import gradio as gr
import numpy as np
import time
import hashlib
import torch
from transformers import AutoTokenizer, AutoModel, AutoModelForQuestionAnswering, pipeline
from tqdm import tqdm
import os
device = "cuda:0" if torch.cuda.is_available() else "cpu"
import textract
from scipy.special import softmax
import pandas as pd
from datetime import datetime
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1").to(device).eval()
tokenizer_ans = AutoTokenizer.from_pretrained("deepset/roberta-large-squad2")
model_ans = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-large-squad2").to(device).eval()
if device == 'cuda:0':
pipe = pipeline("question-answering",model_ans,tokenizer =tokenizer_ans,device = 0)
else:
pipe = pipeline("question-answering",model_ans,tokenizer =tokenizer_ans)
def cls_pooling(model_output):
return model_output.last_hidden_state[:,0]
def encode_query(query):
encoded_input = tokenizer(query, truncation=True, return_tensors='pt').to(device)
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
embeddings = cls_pooling(model_output)
return embeddings.cpu()
def encode_docs(docs,maxlen = 64, stride = 32):
encoded_input = []
embeddings = []
spans = []
file_names = []
name, text = docs
text = text.split(" ")
if len(text) < maxlen:
text = " ".join(text)
encoded_input.append(tokenizer(temp_text, return_tensors='pt', truncation = True).to(device))
spans.append(temp_text)
file_names.append(name)
else:
num_iters = int(len(text)/maxlen)+1
for i in range(num_iters):
if i == 0:
temp_text = " ".join(text[i*maxlen:(i+1)*maxlen+stride])
else:
temp_text = " ".join(text[(i-1)*maxlen:(i)*maxlen][-stride:] + text[i*maxlen:(i+1)*maxlen])
encoded_input.append(tokenizer(temp_text, return_tensors='pt', truncation = True).to(device))
spans.append(temp_text)
file_names.append(name)
with torch.no_grad():
for encoded in tqdm(encoded_input):
model_output = model(**encoded, return_dict=True)
embeddings.append(cls_pooling(model_output))
embeddings = np.float32(torch.stack(embeddings).transpose(0, 1).cpu())
np.save("emb_{}.npy".format(name),dict(zip(list(range(len(embeddings))),embeddings)))
np.save("spans_{}.npy".format(name),dict(zip(list(range(len(spans))),spans)))
np.save("file_{}.npy".format(name),dict(zip(list(range(len(file_names))),file_names)))
return embeddings, spans, file_names
def predict(query,data):
name_to_save = data.name.split("\\")[-1].split(".")[0][:-8]
st = str([query,name_to_save])
hist = st + " " + str(hashlib.sha256(st.encode()).hexdigest())
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
try:
df = pd.read_csv("{}.csv".format(hash(st)))
return df
except Exception as e:
print(e)
print(st)
if name_to_save+".txt" in os.listdir():
doc_emb = np.load('emb_{}.npy'.format(name_to_save),allow_pickle='TRUE').item()
doc_text = np.load('spans_{}.npy'.format(name_to_save),allow_pickle='TRUE').item()
file_names_dicto = np.load('file_{}.npy'.format(name_to_save),allow_pickle='TRUE').item()
doc_emb = np.array(list(doc_emb.values())).reshape(-1,768)
doc_text = list(doc_text.values())
file_names = list(file_names_dicto.values())
else:
text = textract.process("{}".format(data.name)).decode('utf8')
text = text.replace("\r", " ")
text = text.replace("\n", " ")
text = text.replace(" . "," ")
doc_emb, doc_text, file_names = encode_docs((name_to_save,text),maxlen = 64, stride = 32)
doc_emb = doc_emb.reshape(-1, 768)
with open("{}.txt".format(name_to_save),"w",encoding="utf-8") as f:
f.write(text)
start = time.time()
query_emb = encode_query(query)
scores = np.matmul(query_emb, doc_emb.transpose(1,0))[0].tolist()
doc_score_pairs = list(zip(doc_text, scores, file_names))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
k = 5
probs_sum = 0
probs = softmax(sorted(scores,reverse = True)[:k])
table = {"Passage":[],"Answer":[],"Probabilities":[],"Source":[]}
for i, (passage, _, names) in enumerate(doc_score_pairs[:k]):
passage = passage.replace("\n","")
passage = passage.replace(" . "," ")
if probs[i] > 0.1 or (i < 3 and probs[i] > 0.05): #generate answers for more likely passages but no less than 2
QA = {'question':query,'context':passage}
ans = pipe(QA)
probabilities = "P(a|p): {}, P(a|p,q): {}, P(p|q): {}".format(round(ans["score"],5),
round(ans["score"]*probs[i],5),
round(probs[i],5))
passage = passage.replace(str(ans["answer"]),str(ans["answer"]).upper())
table["Passage"].append(passage)
table["Passage"].append("---")
table["Answer"].append(str(ans["answer"]).upper())
table["Answer"].append("---")
table["Probabilities"].append(probabilities)
table["Probabilities"].append("---")
table["Source"].append(names)
table["Source"].append("---")
else:
table["Passage"].append(passage)
table["Passage"].append("---")
table["Answer"].append("no_answer_calculated")
table["Answer"].append("---")
table["Probabilities"].append("P(p|q): {}".format(round(probs[i],5)))
table["Probabilities"].append("---")
table["Source"].append(names)
table["Source"].append("---")
df = pd.DataFrame(table)
print("time: "+ str(time.time()-start))
with open("HISTORY.txt","a", encoding = "utf-8") as f:
f.write(hist)
f.write(" " + str(current_time))
f.write("\n")
f.close()
df.to_csv("{}.csv".format(hash(st)), index=False)
return df
iface = gr.Interface(examples = [
["How high is the highest mountain?","China.pdf"],
["Where does UK prime minister live?","London.pdf"]
],
fn =predict,
inputs = [gr.inputs.Textbox(default="What is Open-domain question answering?"),
gr.inputs.File(),
],
outputs = [
gr.outputs.Dataframe(),
],
allow_flagging ="manual",flagging_options = ["correct","wrong"],
allow_screenshot=False)
iface.launch(share = True,enable_queue=True, show_error =True)