Update app.py

app.py

@@ -95,7 +95,8 @@ date_limit = pd.to_datetime(data['Date'].max())   # Set date limit for end date
 current_date = pd.to_datetime('2021-08-12')
 
 # Create Tabs
-market_analysis, news_analysis, final_recs, chat = st.tabs(["Market Analysis", "News Analysis", "GenAI Recommendations","Ask AI Advisor"])
+market_analysis, news_analysis, final_recs, chat, chat2 = st.tabs(["Market Analysis", "News Analysis", "GenAI Recommendations",
+                                                                   "Ask AI Advisor", "Ask AI Advisor 2"])
 
 with market_analysis:
     st.header("Market Analysis", help = "This module provides market analysis for the following day based on the current date.")
@@ -863,4 +864,78 @@ with chat:
             st.write(f'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.')
 
         else:
-            st.markdown(f"An Error Occured: \n {e}")
+            st.markdown(f"An Error Occured: \n {e}")
+
+with chat2:
+    from langchain_community.document_loaders import CSVLoader
+    from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+    from langchain_community.vectorstores import FAISS
+    from langchain_community.llms import HuggingFaceHub
+    from langchain_core.prompts import PromptTemplate
+    from langchain.chains import RetrievalQA
+
+    st.header("Chat with AI Stock Advisor")
+
+    loader = CSVLoader("Events_SameDay.csv",encoding='iso-8859-1')
+    embeddings = HuggingFaceInstructEmbeddings()
+    persist_directory = 'FAISS_VectorStore'
+    db2 = FAISS.load_local(persist_directory, embeddings, allow_dangerous_deserialization=True)
+    repo_id = "mistralai/Mistral-7B-Instruct-v0.1"
+    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_new_tokens": 1024})
+    
+    system_prompt = """Your task is to extract relevant information from the oil and gas sector data."""
+    B_INST, E_INST = "<s>[INST] ", " [/INST]"
+    template = (
+                    B_INST
+                    + system_prompt
+                    + """
+                Context: {context}
+                User: {question}
+                """
+                    + E_INST +
+                "\nHelpful Answer: \n"
+                )
+    sys_prompt = PromptTemplate(input_variables=["context", "question"], template=template)
+    
+    chain = RetrievalQA.from_chain_type(
+    llm=llm, 
+    chain_type="stuff", 
+    retriever=db2.as_retriever(), 
+    input_key="question",
+    chain_type_kwargs={"prompt": sys_prompt})
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Accept user input
+    if prompt := st.chat_input("Enter your query here."):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        # Display user message in chat message container
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # question = query
+        print(f"User Question: {prompt}")
+        response = chain({"question": prompt})
+        print("Answer generated")
+        result = get_answer(response['result'])
+        print("helpful answer extracted")
+
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            response = st.write_stream(response_generator(result))
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})
+    
+
+    
+
+    
+