Update app.py

app.py

@@ -74,21 +74,18 @@ embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2"
 ##########################################
 
 
-# Define the LLM - We shall use ChatGroq of Groq Platform and LLama70B
-# This llm definition is redundant as now models will be chosen by user
-# llm = ChatGroq(
-#             api_key="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",
-#             model="llama3-70b-8192",
-#             # model = 'gemma-7b-it',
-#             temperature = 0
-#             # model = 'mixtral-8x7B-32768'
-#         )
+
 
 
 
 # Get the data from NSE as pandas dataframe
 # Function to get dataframe from NSE website
 # Data from two pages: NSE Announcements and NSE corporate actions are fetched and hence two dataframes
+
+groq_api_key = os.getenv('groq')
+serp_api_key = os.getenv('serp')
+
+
 def get_pd(d):
 
   # Get the current date
@@ -234,7 +231,8 @@ def get_ca(llm,stock):
 search=DuckDuckGoSearchRun()
 
 
-os.environ['SERPAPI_API_KEY']='2e3312eee2ce5e3d9d97989c87999ae121765134a5f713b91146ba35af405dfc'
+os.environ['SERPAPI_API_KEY']= serp_api_key
+
 from langchain_community.utilities import SerpAPIWrapper
 search = SerpAPIWrapper()
 
@@ -793,7 +791,7 @@ def combined_function1(model,stock):
 
 def get_model(model_name):
   llm = ChatGroq(
-            api_key="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",
+            api_key=groq_api_key,
             model=model_name,
             max_tokens = 8192,
             # model = 'gemma-7b-it',