Create app.py

app.py

@@ -0,0 +1,72 @@
+from flask import Flask, request, jsonify
+import pymongo
+import requests
+import json
+from transformers import MT5ForConditionalGeneration, MT5Tokenizer
+import torch
+from torch.nn.functional import softmax
+
+app = Flask(__name__)
+
+# MongoDB bağlantısı
+mongo_client = pymongo.MongoClient('mongodb://localhost:27017/')
+db = mongo_client['EgitimDatabase']
+collection = db['test']
+
+# Model ve tokenizer'ı yükleme
+model_name = "alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli"
+tokenizer = MT5Tokenizer.from_pretrained(model_name)
+model = MT5ForConditionalGeneration.from_pretrained(model_name)
+model.eval()
+
+def process_nli(premise: str, hypothesis: str):
+    """NLI formatına uygun hale getirir"""
+    return "".join(['xnli: premise: ', premise, ' hypothesis: ', hypothesis])
+
+def generate_text(title, keywords, subheadings):
+    # Prompt oluşturma
+    prompt = f"Başlık: {title}\nAnahtar Kelimeler: {keywords}\nAlt Başlıklar: {subheadings}\n\nBu bilgilerle bir metin oluşturun:"
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    # Modelden çıktı alma
+    outputs = model.generate(**inputs, output_scores=True, return_dict_in_generate=True, num_beams=5)
+    generated_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
+    
+    return generated_text
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    title = data.get('title')
+    keywords = data.get('keywords')
+    subheadings = data.get('subheadings')
+    
+    # MongoDB'den ilgili verileri çekme
+    query = {
+        'title': title,
+        'keywords': {'$in': keywords.split(',')},
+        'subheadings': {'$in': subheadings.split(',')}
+    }
+    documents = list(collection.find(query))
+
+    if not documents:
+        return jsonify({'error': 'No documents found matching the query'}), 404
+
+    # Verilerle metin oluşturma
+    generated_texts = []
+    for doc in documents:
+        generated_text = generate_text(doc['title'], doc['keywords'], doc['subheadings'])
+        generated_texts.append(generated_text)
+
+    # Sonuçları döndürme
+    response = {
+        'title': title,
+        'keywords': keywords,
+        'subheadings': subheadings,
+        'generated_texts': generated_texts
+    }
+    
+    return jsonify(response)
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=8080)