app.py

import os
import re
import base64
import requests
from openai import OpenAI
from dotenv import load_dotenv
from flask import Flask, render_template, request, jsonify

load_dotenv()
client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])

app = Flask(__name__)

def get_blacklist():
    url = "https://api.github.com/repos/ooguz/turkce-kufur-karaliste/contents/karaliste.txt"
    response = requests.get(url)
    if response.status_code == 200:
        content = base64.b64decode(response.json()['content']).decode('utf-8')
        blacklist = content.split('\n')
        return [word.strip() for word in blacklist if word.strip()]
    else:
        raise Exception("GitHub'dan veri çekilemedi.")

def check_content(text):
    blacklist = get_blacklist()
    pattern = r'\b(' + '|'.join(re.escape(word) for word in blacklist) + r')\b'
    if re.search(pattern, text, re.IGNORECASE):
        return "Uygunsuz"
    else:
        return "Uygun"

def analyze_sentiment(text):
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "Verilen metni analiz et ve şu kategorilerden birine yerleştir: Uygun, Uygunsuz, Saldırgan, Kötümser, Kinayeli. Eğer 'Uygun' değilse, neden olduğuna dair kısa, tek cümlelik bir açıklama yap."},
            {"role": "user", "content": text}
        ]
    )
    return response.choices[0].message.content

def analyze_image(image_path):
    with open(image_path, "rb") as image_file:
        base64_image = base64.b64encode(image_file.read()).decode('utf-8')

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Bu görüntüyü analiz et ve uygunluğunu değerlendir. Şu kategorilerden birine yerleştir: Uygun, Uygunsuz, Saldırgan, Kötümser, Kinayeli. Eğer 'Uygun' değilse, neden olduğuna dair kısa, tek cümlelik bir açıklama yap."},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ],
        max_tokens=300
    )
    return response.choices[0].message.content

def analyze_text(text):
    blacklist_result = check_content(text)
    if blacklist_result == "Uygun":
        sentiment_analysis = analyze_sentiment(text)
        sentiment_parts = sentiment_analysis.split('.')
        sentiment_category = sentiment_parts[0].strip()
        if sentiment_category == "Uygun":
            return "Metin onaylandı.", sentiment_category, ""
        else:
            feedback = sentiment_parts[1].strip() if len(sentiment_parts) > 1 else ""
            return f"Metin onaylanmadı.", sentiment_category, feedback
    else:
        return "Metin kara liste kontrolünden geçemedi.", "Uygunsuz", "Kara listedeki kelime kullanımı"

def analyze_image_content(image_path):
    image_analysis = analyze_image(image_path)
    image_parts = image_analysis.split('.')
    image_category = image_parts[0].strip()
    if image_category == "Uygun":
        return "Görsel onaylandı.", image_category, ""
    else:
        feedback = image_parts[1].strip() if len(image_parts) > 1 else ""
        return f"Görsel onaylanmadı.", image_category, feedback

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/analyze_text', methods=['POST'])
def analyze_text_route():
    text = request.form['text']
    result, category, feedback = analyze_text(text)
    return jsonify({
        'result': result,
        'category': category,
        'feedback': feedback
    })

@app.route('/analyze_image', methods=['POST'])
def analyze_image_route():
    if 'image' not in request.files:
        return jsonify({'error': 'No image uploaded'}), 400
    
    image = request.files['image']
    if image.filename == '':
        return jsonify({'error': 'No image selected'}), 400
    
    if image:
        image_path = os.path.join('uploads', image.filename)
        image.save(image_path)
        result, category, feedback = analyze_image_content(image_path)
        os.remove(image_path)  
        return jsonify({
            'result': result,
            'category': category,
            'feedback': feedback
        })

if __name__ == "__main__":
    os.makedirs('uploads', exist_ok=True)
    app.run(debug=True)