from langchain.document_loaders import PyPDFLoader, TextLoader, Docx2txtLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain import HuggingFaceHub
from langchain.chains.summarize import load_summarize_chain
from langchain.chains.llm_summarization_checker.base import LLMSummarizationCheckerChain
from langchain.prompts import PromptTemplate
import os
import gradio as gr
import shutil
import re
import tempfile
import cache
from pathlib import Path
api_token=os.environ['api']
os.environ["HUGGINFACEHUB_API_TOKEN"]=api_token
temp_dir = "/content/sample_data"
def data_ingestion(file_path):
if not os.path.exists(file_path):
raise ValueError(f"File path {file_path} does not exist.")
path = Path(file_path)
file_ext = path.suffix
# file_ext = os.path.splitext(file_path)[-1]
# if file_ext == ".pdf":
# # loader = PyPDFLoader(file_path)
# loader = PDFMinerLoader(file_path)
# document= loader.load()
# elif file_ext in {".docx", ".doc"}:
# loader = Docx2txtLoader(file_path)
# document= loader.load()
# elif file_ext == ".txt":
# loader = TextLoader(file_path)
# document= loader.load()
loader = PDFMinerLoader(file_path)
document= loader.load()
length = len(document[0].page_content)
# Replace CharacterTextSplitter with RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
split_docs = text_splitter.split_documents(document)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
llm = HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
model_kwargs={"temperature":1, "max_length":10000},
huggingfacehub_api_token=api_token)
return split_docs
# text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
# chunk_size=2000, chunk_overlap=0
# )
# split_docs = text_splitter.split_documents(document)
# documents=split_text_into_batches(str(document),400)
# len(documents)
# documents[0]
# #
# from langchain.text_splitter import CharacterTextSplitter
# text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
# documents = text_splitter.split_documents(document)
# Embeddings
# from langchain.chains.question_answering import load_qa_chain
########## CHAIN 1 norm text
def chain1():
prompt_template = """Write a concise summary of the following:
{text}
SUMMARY:"""
prompt = PromptTemplate.from_template(prompt_template)
refine_template = (
"Your job is to produce a final summary\n"
# "We have provided an existing summary up to a certain point: {existing_answer}\n"
"We have the opportunity to refine the existing summary"
"(only if needed) with some more context below.\n"
"------------\n"
"{text}\n"
"------------\n"
"Given the new context, refine the original summary in English"
"If the context isn't useful, return the original summary." )
refine_prompt = PromptTemplate.from_template(refine_template)
chain1 = load_summarize_chain(
llm=HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
model_kwargs={"temperature":1, "max_length":10000},
huggingfacehub_api_token=api_token),
chain_type="refine",
question_prompt=prompt,
# refine_prompt=refine_prompt,
return_intermediate_steps=False,
input_key="input_documents",
output_key="output_text",
)
return chain1
# result = chain({"input_documents":split_docs}, return_only_outputs=True)
########## CHAIN 2 research paper
def chain2():
prompt_template = """This is a Research Paper,your job is to summarise the text portion without any symbols or special characters, skip the mathematical equations for now:
{text}
SUMMARY:"""
prompt = PromptTemplate.from_template(prompt_template)
refine_template = (
"Your job is to produce a final summary\n"
# "We have provided an existing summary up to a certain point: {existing_answer}\n"
"We have the opportunity to refine the existing summary"
"(only if needed) with some more context below.\n"
"------------\n"
"{text}\n"
"------------\n"
"Given the new context, refine the original summary in English"
"If the context isn't useful, return the original summary." )
refine_prompt = PromptTemplate.from_template(refine_template)
chain2 = load_summarize_chain(
llm = HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
model_kwargs={"temperature":1, "max_length":10000},
huggingfacehub_api_token=api_token),
chain_type = "refine",
question_prompt = prompt,
# refine_prompt = refine_prompt,
return_intermediate_steps=False,
input_key="input_documents",
output_key="output_text",
)
return chain2
# result = chain({"input_documents":split_docs}, return_only_outputs=True)
########## CHAIN 3 arxiv_paper_1
def chain3():
prompt_template = """You are being given a markdown document with headers, this is part of a larger arxiv paper. Your job is to write a summary of the document.
here is the content of the section:
"{text}"
SUMMARY:"""
prompt = PromptTemplate.from_template(prompt_template)
refine_template = ("""You are presented with a collection of text snippets. Each snippet is a summary of a specific section from an academic paper published on arXiv. Your objective is to synthesize these snippets into a coherent, concise summary of the entire paper.
