Update app.py

app.py

@@ -3,16 +3,15 @@ import openai
 import pinecone
 from textblob import TextBlob
 from langdetect import detect
-
+from pinecone import Pinecone
 # Initialize OpenAI API
 openai.api_key = "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"  # Replace with your OpenAI API key
 
 # Initialize Pinecone
-pinecone.init(api_key="2c47d51e-211b-4611-8808-5510e07d1f94", environment="us-east-1")
+pc = Pinecone(api_key="2c47d51e-211b-4611-8808-5510e07d1f94", environment="us-east-1")
 
 # Assume you have already created and populated an index
-index_name = "zepto"
-index = pinecone.Index(index_name)
+index = pc.Index('zepto')
 
 def get_embedding(text, model="text-embedding-ada-002"):
     response = openai.Embedding.create(input=[text], model=model)
@@ -49,26 +48,15 @@ def translate_to_english(text):
         return response.choices[0].text.strip()
     return text
 
-def is_query_relevant(query, relevant_keywords=["product", "buy", "purchase"]):
-    for keyword in relevant_keywords:
-        if keyword.lower() in query.lower():
-            return True
-    return False
-
 def process_query(query):
     query = check_and_correct_spelling(query)
     query = correct_and_complete_query(query)
     query = translate_to_english(query)
     
-    if not is_query_relevant(query):
-        return "The query is not relevant. Please enter a different query."
-    
     return query 
 
 def search_in_pinecone(query):
     processed_query = process_query(query)
-    if processed_query.startswith("The query is not relevant"):
-        return processed_query
     
     embedding = get_embedding(processed_query)
     search_results = index.query(vector=embedding, top_k=5, include_metadata=True)