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import streamlit as st
import pandas as pd
from transformers import pipeline
from datasets import load_dataset
import torch
import os
def load_css(file_name):
current_dir = os.path.dirname(__file__)
css_file_path = os.path.join(current_dir, file_name)
with open(css_file_path) as f:
st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
load_css("style.css")
st.set_page_config(
page_title="Intent Classification in Banking",
page_icon="🤖",
layout="centered"
)
@st.cache_resource
def load_model_and_mapping():
model_path = "saved_models/distilbert_distilbert-base-uncased-finetuned-banking77"
device = 0 if torch.cuda.is_available() else -1
classifier = pipeline("text-classification", model=model_path, device=device)
raw_datasets = load_dataset("mteb/banking77")
df_train = raw_datasets['train'].to_pandas()
label_mapping_df = df_train[['label', 'label_text']].drop_duplicates().reset_index(drop=True)
return classifier, label_mapping_df
classifier, label_mapping_df = load_model_and_mapping()
st.title("Intent Classification in Banking")
st.markdown(
"""
This is a demo for a language model that has been fine-tuned to classify 77 different types of customer requests in the banking sector.
"""
)
with st.form("intent_form"):
user_input = st.text_area("Please enter your request here:", "", height=100)
submitted = st.form_submit_button("Classify Intent")
if submitted and user_input:
with st.spinner('The model is analyzing...'):
predictions = classifier(user_input, top_k=5)
mapped_predictions = []
for pred in predictions:
label_id = int(pred['label'].split('_')[1])
matched_row = label_mapping_df[label_mapping_df['label'] == label_id]
if not matched_row.empty:
label_text = matched_row['label_text'].iloc[0]
else:
label_text = "Cannot find label"
mapped_predictions.append({'label': label_text, 'score': pred['score']})
top_prediction = mapped_predictions[0]
st.success(f"**The top predicted intent is:** `{top_prediction['label']}`")
st.metric(label="With confidence", value=f"{top_prediction['score']:.2%}")
st.markdown("---")
st.subheader("Other possibilities:")
df = pd.DataFrame(mapped_predictions)
df = df.rename(columns={'label': 'Intent', 'score': 'Confidence'})
df['Confidence'] = df['Confidence'].apply(lambda x: f"{x:.2%}")
st.dataframe(
df,
column_config={
"Confidence": st.column_config.ProgressColumn(
"Confidence",
format="%.3f",
min_value=0,
max_value=1,
),
},
hide_index=True,
width='stretch'
)
elif submitted and not user_input:
st.warning("Please enter a request to classify.") |