LLM-Chatbot / app.py
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import os
import gradio as gr
from langchain.document_loaders import PDFMinerLoader,CSVLoader ,UnstructuredWordDocumentLoader,TextLoader,OnlinePDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import FAISS
from langchain import HuggingFaceHub
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
DEVICE = 'cpu'
FILE_EXT = ['pdf','text','csv','word','wav']
def loading_file():
return "Loading..."
def get_openai_chat_model(API_key):
try:
from langchain.llms import OpenAI
except ImportError as err:
raise "{}, unable to load openAI. Please install openai and add OPENAIAPI_KEY"
os.environ["OPENAI_API_KEY"] = API_key
llm = OpenAI()
return llm
def process_documents(documents,data_chunk=1000,chunk_overlap=50):
text_splitter = CharacterTextSplitter(chunk_size=data_chunk, chunk_overlap=chunk_overlap,separator='\n')
texts = text_splitter.split_documents(documents)
return texts
def get_hugging_face_model(model_id,API_key,temperature=0.1):
chat_llm = HuggingFaceHub(huggingfacehub_api_token=API_key,
repo_id=model_id,
model_kwargs={"temperature": temperature, "max_new_tokens": 2048})
return chat_llm
def chat_application(llm_service,key):
if llm_service == 'HuggingFace':
llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',API_key=key)
else:
llm = get_openai_chat_model(API_key=key)
return llm
def summarize_contents():
question = "Generate a summary of the contents. Do not return the response in json format"
return qa.run(question)
def document_loader(file_path,api_key,doc_type='pdf',llm='Huggingface'):
document = None
if doc_type == 'pdf':
document = process_pdf_document(document_file=file_path)
elif doc_type == 'text':
document = process_text_document(document_file=file_path)
elif doc_type == 'csv':
document = process_csv_document(document_file=file_path)
elif doc_type == 'word':
document = process_word_document(document_file=file_path)
if document:
embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-base',model_kwargs={"device": DEVICE})
texts = process_documents(documents=document)
vector_db = FAISS.from_documents(documents=texts, embedding= embedding_model)
global qa
qa = RetrievalQA.from_chain_type(llm=chat_application(llm_service=llm,key=api_key),
chain_type='stuff',
retriever=vector_db.as_retriever(),
# chain_type_kwargs=chain_type_kwargs,
# return_source_documents=True
)
else:
return "Error in loading Documents "
return "Document processing complete-Embeddings Created "
def process_text_document(document_file):
loader = TextLoader(document_file.name)
document = loader.load()
return document
def process_csv_document(document_file):
loader = CSVLoader(file_path=document_file.name)
document = loader.load()
return document
def process_word_document(document_file):
loader = UnstructuredWordDocumentLoader(file_path=document_file.name)
document = loader.load()
return document
def process_pdf_document(document_file):
print("Document File Name :",document_file.name)
loader = PDFMinerLoader(document_file.name)
document = loader.load()
return document
css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with Data • OpenAI/HuggingFace</h1>
<p style="text-align: center;">Upload a file from your computer, click the "Load data to LangChain" button, <br />
when everything is ready, you can start asking questions about the data you uploaded ;) <br />
This version is just for QA retrival so it will not use chat history, and uses Hugging face as LLM,
so you don't need any key</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
with gr.Box():
gr.Row()
LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='Large Language Model Selection',info='LLM Service')
file_extension = gr.Dropdown(FILE_EXT, label="File Extensions", info="Select your files extensions!")
API_key = gr.Textbox(label="Add API key", type="password")
with gr.Column():
with gr.Box():
pdf_doc = gr.File(label="Upload File to start QA", file_types=FILE_EXT, type="file")
with gr.Row():
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=True)
load_pdf = gr.Button("Upload File & Generate Embeddings",).style(full_width=False)
# chatbot = gr.Chatbot()
# question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter")
# submit_button = gr.Button("Send Message")
load_pdf.click(loading_file, None, langchain_status, queue=False)
load_pdf.click(document_loader, inputs=[pdf_doc,API_key,file_extension,LLM_option], outputs=[langchain_status], queue=False)
with gr.Column():
with gr.Row():
chatbot = gr.Chatbot(height=300)
sources = gr.HTML(value = "Source paragraphs where I looked for answers will appear here", height=300)
with gr.Row():
message = gr.Textbox(label="Type your question?",lines=1).style(full_width=False)
submit_query = gr.Button(value="Send message", variant="secondary", scale = 1)
demo.launch()