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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.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
DEVICE = 'cpu'
FILE_EXT = ['pdf','text','csv','word']
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 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,temperature=0.1,max_tokens=4096,API_key=None):
chat_llm = HuggingFacePipeline.from_model_id(
model_id=model_id,
task="text-generation",
pipeline_kwargs={"max_new_tokens": max_tokens,"temperature": temperature,},
)
# 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(temperature=0.1, max_tokens=1024):
llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',temperature=temperature, max_tokens=max_tokens)
return llm
def document_loader(file_path,doc_type='pdf',temperature=0.1,max_tokens=2048):
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)
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(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():
question = gr.Textbox(label="Type your question !",lines=1)
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(['tiiuae/falcon-7b-instruct'],label='Large Language Model Selection',info='LLM Service')
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=1024,
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,file_extension,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() |