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import openai
import random
import time
import gradio as gr
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import DeepLake
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.memory import ConversationBufferMemory
from langchain.llms import OpenAI
def set_api_key(key):
os.environ["OPENAI_API_KEY"] = key
return f"Your API Key has been set to: {key}"
def reset_api_key():
os.environ["OPENAI_API_KEY"] = ""
return "Your API Key has been reset"
def get_api_key():
api_key = os.getenv("OPENAI_API_KEY")
return api_key
def set_model(model):
os.environ["OPENAI_MODEL"] = model
return f"{model} selected"
def get_model():
model = os.getenv("OPENAI_MODEL")
return model
def upload_file(files):
file_paths = [file.name for file in files]
return file_paths
def create_vectorstore(files):
pdf_dir = files.name
pdf_loader = PyPDFDirectoryLoader(pdf_dir)
pdf_docs = pdf_loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(pdf_docs)
embeddings = OpenAIEmbeddings()
db = DeepLake.from_documents(texts, dataset_path="./documentation_db", embedding=embeddings, overwrite=True)
return "Vectorstore Successfully Created"
def respond(message, chat_history):
# Get embeddings
embeddings = OpenAIEmbeddings()
#Connect to existing vectorstore
db = DeepLake(dataset_path="./documentation_db", embedding_function=embeddings, read_only=True)
#Set retriever settings
retriever = db.as_retriever(search_kwargs={"distance_metric":'cos',
"fetch_k":10,
"maximal_marginal_relevance":True,
"k":10})
if len(chat_history) != 0:
chat_history = [(chat_history[0][0], chat_history[0][1])]
model = get_model()
# Create ChatOpenAI and ConversationalRetrievalChain
model = ChatOpenAI(model=model)
qa = ConversationalRetrievalChain.from_llm(model, retriever)
bot_message = qa({"question": message, "chat_history": chat_history})
chat_history = [(message, bot_message["answer"])]
time.sleep(1)
return "", chat_history
with gr.Blocks() as demo:
#create chat history
chat_history = []
with gr.Row():
#create textbox for API input
api_input = gr.Textbox(label = "API Key",
placeholder = "Please provide your OpenAI API key here.")
#create textbox to validate API
api_key_status = gr.Textbox(label = "API Key Status",
placeholder = "Your API Key has not be set yet. Please enter your key.",
interactive = False)
#create button to submit API key
api_submit_button = gr.Button("Submit")
#set api_submit_button functionality
api_submit_button.click(set_api_key, inputs=api_input, outputs=api_key_status)
#create button to reset API key
api_reset_button = gr.Button("Clear API Key from session")
#set api_reset_button functionality
api_reset_button.click(reset_api_key, outputs=api_key_status)
with gr.Row():
with gr.Column():
#create dropdown to select model (gpt-3.5-turbo or gpt4)
model_selection = gr.Dropdown(
["gpt-3.5-turbo", "gpt-4"], label="Model Selection", info="Please ensure you provide the API Key that corresponds to the Model you select!"
)
#create button to submit model selection
model_submit_button = gr.Button("Submit Model Selection")
model_status = gr.Textbox(label = "Selected Model", interactive = False, lines=4)
#set model_submit_button functionality
model_submit_button.click(set_model, inputs=model_selection, outputs=model_status)
file_output = gr.File(label = "Uploaded files - Please note these files are persistent and will not be automatically deleted")
upload_button = gr.UploadButton("Click to Upload a PDF File", file_types=["pdf"], file_count="multiple")
upload_button.upload(upload_file, upload_button, file_output)
create_vectorstore_button = gr.Button("Click to create the vectorstore for your uploaded documents")
db_output = gr.Textbox(label = "Vectorstore Status")
create_vectorstore_button.click(create_vectorstore, inputs=file_output, outputs = db_output)
chatbot = gr.Chatbot(label="ChatGPT Powered Grant Writing Assistant")
msg = gr.Textbox(label="User Prompt", placeholder="Your Query Here")
clear = gr.Button("Clear")
msg.submit(respond, inputs = [msg, chatbot], outputs = [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch() |