Update app.py

app.py

@@ -12,6 +12,7 @@ from htmlTemplates import css, bot_template, user_template
 from langchain.llms import HuggingFaceHub
 from deep_translator import GoogleTranslator
 import pandas as pd
+from langchain_groq import ChatGroq
 # set this key as an environment variable
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['Key2']
 ###########################################################################################
@@ -52,14 +53,14 @@ def get_vectorstore(text_chunks : list) -> FAISS:
 
 def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain:
     # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
-    llm = HuggingFaceHub(
+    #llm = HuggingFaceHub(
         #repo_id="mistralai/Mistral-7B-Instruct-v0.2",
         #repo_id="cognitivecomputations/Llama-3-70B-Gradient-1048k-adapter",
         #repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
-        repo_id="mostafaamiri/persian-llama-7b-GGUF-Q4",
-        model_kwargs={"temperature": 0.1, "max_length": 2048},
-    )
-
+     #   repo_id="mostafaamiri/persian-llama-7b-GGUF-Q4",
+     #   model_kwargs={"temperature": 0.1, "max_length": 2048},
+    #)
+    llm = ChatGroq(temperature=0.5, model_name="llama3-8b-8192", groq_api_key='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')
     memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
     conversation_chain = ConversationalRetrievalChain.from_llm(
         llm=llm, retriever=vectorstore.as_retriever(),memory=memory