streamlit_demo_usage.py

import streamlit as st
# To make things easier later, we're also importing numpy and pandas for working with sample data.
import numpy
import pandas

# Don't worry, we'll explain this method in the next section. We need to make at least one
# call to Streamlit in order to generate a report.
st.title("Demo Test")
# streamlit.header("I'm a large heading")
# streamlit.subheader("I'm not a large heading")



st.markdown("**AllenNLP Reading Comprehension demo**")


st.markdown('**Passage**:')
passage_pretext = 'The Matrix is a 1999 science fiction action film.'
passage =st.text_area('Passage Input:', passage_pretext)




import allennlp
from allennlp.predictors.predictor import Predictor
@st.cache
def load_predictor():
    predictor = Predictor.from_path("https://s3-us-west-2.amazonaws.com/allennlp/models/bidaf-model-2017.09.15-charpad.tar.gz")
    return predictor

predictor = load_predictor()

q = "When is the Matrix written?"

question = st.text_input('question:', q)


# st.write(passage)
# st.write(question)
answer= predictor.predict(passage = passage, question = question)

best_span = answer["best_span_str"]
best_span_idx = answer["best_span"]

st.write(answer)
st.write(best_span)


def in_span(pos: int, idxs):
    if pos >= idxs[0] and pos <= idxs[1]:
        return True
    return False

str = ["**"+x+"**" if in_span(pos, best_span_idx) else x for pos,x in enumerate(answer["passage_tokens"])]

st.markdown(" ".join(str))

import matplotlib.pyplot as plt

import numpy as np

attention_array = np.asarray(answer["passage_question_attention"])
fig, ax = plt.subplots()
im = ax.imshow(attention_array)
st.pyplot()




st.header("Address Parsing Demo")


address = st.text_input('address:', value='广东省广州市')