chatgptchanges

app.py

@@ -1,8 +1,8 @@
 import streamlit as st
 import os
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceBgeEmbeddings
-from langchain.llms import Together
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_community.llms import Together
 from langchain import hub
 from operator import itemgetter
 from langchain.schema.runnable import RunnableParallel
@@ -11,13 +11,13 @@ from typing import List, Tuple
 from langchain.chains import LLMChain
 from langchain.chains import RetrievalQA
 from langchain.schema.output_parser import StrOutputParser
-from langchain.memory import StreamlitChatMessageHistory
+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationSummaryMemory
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
 from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
-
+import time
 
 # Load the embedding function
 model_name = "BAAI/bge-base-en"
@@ -28,13 +28,6 @@ embedding_function = HuggingFaceBgeEmbeddings(
     encode_kwargs=encode_kwargs
 )
 
-# Load the ChromaDB vector store
-# persist_directory="./mrcpchromadb/"
-# vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="mrcppassmednotes")
-
-
-
-
 # Load the LLM
 llm = Together(
     model="mistralai/Mixtral-8x22B-Instruct-v0.1",
@@ -56,17 +49,13 @@ llmc = Together(
 msgs = StreamlitChatMessageHistory(key="langchain_messages")
 memory = ConversationBufferMemory(chat_memory=msgs)
 
-
-
 DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
 
 def _combine_documents(
         docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
     ):
-        doc_strings = [format_document(doc, document_prompt) for doc in docs]
-        return document_separator.join(doc_strings)
-
-
+    doc_strings = [format_document(doc, document_prompt) for doc in docs]
+    return document_separator.join(doc_strings)
 
 chistory = []
 
@@ -74,14 +63,9 @@ def store_chat_history(role: str, content: str):
     # Append the new message to the chat history
     chistory.append({"role": role, "content": content})
 
-
 # Define the Streamlit app
 def app():
-
-
-
     with st.sidebar:
-
         st.title("dochatter")
         # Create a dropdown selection box
         option = st.selectbox(
@@ -90,66 +74,31 @@ def app():
         )
         # Depending on the selected option, choose the appropriate retriever
         if option == 'RespiratoryFishman':
-            persist_directory="./respfishmandbcud/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="fishmannotescud")
+            persist_directory = "./respfishmandbcud/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="fishmannotescud")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever # replace with your actual retriever
-        
-        if option == 'RespiratoryMurray':
-            persist_directory="./respmurray/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="respmurraynotes")
+        elif option == 'RespiratoryMurray':
+            persist_directory = "./respmurray/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="respmurraynotes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever
-
-        if option == 'MedMRCP2':
-            persist_directory="./medmrcp2store/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="medmrcp2notes")
+        elif option == 'MedMRCP2':
+            persist_directory = "./medmrcp2store/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="medmrcp2notes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever
-
-        if option == 'General Medicine':
-            persist_directory="./oxfordmedbookdir/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="oxfordmed")
+        elif option == 'General Medicine':
+            persist_directory = "./oxfordmedbookdir/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="oxfordmed")
             retriever = vectordb.as_retriever(search_kwargs={"k": 7})
-            retriever = retriever
-            
         else:
-            persist_directory="./mrcpchromadb/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="mrcppassmednotes")
+            persist_directory = "./mrcpchromadb/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="mrcppassmednotes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever # replace with your actual retriever
-            retriever = retriever  # replace with your actual retriever
-
-        #template = """You are an AI chatbot having a conversation with a human. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
-        #{context}
-        #{history}
-        #Human: {human_input}
-        #AI: """
-        #prompt = PromptTemplate(input_variables=["history", "question"], template=template)
-        #template = st.text_area("Template", value=template, height=180)
-        #prompt2 = ChatPromptTemplate.from_template(template)
-
-    
-    
 
     # Session State
-    # Store LLM generated responses
     if "messages" not in st.session_state.keys():
         st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
-    
-
-    
-
-    
-
-
-    
-
-
-    ## Retry lets go
 
     _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question which contains the themes of the conversation. Do not write the question. Do not write the answer.
-
     Chat History:
     {chat_history}
     Follow Up Input: {question}
@@ -158,19 +107,14 @@ def app():
 
     template = """You are helping a doctor. Answer with what you know from the context provided. Please be as detailed and thorough. Answer the question based on the following context:
     {context}
-
     Question: {question}
     """
     ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
 
-
     _inputs = RunnableParallel(
         standalone_question=RunnablePassthrough.assign(
             chat_history=lambda x: chistory
-        )
-        | CONDENSE_QUESTION_PROMPT
-        | llmc
-        | StrOutputParser(),
+        ) | CONDENSE_QUESTION_PROMPT | llmc | StrOutputParser(),
     )
     _context = {
         "context": itemgetter("standalone_question") | retriever | _combine_documents,
@@ -178,97 +122,37 @@ def app():
     }
     conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | llm
 
-
-    
-
-    
-
-
-    
-    
-   
-    
-    st.header("Ask Away!")
-    # Display the messages
+    st.header("Hello Doctor!")
     for message in st.session_state.messages:
         with st.chat_message(message["role"]):
             st.write(message["content"])
             store_chat_history(message["role"], message["content"])
 
-    # prompt = hub.pull("rlm/rag-prompt")
-    
-    
-    
-    
     prompts2 = st.chat_input("Say something")
 
-    # Implement using different book sources, if statements
-    
-
-    
-
-    
-    
-
-    
-
-    
-
-    
-    
-    
     if prompts2:
         st.session_state.messages.append({"role": "user", "content": prompts2})
         with st.chat_message("user"):
             st.write(prompts2)
-            
-    
 
     if st.session_state.messages[-1]["role"] != "assistant":
         with st.chat_message("assistant"):
             with st.spinner("Thinking..."):
-                response = conversational_qa_chain.invoke(
-                {
-                    "question": prompts2,
-                    "chat_history": chistory,
-                }
-            )
-                st.write(response)
-        message = {"role": "assistant", "content": response}
-        st.session_state.messages.append(message)
-
-
-
-    
-        
-        
-        
-        
-
-
-   
-
-    # Create a button to submit the question
-    
-
-
-    
-
-        
-        
-
-        
-        
-        
-
-        
-        
-
-        
-
-    
-# Initialize history
-history = []
+                for _ in range(3):  # Retry up to 3 times
+                    try:
+                        response = conversational_qa_chain.invoke(
+                            {
+                                "question": prompts2,
+                                "chat_history": chistory,
+                            }
+                        )
+                        st.write(response)
+                        message = {"role": "assistant", "content": response}
+                        st.session_state.messages.append(message)
+                        break
+                    except Exception as e:
+                        st.error(f"An error occurred: {e}")
+                        time.sleep(2)  # Wait 2 seconds before retrying
 
 if __name__ == '__main__':
-    app()
+    app()