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import json
from pymongo import MongoClient
from sentence_transformers import SentenceTransformer, util
import boto3
import os
#----------------------------------------------------------------------------
def lambda_handler(event, context):
# S3'tan Python versiyonlarını alma
python_versions = _known_python_versions()
# Kullanıcıdan gelen inputu işleme
user_input = event.get("input")
if not user_input:
return {
"statusCode": 400,
"body": json.dumps("Input data is required")
}
save_user_input_to_mongodb({"input": user_input})
refenece_data_mongodb({})
# Similarity hesaplama
similarity_score = calculate_similarity(user_input, "Referans metin")
# Sonuçları döndürme
return {
"statusCode": 200,
"body": json.dumps({
"python_versions": python_versions,
"similarity_score": similarity_score
})
}
#-------------------------------------------------------------------------------
#s3 keywords'ü oluşturma gerekli
def _known_python_versions():
"""Get current list from S3."""
try:
s3 = boto3.resource("s3")
obj = s3.Object(os.environ["S3_BUCKET"], "python/versions.json")
content = obj.get()["Body"].read().decode("utf-8")
except botocore.exceptions.ClientError:
print("Key not found, using empty list")
content = '{"python_versions":[]}'
return json.loads(content)
def save_user_input_to_mongodb(input_data):
client = MongoClient(os.environ["MONGODB_URI"])
db = client["EgitimDatabase"]
collection = db["input"]
collection.insert_one(input_data)
client.close()
def refenece_data_mongodb(reference_data):
client = MongoClient(os.environ["MONGODB_URI"])
db = client["EgitimDatabase"]
collection = db["test"]
collection.insert_one(reference_data)
client.close()
#--------------------------------------------------------------------------------
def calculate_similarity(text1, text2):
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
embedding1 = model.encode(text1, convert_to_tensor=True)
embedding2 = model.encode(text2, convert_to_tensor=True)
similarity_score = util.pytorch_cos_sim(embedding1, embedding2)
return similarity_score.item()
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