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Runtime error
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import requests
import streamlit as st
from layouts.mainlayout import mainlayout
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from components.file_streaming import *
from components.display import *
@mainlayout
def display():
with st.expander("What happens when I upload a PDF? 📑", expanded=True):
st.info(
"""
- The PDF is uploaded to the backend server. ⚙️
- The PDF is converted into small chunks for faster processing. 🚀
- The chunks are broken down into tokens. A token is a single word or a group of words. 📝
- The tokens are converted into embedding vectors. 📊
- The embedding vectors are stored in a vector store. 🗄️
""",
icon="ℹ️",
)
st.divider()
display()
BASE_URL = "http://127.0.0.1:8000"
uploaded_files = st.sidebar.file_uploader(label="Upload PDF files", type=["pdf"])
if not uploaded_files:
st.info("Please upload PDF documents to continue.")
st.stop()
upload_data(uploaded_files)
if "messages" not in st.session_state.keys():
st.session_state.messages = [
{
"role": "assistant",
"content": "What's troubling you? Ask me a question right away!",
}
]
# Display or clear chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
def clear_chat_history():
st.session_state.messages = [
{
"role": "assistant",
"content": "What's troubling you? Ask me a question right away!",
}
]
st.sidebar.button("Clear Chat History", on_click=clear_chat_history)
def generate_mistral_response(question: str):
for dict_message in st.session_state.messages:
if dict_message["role"] == "user":
question = dict_message["content"]
try:
response = requests.post(
f"{BASE_URL}/api/inference",
json={"promptMessage": question}).json()
if response["status"]=="error":
st.error("Please refresh the page and try uploading the file again.")
st.stop()
answer = response["result"]["answer"]
except Exception as e:
if response.json()=='exception.ModelDeployingException()':
st.error("Model is deploying in the backend servers. Please try again after some time")
st.stop()
with st.expander("Source documents 🧐", expanded=True):
source_documents = requests.post(
f"{BASE_URL}/api/inference",
json={"promptMessage": question}).json()["result"]["source_documents"]
display_source_document(source_documents)
return answer
# User-provided prompt
if prompt := st.chat_input(
disabled=not st.session_state.messages[-1]["role"] == "assistant",
placeholder="Hello, please ask me a question! 🤖"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
# ask question
st.write(st.session_state)
# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response = generate_mistral_response(prompt)
placeholder = st.empty()
full_response = ""
for item in response:
full_response += item
placeholder.markdown(full_response)
placeholder.markdown(full_response)
message = {"role": "assistant", "content": full_response}
st.session_state.messages.append(message)
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