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import requests
import json
import gradio as gr
# from concurrent.futures import ThreadPoolExecutor
import pdfplumber
import pandas as pd
import langchain
import time
from cnocr import CnOcr
import pinecone
import openai
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
# from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import UnstructuredWordDocumentLoader
from langchain.document_loaders import UnstructuredPowerPointLoader
# from langchain.document_loaders.image import UnstructuredImageLoader
from langchain.chains.question_answering import load_qa_chain
from langchain import OpenAI
from sentence_transformers import SentenceTransformer, models, util
word_embedding_model = models.Transformer('sentence-transformers/all-MiniLM-L6-v2', do_lower_case=True)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode='cls')
embedder = SentenceTransformer(modules=[word_embedding_model, pooling_model])
ocr = CnOcr()
# chat_url = 'https://Raghav001-API.hf.space/sale'
chat_url = 'https://Raghav001-API.hf.space/chatpdf'
chat_emd = 'https://Raghav001-API.hf.space/embedd'
headers = {
'Content-Type': 'application/json',
}
# thread_pool_executor = ThreadPoolExecutor(max_workers=4)
history_max_len = 500
all_max_len = 3000
# Initialize Pinecone client and create an index
pinecone.init(api_key='d0a5b89b-b901-4b47-bc99-38b93695390d',environment = 'asia-southeast1-gcp')
index = pinecone.Index(index_name='test')
def get_emb(text):
emb_url = 'https://Raghav001-API.hf.space/embeddings'
data = {"content": text}
try:
result = requests.post(url=emb_url,
data=json.dumps(data),
headers=headers
)
print("--------------------------------Embeddings--------------------------------------")
print(result.json()['data'][0]['embedding'])
return result.json()['data'][0]['embedding']
except Exception as e:
print('data', data, 'result json', result.json())
def doc_emb(doc: str):
texts = doc.split('\n')
# futures = []
emb_list = embedder.encode(texts)
print('emb_list',emb_list)
# for text in texts:
# futures.append(thread_pool_executor.submit(get_emb, text))
# for f in futures:
# emb_list.append(f.result())
print('\n'.join(texts))
gr.Textbox.update(value="")
return texts, emb_list, gr.Textbox.update(visible=True), gr.Button.update(visible=True), gr.Markdown.update(
value="""success ! Let's talk"""), gr.Chatbot.update(visible=True)
def get_response(msg, bot, doc_text_list, doc_embeddings):
# future = thread_pool_executor.submit(get_emb, msg)
gr.Textbox.update(value="")
now_len = len(msg)
req_json = {'question': msg}
his_bg = -1
for i in range(len(bot) - 1, -1, -1):
if now_len + len(bot[i][0]) + len(bot[i][1]) > history_max_len:
break
now_len += len(bot[i][0]) + len(bot[i][1])
his_bg = i
req_json['history'] = [] if his_bg == -1 else bot[his_bg:]
# query_embedding = future.result()
query_embedding = embedder.encode([msg])
cos_scores = util.cos_sim(query_embedding, doc_embeddings)[0]
score_index = [[score, index] for score, index in zip(cos_scores, [i for i in range(len(cos_scores))])]
score_index.sort(key=lambda x: x[0], reverse=True)
print('score_index:\n', score_index)
print('doc_emb_state', doc_emb_state)
index_set, sub_doc_list = set(), []
for s_i in score_index:
doc = doc_text_list[s_i[1]]
if now_len + len(doc) > all_max_len:
break
index_set.add(s_i[1])
now_len += len(doc)
# Maybe the paragraph is truncated wrong, so add the upper and lower paragraphs
if s_i[1] > 0 and s_i[1] -1 not in index_set:
doc = doc_text_list[s_i[1]-1]
if now_len + len(doc) > all_max_len:
break
index_set.add(s_i[1]-1)
now_len += len(doc)
if s_i[1] + 1 < len(doc_text_list) and s_i[1] + 1 not in index_set:
doc = doc_text_list[s_i[1]+1]
if now_len + len(doc) > all_max_len:
break
index_set.add(s_i[1]+1)
now_len += len(doc)
index_list = list(index_set)
index_list.sort()
for i in index_list:
sub_doc_list.append(doc_text_list[i])
req_json['doc'] = '' if len(sub_doc_list) == 0 else '\n'.join(sub_doc_list)
data = {"content": json.dumps(req_json)}
print('data:\n', req_json)
result = requests.post(url=chat_url,
data=json.dumps(data),
headers=headers
)
res = result.json()['content']
bot.append([msg, res])
return bot[max(0, len(bot) - 3):]
def up_file(fls):
doc_text_list = []
names = []
print(names)
for i in fls:
names.append(str(i.name))
pdf = []
docs = []
pptx = []
for i in names:
if i[-3:] == "pdf":
pdf.append(i)
elif i[-4:] == "docx":
docs.append(i)
else:
pptx.append(i)
#Pdf Extracting
for idx, file in enumerate(pdf):
print("11111")
#print(file.name)
with pdfplumber.open(file) as pdf:
for i in range(len(pdf.pages)):
# Read page i+1 of a PDF document
page = pdf.pages[i]
res_list = page.extract_text().split('\n')[:-1]
for j in range(len(page.images)):
# Get the binary stream of the image
img = page.images[j]
file_name = '{}-{}-{}.png'.format(str(time.time()), str(i), str(j))
with open(file_name, mode='wb') as f:
f.write(img['stream'].get_data())
try:
res = ocr.ocr(file_name)
# res = PyPDFLoader(file_name)
except Exception as e:
res = []
if len(res) > 0:
res_list.append(' '.join([re['text'] for re in res]))
tables = page.extract_tables()
for table in tables:
# The first column is used as the header
df = pd.DataFrame(table[1:], columns=table[0])
try:
records = json.loads(df.to_json(orient="records", force_ascii=False))
for rec in records:
res_list.append(json.dumps(rec, ensure_ascii=False))
except Exception as e:
res_list.append(str(df))
doc_text_list += res_list
doc_text_list = [str(text).strip() for text in doc_text_list if len(str(text).strip()) > 0]
# print(doc_text_list)
return gr.Textbox.update(value='\n'.join(doc_text_list), visible=True), gr.Button.update(
visible=True), gr.Markdown.update(
value="Processing")
def get_answer(query_live):
llm = OpenAI(temperature=0, openai='aaa')
qa_chain = load_qa_chain(llm,chain_type='stuff')
query = query_live
docs = docstore.similarity_search(query)
qa_chain.run(input_documents = docs, question = query)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
file = gr.File(file_types=['.pdf'], label='Click to upload Document', file_count='multiple')
doc_bu = gr.Button(value='Submit', visible=False)
txt = gr.Textbox(label='result', visible=False)
doc_text_state = gr.State([])
doc_emb_state = gr.State([])
with gr.Column():
md = gr.Markdown("Please Upload the PDF")
chat_bot = gr.Chatbot(visible=False)
msg_txt = gr.Textbox(visible = False)
chat_bu = gr.Button(value='Clear', visible=False)
file.change(up_file, [file], [txt, doc_bu, md]) #hiding the text
doc_bu.click(doc_emb, [txt], [doc_text_state, doc_emb_state, msg_txt, chat_bu, md, chat_bot])
msg_txt.submit(get_response, [msg_txt, chat_bot,doc_text_state, doc_emb_state], [chat_bot],queue=False)
chat_bu.click(lambda: None, None, chat_bot, queue=False)
if __name__ == "__main__":
demo.queue().launch(show_api=False)
# demo.queue().launch(share=False, server_name='172.22.2.54', server_port=9191) |