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Commit 8bce82eb by Hyder
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delete-pages

parent abfef4d5
Showing with 0 additions and 207 deletions
Final-Project/Pages/1_Basic_Chatbot.py deleted 100644 → 0
import utils
import streamlit as st
from streaming import StreamHandler
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
st.set_page_config(page_title="Chatbot", page_icon="💬")
st.header('Basic Chatbot')
st.write('Allows users to interact with the OpenAI LLMs')
class Basic:
def __init__(self):
utils.configure_openai_api_key()
self.openai_model = "gpt-3.5-turbo"
def setup_chain(self):
llm = OpenAI(model_name=self.openai_model,
temperature=0, streaming=True)
chain = ConversationChain(llm=llm, verbose=True)
return chain
@utils.enable_chat_history
def main(self):
chain = self.setup_chain()
user_query = st.chat_input(placeholder="Ask me anything!")
if user_query:
utils.display_msg(user_query, 'user')
with st.chat_message("assistant"):
try:
st_cb = StreamHandler(st.empty())
response = chain.run(user_query, callbacks=[st_cb])
st.session_state.messages.append(
{"role": "assistant", "content": response})
except Exception as e:
print(e)
if __name__ == "__main__":
obj = Basic()
obj.main()
Final-Project/Pages/2_Chatbot_Agent.py deleted 100644 → 0
import utils
import streamlit as st
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.tools import DuckDuckGoSearchRun
from langchain.agents import initialize_agent, Tool
from langchain.callbacks import StreamlitCallbackHandler
st.set_page_config(page_title="ChatWeb", page_icon="🌐")
st.header('Chatbot with Web Browser Access')
st.write('Equipped with internet agent, enables users to ask questions about recent events')
class ChatbotTools:
def __init__(self):
utils.configure_openai_api_key()
self.openai_model = "gpt-3.5-turbo"
def setup_agent(self):
# Define tool
ddg_search = DuckDuckGoSearchRun()
tools = [
Tool(
name="DuckDuckGoSearch",
func=ddg_search.run,
description="Useful for when you need to answer questions about current events. You should ask targeted questions",
)
]
# Setup LLM and Agent
llm = ChatOpenAI(model_name=self.openai_model, streaming=True)
agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True,
verbose=True
)
return agent
@utils.enable_chat_history
def main(self):
agent = self.setup_agent()
user_query = st.chat_input(placeholder="Ask me anything!")
if user_query:
utils.display_msg(user_query, 'user')
with st.chat_message("assistant"):
try:
st_cb = StreamlitCallbackHandler(st.container())
response = agent.run(user_query, callbacks=[st_cb])
st.session_state.messages.append(
{"role": "assistant", "content": response})
st.write(response)
except Exception as e:
print(e)
if __name__ == "__main__":
obj = ChatbotTools()
obj.main()
Final-Project/Pages/3_Chat_with_your_Data.py deleted 100644 → 0
import os
import utils
import streamlit as st
from streaming import StreamHandler
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyPDFLoader
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
st.set_page_config(page_title="ChatPDF", page_icon="📄")
st.header('Chat with your Documents')
st.write('Has access to custom documents and can respond to user queries by referring to the content within those documents')
class CustomDataChatbot:
def __init__(self):
utils.configure_openai_api_key()
self.openai_model = "gpt-3.5-turbo"
def save_file(self, file):
folder = 'tmp'
if not os.path.exists(folder):
os.makedirs(folder)
file_path = f'./{folder}/{file.name}'
with open(file_path, 'wb') as f:
f.write(file.getvalue())
return file_path
@st.spinner('Analyzing documents..')
def setup_qa_chain(self, uploaded_files):
# Load documents
docs = []
for file in uploaded_files:
file_path = self.save_file(file)
loader = PyPDFLoader(file_path)
docs.extend(loader.load())
# Split documents
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1500,
chunk_overlap=200
)
splits = text_splitter.split_documents(docs)
# Create embeddings and store in vectordb
embeddings = OpenAIEmbeddings()
vectordb = FAISS.from_documents(splits, embeddings)
# Define retriever
retriever = vectordb.as_retriever()
# Setup memory for contextual conversation
memory = ConversationBufferMemory(
memory_key='chat_history',
return_messages=True
)
# Setup LLM and QA chain
llm = ChatOpenAI(model_name=self.openai_model,
temperature=0, streaming=True)
qa_chain = ConversationalRetrievalChain.from_llm(
llm, retriever=retriever, memory=memory, verbose=True)
return qa_chain
@utils.enable_chat_history
def main(self):
# User Inputs
uploaded_files = st.sidebar.file_uploader(label='Upload PDF files', type=[
'pdf'], accept_multiple_files=True)
if not uploaded_files:
st.error("Please upload PDF documents to continue!")
st.stop()
user_query = st.chat_input(placeholder="Ask me anything!")
if uploaded_files and user_query:
qa_chain = self.setup_qa_chain(uploaded_files)
utils.display_msg(user_query, 'user')
with st.chat_message("assistant"):
try:
st_cb = StreamHandler(st.empty())
response = qa_chain.run(user_query, callbacks=[st_cb])
st.session_state.messages.append(
{"role": "assistant", "content": response})
except Exception as e:
print(e)
if __name__ == "__main__":
obj = CustomDataChatbot()
obj.main()
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