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