Update app.py

app.py

@@ -3,32 +3,34 @@ import json
 import gradio as gr
 import openai
 from sentence_transformers import SentenceTransformer
-import pinecone
+from pinecone import Pinecone, ServerlessSpec
 
-# بارگذاری کلید OpenAI از متغیر محیطی
 openai.api_key = os.getenv("openai")
 
-# بارگذاری مدل embedding
 model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
 
-# بارگذاری داده (اختیاری)
 with open("tiyam_qa_data.json", "r", encoding="utf-8") as f:
     data = json.load(f)
 
-# دریافت مقادیر متغیرهای محیطی مربوط به Pinecone
-PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
-PINECONE_ENVIRONMENT = os.getenv("PINECONE_ENVIRONMENT", "us-west1-gcp")
-PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "tiyam-chat")
+api_key = os.getenv("PINECONE_API_KEY")
+region = os.getenv("PINECONE_ENVIRONMENT", "us-west1-gcp")
+index_name = os.getenv("PINECONE_INDEX_NAME", "tiyam-chat")
 
-# اتصال به Pinecone
-pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
+pc = Pinecone(api_key=api_key)
 
-# اگر ایندکس وجود نداشت بساز
-if PINECONE_INDEX_NAME not in pinecone.list_indexes():
-    pinecone.create_index(PINECONE_INDEX_NAME, dimension=384)  # بعد embedding مدل را وارد کنید
+existing_indexes = pc.list_indexes().names()
+if index_name not in existing_indexes:
+    pc.create_index(
+        name=index_name,
+        dimension=384,
+        metric="cosine",
+        spec=ServerlessSpec(
+            cloud="aws",
+            region=region
+        )
+    )
 
-# گرفتن ایندکس
-index = pinecone.Index(PINECONE_INDEX_NAME)
+index = pc.Index(index_name)
 
 def retrieve_answer(query, threshold=0.65, top_k=1):
     query_embedding = model.encode([query])[0]