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import streamlit as st
import torch
import transformers
from transformers import AutoTokenizer, AutoModelWithLMHead
# device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
device=torch.device("cpu")
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
model=torch.load("Gpt_neo_Epoch_10_Loss_031_data_5000.pth",map_location=torch.device('cpu'))
def predict_query(input_sentence,max_len=40,temp=0.7):
pred=[]
seq=tokenizer(input_sentence,return_tensors='pt')['input_ids'].to(device)
outputs=model.generate(seq,
max_length=max_len,
do_sample=True,
top_p=0.95,
#num_beams=5,
temperature=temp,
no_repeat_ngram_size=3,
num_return_sequences=5
).to(device)
for i,out in enumerate(outputs):
out=tokenizer.decode(out, skip_special_tokens=True)
idx=out.find("<|sep|>")+7
out=out[idx:]
print(f"Sugestion{i} :{out}")
pred.append(tokenizer.decode(out, skip_special_tokens=True))
return pred
# option = st.selectbox(
# 'Please Select option',
# ('Predictive writing',"None"),index=1)
st.title("Predictive scientific writing")
st.write('### Using AI to Generate scientific literature')
st.sidebar.markdown(
'''
## This is a demo of a text generation model trained with GPT-2
''')
max_len = st.sidebar.slider(label='Output Size', min_value=1, max_value=150, value=10, step=1)
# samples = st.sidebar.slider(label='Number of Samples', min_value=1, max_value=50, value=10, step=1)
temp = st.sidebar.slider(label='Temperature', min_value=0.0, max_value=2.0, value=0.8, step=0.1)
# temp = st.sidebar.slider(label='Temperature', min_value=0.1, max_value=1.0, value=5.0, step=0.05)
# do_sample=st.sidebar.checkbox("do_sample")
# max_len=st.slider("max_len",1,100,None,1,key="max_len")
# top_k=st.slider("top_k",1,50,None,1)
# do_sample=st.checkbox("do_sample")
# print(max_len)
sentence = st.text_area('Input your sentence here:')
clear=st.button("Clear")
Enter=st.button("Generate")
if clear:
print(clear)
st.markdown(' ')
if Enter:
st.header("Output-")
print("Generating predictions......\n\n")
# out=generate(sentence,max_len,top_k,do_sample)
out=predict_query(sentence,max_len,temp)
for i,out in enumerate(out):
st.markdown(f"Sugestion {i} :{out}")
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