Spaces:
Sleeping
Sleeping
comment chat system
Browse files
app.py
CHANGED
@@ -202,12 +202,11 @@ def load_embeddings():
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return embeddings
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def main():
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msgs = StreamlitChatMessageHistory(key="langchain_messages")
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print(msgs)
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if "messages" not in st.session_state:
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data = []
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# DB_FAISS_UPLOAD_PATH = "vectorstores/db_faiss"
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st.header("DOCUMENT QUESTION ANSWERING IS2")
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# directory = "data"
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@@ -254,6 +253,15 @@ def main():
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return_source_documents = True,
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memory = memory,
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chain_type_kwargs = {"prompt":qa_prompt})
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# qa_chain = ConversationalRetrievalChain(
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# retriever =db.as_retriever(search_kwargs={'k':2}),
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# question_generator=question_generator,
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@@ -264,44 +272,44 @@ def main():
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# #get_chat_history=lambda h :h
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# )
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for message in st.session_state.messages:
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if query := st.chat_input("What is up?"):
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clear_button = st.button("Start new convo")
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if clear_button :
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if __name__ == '__main__':
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return embeddings
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def main():
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# msgs = StreamlitChatMessageHistory(key="langchain_messages")
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# print(msgs)
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# if "messages" not in st.session_state:
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# st.session_state.messages = []
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# DB_FAISS_UPLOAD_PATH = "vectorstores/db_faiss"
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st.header("DOCUMENT QUESTION ANSWERING IS2")
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# directory = "data"
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return_source_documents = True,
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memory = memory,
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chain_type_kwargs = {"prompt":qa_prompt})
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query = st.text_input("ASK ABOUT THE DOCS:")
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if query:
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start = time.time()
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response = qa_chain({'query': query})
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st.write(response["result"])
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end = time.time()
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st.write("Respone time:",int(end-start),"sec")
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# qa_chain = ConversationalRetrievalChain(
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# retriever =db.as_retriever(search_kwargs={'k':2}),
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# question_generator=question_generator,
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# #get_chat_history=lambda h :h
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# )
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# for message in st.session_state.messages:
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# with st.chat_message(message["role"]):
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# st.markdown(message["content"])
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# # Accept user input
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# if query := st.chat_input("What is up?"):
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# # Display user message in chat message container
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# with st.chat_message("user"):
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# st.markdown(query)
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# # Add user message to chat history
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# st.session_state.messages.append({"role": "user", "content": query})
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# start = time.time()
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# response = qa_chain({'query': query})
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# # url_list = set([i.metadata['source'] for i in response['source_documents']])
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# #print(f"condensed quesion : {question_generator.run({'chat_history': response['chat_history'], 'question' : query})}")
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# with st.chat_message("assistant"):
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# st.markdown(response['result'])
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# end = time.time()
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# st.write("Respone time:",int(end-start),"sec")
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# print(response)
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# # Add assistant response to chat history
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# st.session_state.messages.append({"role": "assistant", "content": response['result']})
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# # with st.expander("See the related documents"):
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# # for count, url in enumerate(url_list):
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# # #url_reg = regex_source(url)
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# # st.write(str(count+1)+":", url)
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# clear_button = st.button("Start new convo")
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# if clear_button :
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# st.session_state.messages = []
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# qa_chain.memory.chat_memory.clear()
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if __name__ == '__main__':
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