Create handler.py

handler.py

@@ -0,0 +1,67 @@
+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()