Update app.py

app.py

@@ -724,6 +724,8 @@ with final_recs:
     
     
 with chat:
+    st.title("Chat with your AI Stock Advisor")
+    
     from langchain.indexes import VectorstoreIndexCreator
     from langchain.chains import RetrievalQA
     from langchain.llms import OpenAI
@@ -734,32 +736,7 @@ with chat:
     from langchain_community.vectorstores import FAISS
     from langchain_community.llms import HuggingFaceHub
     from langchain_core.prompts import PromptTemplate
-    
-    print("Libraries imported")
-    
-    loader = CSVLoader("Events_SameDay.csv",encoding='iso-8859-1')
-    print("CSV imported")
-    
-    # Create an index using the loaded documents
-    try:
-        index_creator = VectorstoreIndexCreator()
-        print("index_creator Created")
-    
-        docsearch = index_creator.from_loaders([loader])
-        print("DB Created")
-    except Exception as e:
-        st.write(f'Error Occured: {e}')
-    repo_id = "mistralai/Mistral-7B-Instruct-v0.1"
-    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_new_tokens": 1024})
-    print("MistralAi Loaded")
-
-    prompt_temp="As a financial expert for stock market, if user is asking for trading recommendation, then you need to generate trading signal recommendations utilizing insights from two approaches. One is the technical indicators signals EMA55, RSI, EMA9, and MACD (all ranging from -3 to 3, where –3 is strong sell, -2 is moderate sell, -1 is weak sell, 0 is for hold, 1 is for weak buy, 2 is for moderate buy and 3 is for strong buy) from the respective signal while other insight is from news impacts (either positive or negative between -5 to 5). Provide your recommendation with balanced approach if news impact is too much positive or negative, technical indicator can be ignored and buy or sell suggestion based on news impact can be given. On the contrary, if technical indicators are opposite to news impact, a hold position is a reasonable suggestion. If technical indicators are all positive along news impact, strong buy signal can be generated. If technical indicators and news impact are all negative a strong sell signal can be generated. If news impact is too low, then generate recommendation based on technical indicator specially with more weightage to ema 55 in all the technical indicators. Your response should cover all technical aspects including the analysis of technical indicators as well as covering the news sentiments. Also cover some logical financial rational as well as the explanations."
-    # Create a question-answering chain using the index
-    chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.vectorstore.as_retriever(), input_key="question")
-    print("Chain Created")
-    
-    
-    
+
     def get_answer(text):
       text = response['result']
       helpful_answer_index = text.find('Helpful Answer:')
@@ -776,39 +753,61 @@ with chat:
         for word in response.split():
             yield word + " "
             time.sleep(0.05)
-
-
-    st.title("Chat with your AI Stock Advisor")
-
-    # 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}")
-        try:
-            response = chain({"question": prompt})
-            print("Answer generated")
-            result = get_answer(response['result'])
-            print("helpful answer extracted")
+    print("Libraries imported")
+    
+    loader = CSVLoader("Events_SameDay.csv",encoding='iso-8859-1')
+    print("CSV imported")
+    
+    # Create an index using the loaded documents
+    try:
+        index_creator = VectorstoreIndexCreator()
+        print("index_creator Created")
     
-            # 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})
-        except Exception as e:
-            st.write(f'Error: {e}')
+        docsearch = index_creator.from_loaders([loader])
+        print("DB Created")
+    
+        repo_id = "mistralai/Mistral-7B-Instruct-v0.1"
+        llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_new_tokens": 1024})
+        print("MistralAi Loaded")
+    
+        prompt_temp="As a financial expert for stock market, if user is asking for trading recommendation, then you need to generate trading signal recommendations utilizing insights from two approaches. One is the technical indicators signals EMA55, RSI, EMA9, and MACD (all ranging from -3 to 3, where –3 is strong sell, -2 is moderate sell, -1 is weak sell, 0 is for hold, 1 is for weak buy, 2 is for moderate buy and 3 is for strong buy) from the respective signal while other insight is from news impacts (either positive or negative between -5 to 5). Provide your recommendation with balanced approach if news impact is too much positive or negative, technical indicator can be ignored and buy or sell suggestion based on news impact can be given. On the contrary, if technical indicators are opposite to news impact, a hold position is a reasonable suggestion. If technical indicators are all positive along news impact, strong buy signal can be generated. If technical indicators and news impact are all negative a strong sell signal can be generated. If news impact is too low, then generate recommendation based on technical indicator specially with more weightage to ema 55 in all the technical indicators. Your response should cover all technical aspects including the analysis of technical indicators as well as covering the news sentiments. Also cover some logical financial rational as well as the explanations."
+        # Create a question-answering chain using the index
+        chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.vectorstore.as_retriever(), input_key="question")
+        print("Chain Created")
+        
+        # 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})
+    except Exception as e:
+        if 'You exceeded your current quota' in e:
+            st.markdown(':red[Insufficient Quota]')
+            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}")