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import os | |
import gradio as gr | |
import time | |
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'] | |
DEFAULT_SYSTEM_PROMPT = "As a chatbot you are answering set of questions being requested ." | |
MAX_NEW_TOKENS = 4096 | |
DEFAULT_TEMPERATURE = 0.1 | |
DEFAULT_MAX_NEW_TOKENS = 2048 | |
MAX_INPUT_TOKEN_LENGTH = 4000 | |
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=1500,chunk_overlap=100): | |
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,max_tokens=4096): | |
chat_llm = HuggingFaceHub(huggingfacehub_api_token=API_key, | |
repo_id=model_id, | |
model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens}) | |
return chat_llm | |
def chat_application(llm_service,key,temperature=0.1,max_tokens=1024): | |
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 document_loader(file_path,api_key,doc_type='pdf',llm='Huggingface',temperature=0.1,max_tokens=4096): | |
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) | |
print("Document :",document) | |
embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-base',model_kwargs={"device": DEVICE}) | |
texts = process_documents(documents=document) | |
global vector_db | |
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, | |
temperature=temperature, | |
max_tokens=max_tokens | |
), | |
chain_type='stuff', | |
retriever=vector_db.as_retriever(), | |
# chain_type_kwargs=chain_type_kwargs, | |
return_source_documents=True | |
) | |
return "Document Processing completed ..." | |
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 | |
def clear_chat(): | |
return [] | |
def infer(question, history): | |
# res = [] | |
# # for human, ai in history[:-1]: | |
# # pair = (human, ai) | |
# # res.append(pair) | |
# chat_history = res | |
print("Question in infer :",question) | |
result = qa({"query": question}) | |
matching_docs_score = vector_db.similarity_search_with_score(question) | |
print(" Matching_doc ",matching_docs_score) | |
return result["result"] | |
def bot(history): | |
response = infer(history[-1][0], history) | |
history[-1][1] = "" | |
for character in response: | |
history[-1][1] += character | |
time.sleep(0.05) | |
yield history | |
def add_text(history, text): | |
history = history + [(text, None)] | |
return history, "" | |
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 system,UpLoad file and generate embeddings, <br /> | |
once status is ready, you can start asking questions about the data you uploaded without chat history <br /> | |
and gives you option to use HuggingFace/OpenAI as LLM's, make sure to add your key. | |
</p> | |
</div> | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.HTML(title) | |
with gr.Group(): | |
chatbot = gr.Chatbot(height=300) | |
with gr.Row(): | |
sources = gr.HTML(value = "Source paragraphs where I looked for answers will appear here", height=300) | |
with gr.Row(): | |
question = gr.Textbox(label="Type your question !",lines=1).style(full_width=True) | |
submit_btn = gr.Button(value="Send message", variant="primary", scale = 1) | |
clean_chat_btn = gr.Button("Delete Chat") | |
with gr.Column(): | |
with gr.Box(): | |
LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='Large Language Model Selection',info='LLM Service') | |
API_key = gr.Textbox(label="Add API key", type="password") | |
with gr.Column(): | |
with gr.Box(): | |
file_extension = gr.Dropdown(FILE_EXT, label="File Extensions", info="Select type of file to upload !") | |
pdf_doc = gr.File(label="Upload File to start QA", file_types=FILE_EXT, type="file") | |
with gr.Accordion(label='Advanced options', open=False): | |
max_new_tokens = gr.Slider( | |
label='Max new tokens', | |
minimum=2048, | |
maximum=MAX_NEW_TOKENS, | |
step=1, | |
value=DEFAULT_MAX_NEW_TOKENS, | |
) | |
temperature = gr.Slider( | |
label='Temperature', | |
minimum=0.1, | |
maximum=4.0, | |
step=0.1, | |
value=DEFAULT_TEMPERATURE, | |
) | |
with gr.Row(): | |
langchain_status = gr.Textbox(label="Status", placeholder="", interactive = False) | |
load_pdf = gr.Button("Upload File & Generate Embeddings",).style(full_width = False) | |
# chatbot = gr.Chatbot()l̥ | |
# question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter") | |
# submit_button = gr.Button("Send Message") | |
if pdf_doc: | |
load_pdf.click(loading_file, None, langchain_status, queue=False) | |
load_pdf.click(document_loader, inputs=[pdf_doc,API_key,file_extension,LLM_option,temperature,max_new_tokens], outputs=[langchain_status], queue=False) | |
question.submit(add_text, inputs=[chatbot, question], outputs=[chatbot, question]).then(bot, chatbot, chatbot) | |
submit_btn.click(add_text, inputs=[chatbot, question], outputs=[chatbot, question]).then(bot, chatbot, chatbot) | |
# submit_btn.then(chatf.highlight_found_text, [chatbot, sources], [sources]) | |
clean_chat_btn.click(clear_chat, [], chatbot) | |
demo.launch() |