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import gradio as gr
import torch
import itertools
import pandas as pd
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = 'philipp-zettl/t5-small-long-qa'
qa_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model_name = 'philipp-zettl/t5-small-qg'
qg_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-small')
# Move only the student model to GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
qa_model = qa_model.to(device)
qg_model = qg_model.to(device)
max_questions = 1
max_answers = 1
def run_model(inputs, tokenizer, model, temperature=0.5, num_return_sequences=1):
all_outputs = []
for input_text in inputs:
model_inputs = tokenizer([input_text], max_length=512, padding=True, truncation=True)
input_ids = torch.tensor(model_inputs['input_ids']).to(device)
for sample in input_ids:
sample_outputs = []
with torch.no_grad():
sample_output = model.generate(
input_ids[:1],
max_length=85,
temperature=temperature,
do_sample=True,
num_return_sequences=num_return_sequences,
low_memory=True,
num_beams=max(2, num_return_sequences),
use_cache=True,
)
for i, sample_output in enumerate(sample_output):
sample_output = sample_output.unsqueeze(0)
sample_output = tokenizer.decode(sample_output[0], skip_special_tokens=True)
sample_outputs.append(sample_output)
all_outputs.append(sample_outputs)
return all_outputs
def gen(content, temperature_qg=0.5, temperature_qa=0.75, num_return_sequences_qg=1, num_return_sequences_qa=1):
inputs = [
f'context: {content}'
]
question = run_model(inputs, tokenizer, qg_model, temperature_qg, num_return_sequences_qg)
inputs = list(itertools.chain.from_iterable([
[f'question: {q} {inputs[0]}' for q in q_set] for q_set in question
]))
answer = run_model(inputs, tokenizer, qa_model, temperature_qa, num_return_sequences_qa)
questions = list(itertools.chain.from_iterable(question))
answers = list(itertools.chain.from_iterable(answer))
results = []
for idx, ans in enumerate(answers):
results.append({'question': questions[idx % num_return_sequences_qg], 'answer': ans})
return results
def variable_outputs(k, max_elems=10):
k = int(k)
return [gr.Text(visible=True)] * k + [gr.Text(visible=False)] * (max(max_elems, 10)- k)
def set_outputs(content, max_elems=10):
c = eval(content)
print('received content: ', c)
return [gr.Text(value=t, visible=True) for t in c] + [gr.Text(visible=False)] * (max(max_elems, 10) - len(c))
def create_file_download(qnas):
with open('qnas.tsv', 'w') as f:
for idx, qna in qnas.iterrows():
f.write(qna['Question'] + '\t' + qna['Answer'])
if idx < len(qnas) - 1:
f.write('\n')
return 'qnas.tsv'
with gr.Blocks() as demo:
with gr.Row(equal_height=True):
with gr.Group("Content"):
content = gr.Textbox(label='Content', lines=15, placeholder='Enter text here', max_lines=10_000)
with gr.Group("Settings"):
temperature_qg = gr.Slider(label='Temperature QG', value=0.5, minimum=0, maximum=1, step=0.01)
temperature_qa = gr.Slider(label='Temperature QA', value=0.75, minimum=0, maximum=1, step=0.01)
num_return_sequences_qg = gr.Number(label='Number Questions', value=max_questions, minimum=1, step=1, maximum=max(max_questions, 10))
num_return_sequences_qa = gr.Number(label="Number Answers", value=max_answers, minimum=1, step=1, maximum=max(max_questions, 10))
with gr.Row():
gen_btn = gr.Button("Generate")
@gr.render(inputs=[content, temperature_qg, temperature_qa, num_return_sequences_qg, num_return_sequences_qa], triggers=[gen_btn.click])
def render_results(content, temperature_qg, temperature_qa, num_return_sequences_qg, num_return_sequences_qa):
qnas = gen(content, temperature_qg, temperature_qa, num_return_sequences_qg, num_return_sequences_qa)
df = gr.Dataframe(
value=[u.values() for u in qnas],
headers=['Question', 'Answer'],
col_count=2,
wrap=True
)
pd_df = pd.DataFrame([u.values() for u in qnas], columns=['Question', 'Answer'])
download = gr.DownloadButton(label='Download (without headers)', value=create_file_download(pd_df))
demo.launch() |