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import os
import gradio as gr
from langchain.document_loaders import PDFMinerLoader,CSVLoader ,UnstructuredWordDocumentLoader,TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import FAISS
from langchain import HuggingFaceHub
DEVICE = 'cpu '
FILE_EXT = ['pdf','text','csv','word','wav']
def loading_pdf():
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 = RecursiveCharacterTextSplitter(chunk_size=data_chunk, chunk_overlap=chunk_overlap)
texts = text_splitter.split_documents(documents[0])
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_api(file_data,doc_type='pdf',key=None,llm_model='HuggingFace'):
embedding_model = SentenceTransformerEmbeddings(model_name='all-mpnet-base-v2',model_kwargs={"device": DEVICE})
document = None
if doc_type == 'pdf':
document = process_pdf_document(document_file_name=file_data)
elif doc_type == 'text':
document = process_text_document(document_file_name=file_data)
elif doc_type == 'csv':
document = process_csv_document(document_file_name=file_data)
elif doc_type == 'word':
document = process_word_document(document_file_name=file_data)
texts = process_documents(documents=document)
vectordb = FAISS.from_documents(documents=texts, embedding= embedding_model)
if llm_model == 'HuggingFace':
llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',API_key=key)
else:
llm_model = get_openai_chat_model(API_key=key)
def process_text_document(document_file_name):
loader = TextLoader(document_file_name)
document = loader.load()
return document
def process_csv_document(document_file_name):
loader = CSVLoader(file_path=document_file_name)
document = loader.load()
return document
def process_word_document(document_file_name):
loader = UnstructuredWordDocumentLoader(file_path=document_file_name)
document = loader.load()
return document
def process_pdf_document(document_file_name):
loader = PDFMinerLoader(document_file_name)
document = loader.load()[0]
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():
LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='LLM',info='select the LLM to be used')
API_key = gr.Textbox(label="You OpenAI/Huggingface API key", type="password")
with gr.Column():
file_extension = gr.Dropdown(FILE_EXT, label="File Extensions", info="Select your files extensions!")
pdf_doc = gr.File(label="Load a File", file_types=FILE_EXT, type="file")
with gr.Row():
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
load_pdf = gr.Button("Load file to langchain")
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_pdf, None, langchain_status, queue=False)
load_pdf.click(chat_api, inputs=[pdf_doc,file_extension,API_key,LLM_option], outputs=[langchain_status], queue=False) |