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from os import path
import streamlit as st
import tensorflow as tf
import random
from transformers import ElectraTokenizerFast, TFElectraForQuestionAnswering
from datasets import Dataset, DatasetDict, load_dataset
model_hf = "nguyennghia0902/electra-small-discriminator_0.0001_16_15e"
tokenizer = ElectraTokenizerFast.from_pretrained(model_hf)
reload_model = TFElectraForQuestionAnswering.from_pretrained(model_hf)
@st.cache_resource
def predict(question, context):
inputs = tokenizer(question, context, return_offsets_mapping=True,return_tensors="tf",max_length=512, truncation=True)
offset_mapping = inputs.pop("offset_mapping")
outputs = reload_model(**inputs)
answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
start_char = offset_mapping[0][answer_start_index][0]
end_char = offset_mapping[0][answer_end_index][1]
predicted_answer_text = context[start_char:end_char]
return predicted_answer_text
def main():
st.set_page_config(page_title="Sample in Dataset", page_icon="📝")
# giving a title to our page
col1, col2 = st.columns([2, 1])
col1.title("Sample in Dataset")
new_data = load_dataset("nguyennghia0902/project02_textming_dataset", data_files={'train': 'raw_newformat_data/traindata-00000-of-00001.arrow', 'test': 'raw_newformat_data/testdata-00000-of-00001.arrow'})
sample = random.choice(new_data['test'])
sampleQ = sample['question']
sampleC = sample['context']
sampleA = sample['answers']["text"][0]
text = st.text_area(
"Sample CONTEXT:",
sampleC,
height=200,
)
question = st.text_area(
"Sample QUESTION: ",
sampleQ,
height=5,
)
answer = st.text_area(
"True ANSWER:",
sampleA,
height=5,
)
# Create a prediction button
if st.button("Sample & Predict"):
prediction = ""
prediction = predict(question, text)
if prediction == "":
st.error(prediction)
else:
st.success(prediction)
if __name__ == "__main__":
print(tf.__version__)
main() |