </div>
                     <div class="category-card">
                         <h5 class="font-medium mb-2">Categories</h5>
         });
     </script>
 </body>
+</html>''')
+if __name__ == '__main__':
+    # Create directories and files
+    create_directories_and_files()
+    port = int(os.getenv('PORT', 7860))
+    app.run(host='0.0.0.0', port=port, debug=True)