DOCUMENT SNIPPETS:
"{text}"
INSTRUCTIONS: Craft a concise summary below, capturing the essence of the paper based on the provided snippets.
It is also important that you highlight the key contributions of the paper, and 3 key takeaways from the paper.
Lastly you should provide a list of 5 questions that you would ask the author of the paper if you had the chance. Remove all the backslash n (\n)
SUMMARY:
"""
)
refine_prompt = PromptTemplate.from_template(refine_template)
chain3 = load_summarize_chain(
llm=HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
model_kwargs={"temperature":1, "max_length":10000},
huggingfacehub_api_token=api_token),
chain_type="refine",
question_prompt=prompt,
# refine_prompt=refine_prompt,
return_intermediate_steps=False,
input_key="input_documents",
output_key="output_text",
)
return chain3
# result = chain({"input_documents":split_docs}, return_only_outputs=True)
# chain.run(document)
# print(result["output_text"])
def chain_function(checkbox_values):
if "Research Paper" in checkbox_values:
output = chain3()
elif "Legal Document" in checkbox_values:
output = chain2()
elif "Study Material" in checkbox_values:
output = chain1()
else:
output = "Please select a document type to run."
return output
def result(chain, split_docs):
summaries = []
for doc in split_docs:
result = chain({"input_documents": [doc]})
# result = chain({"input_documents": [doc]}, return_only_outputs=True)
summaries.append(result["output_text"])
text_concat = ""
for i in summaries:
text_concat += i
# output = re.sub(r'\n'," "," ",text_concat)
return text_concat
title = """S I M P L I F Y
"""
# description = r"""S I M P L I F Y
# """
# article = r"""
# If PhotoMaker is helpful, please help to ⭐ the Github Repo. Thanks!
# [](https://github.com/TencentARC/PhotoMaker)
# ---
# 📝 **Citation**
#
# If our work is useful for your research, please consider citing:
# ```bibtex
# @article{li2023photomaker,
# title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
# author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
# booktitle={arXiv preprint arxiv:2312.04461},
# year={2023}
# }
# ```
# 📋 **License**
#
# Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/TencentARC/PhotoMaker/blob/main/LICENSE) for details.
# 📧 **Contact**
#
# If you have any questions, please feel free to reach me out at zhenli1031@gmail.com.
# """
# tips = r"""
# ### Usage tips of PhotoMaker
# 1. Upload more photos of the person to be customized to **improve ID fidelty**. If the input is Asian face(s), maybe consider adding 'asian' before the class word, e.g., `asian woman img`
# 2. When stylizing, does the generated face look too realistic? Adjust the **Style strength** to 30-50, the larger the number, the less ID fidelty, but the stylization ability will be better.
# 3. If you want to generate realistic photos, you could try switching to our other gradio application [PhotoMaker](https://huggingface.co/spaces/TencentARC/PhotoMaker).
# 4. For **faster** speed, reduce the number of generated images and sampling steps. However, please note that reducing the sampling steps may compromise the ID fidelity.
# """
# def process_file(file_obj):
# destination_path = "/content/sample_data" # Replace with your desired path
# shutil.copy(file_obj, destination_path) # Save file to specified path
# return os.path.join(destination_path, file_obj)
def process_file(list_file_obj):
# list_file_path = [x.name for x in list_file_obj if x is not None]
# file_content = file_obj.data
# with tempfile.TemporaryFile() as temp_file:
# temp_file.write(file_content)
# temp_file_path = temp_file.name
return list_file_obj[0].name
def inference(checkbox_values, uploaded_file):
file_path = process_file(uploaded_file)
split_docs = data_ingestion(file_path)
chain = chain_function(checkbox_values)
summary = result(chain, split_docs)
return summary
with gr.Blocks(theme="monochrome") as demo:
gr.Markdown(title)
with gr.Row():
with gr.Column():
checkbox_values = gr.CheckboxGroup(["Research Paper", "Legal Document", "Study Material"], label="Choose the document type")
uploaded_file = gr.Files(height=100, file_count="multiple", file_types=["text", ".docx", "pdf"], interactive=True, label="Upload your File.")
btn = gr.Button("Submit") # Place the button outside the Row for vertical alignment
with gr.Column():
txt = gr.Textbox(
show_label=False,scale=2,
# placeholder="Simplify."
)
btn.click(
fn=inference,
inputs=[checkbox_values, uploaded_file],
outputs=[txt],
queue=False
)
# debug = True
demo.launch(debug = True)