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from os import path
import streamlit as st
import tensorflow as tf
from transformers import ElectraTokenizerFast, TFElectraForQuestionAnswering
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="Question Answering", page_icon="📝")
# giving a title to our page
col1, col2 = st.columns([2, 1])
col1.title("Question Answering")
col2.link_button("Explore my model", "https://huggingface.co/"+model_hf)
text = st.text_area(
"CONTEXT: Please enter a context:",
placeholder="Enter your context here",
height=200,
)
question = st.text_area(
"QUESTION: Please enter a question:",
placeholder="Enter your question here",
height=5,
)
prediction = ""
upload_file = st.file_uploader("QUESTION: Or upload a file with some questions", type=["txt"])
if upload_file is not None:
question = upload_file.read().decode("utf-8")
for line in question.splitlines():
line = line.strip()
if not line:
continue
prediction = predict(line, text)
st.success(line + "\n\nAnswer: " + prediction)
# Create a prediction button
elif st.button("Predict"):
prediction = ""
stripped_text = text.strip()
if not stripped_text:
st.error("Please enter a context.")
return
stripped_question = question.strip()
if not stripped_question:
st.error("Please enter a question.")
return
prediction = predict(stripped_question, stripped_text)
if prediction == "":
st.error(prediction)
else:
st.success(prediction)
if __name__ == "__main__":
main() |