check?

app.py

@@ -2,80 +2,69 @@ import streamlit as st
 import os
 from langchain_community.vectorstores import Chroma
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain_together import Together
-from langchain.prompts import ChatPromptTemplate, PromptTemplate
-from langchain.schema import format_document
-from typing import List
-from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import Together
+from langchain import hub
+from operator import itemgetter
+from langchain.schema.runnable import RunnableParallel
+from langchain.chains import LLMChain
+from langchain.chains import RetrievalQA
+from langchain.schema.output_parser import StrOutputParser
 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"
-encode_kwargs = {'normalize_embeddings': True}
-embedding_function = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
+    encode_kwargs=encode_kwargs
+)
+
+
+
+
+
 
-# Load the LLM
-llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.2, max_tokens=19096, top_k=10, together_api_key=os.environ['pilotikval'])
 
+
+# Load the LLM
+llm = Together(
+    model="mistralai/Mixtral-8x22B-Instruct-v0.1",
 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"):
+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)
 
+
+
 chistory = []
 
-def store_chat_history(role: str, content: str):
+    # Append the new message to the chat history
     chistory.append({"role": role, "content": content})
 
-def render_message_with_copy_button(role: str, content: str, key: str):
-    html_code = f"""
-    <div class="message" style="position: relative; padding-right: 40px;">
-        <div class="message-content">{content}</div>
-        <button onclick="copyToClipboard('{key}')" style="position: absolute; right: 0; top: 0; background-color: transparent; border: none; cursor: pointer;">
-            <img src="https://img.icons8.com/material-outlined/24/grey/copy.png" alt="Copy">
-        </button>
-    </div>
-    <textarea id="{key}" style="display:none;">{content}</textarea>
-    <script>
-        function copyToClipboard(key) {{
-            var copyText = document.getElementById(key);
-            copyText.style.display = "block";
-            copyText.select();
-            document.execCommand("copy");
-            copyText.style.display = "none";
-            alert("Copied to clipboard");
-        }}
-    </script>
-    """
-    st.write(html_code, unsafe_allow_html=True)
-
-def get_streaming_response(user_query, chat_history):
-    template = """
-    You are a knowledgeable assistant. Provide a detailed and thorough answer to the question based on the following context:
 
-    Chat history: {chat_history}
+# Define the Streamlit app
+def app():
 
-    User question: {user_question}
-    """
-    prompt = ChatPromptTemplate.from_template(template)
-    
-    inputs = {
-        "chat_history": chat_history,
-        "user_question": user_query
-    }
 
-    chain = prompt | llm
-    return chain.stream(inputs)
 
-def app():
     with st.sidebar:
+
         st.title("dochatter")
-        option = st.selectbox('Which retriever would you like to use?', ('General Medicine', 'RespiratoryFishman', 'RespiratoryMurray', 'MedMRCP2', 'OldMedicine'))
+        # Create a dropdown selection box
+        option = st.selectbox(
+        )
+        # 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")
@@ -83,47 +72,176 @@ def app():
         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})
         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})
         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})
+
+
         else:
             persist_directory = "./mrcpchromadb/"
             vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="mrcppassmednotes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    # Session State
+
     if "messages" not in st.session_state.keys():
         st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
 
-    st.header("Ask Away!")
-    for i, message in enumerate(st.session_state.messages):
+
+
+
+
+
+
+
+
+
+
+
+    _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}
+
+    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(),
+
+
+
+    )
+    _context = {
+        "context": itemgetter("standalone_question") | retriever | _combine_documents,
+    }
+    conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | llm
+
+    st.header("Hello Doctor!")
+
+
+
+
+
+
+
+
+
+
+
+    for message in st.session_state.messages:
         with st.chat_message(message["role"]):
-            render_message_with_copy_button(message["role"], message["content"], key=f"message-{i}")
+            st.write(message["content"])
             store_chat_history(message["role"], message["content"])
 
-    user_query = st.chat_input("Say something")
-    if user_query:
-        st.session_state.messages.append({"role": "user", "content": user_query})
+
+
+
+
+
+    prompts2 = st.chat_input("Say something")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    if prompts2:
+        st.session_state.messages.append({"role": "user", "content": prompts2})
         with st.chat_message("user"):
-            st.write(user_query)
+            st.write(prompts2)
+
+
 
+    if st.session_state.messages[-1]["role"] != "assistant":
         with st.chat_message("assistant"):
             with st.spinner("Thinking..."):
-                chat_history = "\n".join([f"{msg['role']}: {msg['content']}" for msg in chistory])
-                try:
-                    response_generator = get_streaming_response(user_query, chat_history)
-                    response_text = ""
-                    for response_part in response_generator:
-                        response_text += response_part
-                        st.write(response_text)
-                    st.session_state.messages.append({"role": "assistant", "content": response_text})
-                except Exception as e:
-                    st.error(f"An error occurred: {e}")
+                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
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
+
+
 
 if __name__ == '__main__':
-    app()
+    app()