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import streamlit as st
import time
import pandas as pd
import io
import zipfile
from gliner import GLiNER
from streamlit_extras.stylable_container import stylable_container
import plotly.express as px
import os
from comet_ml import Experiment
COMET_API_KEY = os.environ.get("COMET_API_KEY")
COMET_WORKSPACE = os.environ.get("COMET_WORKSPACE")
COMET_PROJECT_NAME = os.environ.get("COMET_PROJECT_NAME")
if COMET_API_KEY and COMET_WORKSPACE and COMET_PROJECT_NAME:
comet_initialized = True
else:
comet_initialized = False
st.warning("Comet ML not initialized. Check environment variables.")
text = st.text_area("Type or paste your text below, and then press Ctrl + Enter", key='my_text_area')
if st.button("Results"):
with st.spinner("Wait for it...", show_time=True):
time.sleep(5)
model = GLiNER.from_pretrained("xomad/gliner-model-merge-large-v1.0")
labels = ["person", "country", "city", "organization", "date", "money", "percent value", "position"]
entities = model.predict_entities(text, labels)
df = pd.DataFrame(entities)
if comet_initialized:
experiment = Experiment(
api_key=COMET_API_KEY,
workspace=COMET_WORKSPACE,
project_name=COMET_PROJECT_NAME,
)
experiment.log_parameter("input_text", text)
experiment.log_table("predicted_entities", df)
properties = {"border": "2px solid gray", "color": "blue", "font-size": "16px"}
df_styled = df.style.set_properties(**properties)
st.dataframe(df_styled)
with st.expander("See Glossary of tags"):
st.write('''
'**text**': ['entity extracted from your text data']
'**score**': ['accuracy score; how accurately a tag has been assigned to a given entity']
'**label**': ['label (tag) assigned to a given extracted entity']
'**start**': ['index of the start of the corresponding entity']
'**end**': ['index of the end of the corresponding entity']
''')
if df is not None:
fig = px.treemap(df, path=[px.Constant("all"), 'text', 'label'],
values='score', color='label')
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
st.subheader("Tree map", divider = "red")
st.plotly_chart(fig)
if comet_initialized:
experiment.log_figure(figure=fig, figure_name="entity_treemap")
if df is not None:
value_counts1 = df['label'].value_counts()
df1 = pd.DataFrame(value_counts1)
final_df = df1.reset_index().rename(columns={"index": "label"})
col1, col2 = st.columns(2)
with col1:
fig1 = px.pie(final_df, values='count', names='label', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted labels')
fig1.update_traces(textposition='inside', textinfo='percent+label')
st.subheader("Pie Chart", divider = "red")
st.plotly_chart(fig1)
if comet_initialized:
experiment.log_figure(figure=fig1, figure_name="label_pie_chart")
with col2:
fig2 = px.bar(final_df, x="count", y="label", color="label", text_auto=True, title='Occurrences of predicted labels')
st.subheader("Bar Chart", divider = "red")
st.plotly_chart(fig2)
if comet_initialized:
experiment.log_figure(figure=fig2, figure_name="label_bar_chart")
dfa = pd.DataFrame(
data={
'text': ['entity extracted from your text data'], 'score': ['accuracy score; how accurately a tag has been assigned to a given entity'], 'label': ['label (tag) assigned to a given extracted entity'],
'start': ['index of the start of the corresponding entity'],
'end': ['index of the end of the corresponding entity'],
})
buf = io.BytesIO()
with zipfile.ZipFile(buf, "w") as myzip:
myzip.writestr("Summary of the results.csv", df.to_csv(index=False))
myzip.writestr("Glossary of tags.csv", dfa.to_csv(index=False))
if comet_initialized:
myzip.writestr("Summary of the results_glossary_combined.csv", pd.concat([df, dfa]).to_csv(index=False))
with stylable_container(
key="download_button",
css_styles="""button { background-color: yellow; border: 1px solid black; padding: 5px; color: black; }""",
):
st.download_button(
label="Download zip file",
data=buf.getvalue(),
file_name="zip file.zip",
mime="application/zip",
)
if comet_initialized:
experiment.log_asset(buf.getvalue(), file_name="downloadable_results.zip")
st.divider()
if comet_initialized:
experiment.end() |