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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import altair as alt | |
| from PIL import Image | |
| from embeddings.embeddings import load_model | |
| from sentence_transformers import util | |
| # Create sample data | |
| data = pd.DataFrame({ | |
| 'Name': ['Alice', 'Bob', 'Charlie', 'David'], | |
| 'Age': [25, 30, 22, 35] | |
| }) | |
| # Sample PNG file | |
| image = Image.open('plots\clusters.png') | |
| # Sample HTML chart | |
| chart_data = pd.read_csv(r"data\top_cluster_dataset.csv",dtype={'Headline': str, 'x': np.float64, 'y': np.float64, 'labels': str}) | |
| # Create a Streamlit app | |
| st.set_page_config(page_title="Sample Webpage", page_icon=":bar_chart:") | |
| # Define tabs | |
| tabs = ["Search", "Clustering Results"] | |
| selected_tab = st.sidebar.radio("Select a Tab", tabs) | |
| # Main content | |
| if selected_tab == "Search": | |
| sample_sentences = chart_data['Headline'].sample(10, random_state=1).tolist() | |
| st.title("Calculate Sentences Similarity") | |
| # select model to use dropdown | |
| st.subheader("Select a model to use") | |
| model_list = ["Ransaka/SinhalaRoberta","keshan/SinhalaBERTo"] | |
| selected_model = st.selectbox("Select Model", model_list) | |
| model = load_model(selected_model) | |
| sentence1 = st.text_input("Enter Sentence 1", "") | |
| sentence2 = st.text_input("Enter Sentence 2", "") | |
| if sentence1 and sentence2: | |
| # add button to calculate similarity | |
| if st.button("Calculate Similarity"): | |
| with st.spinner('Calculating Similarity...'): | |
| # Calculate similarity | |
| similarity = util.pytorch_cos_sim(model.encode(sentence1), model.encode(sentence2))[0][0] | |
| if similarity > 0.7: | |
| st.success(f"Sentences are similar (Score: {similarity:.3f})") | |
| elif similarity > 0.5: | |
| st.warning(f"Sentences are somewhat similar (Score: {similarity:.3f})") | |
| else: | |
| st.error(f"Sentences are not similar (Score: {similarity:.3f})") | |
| else: | |
| st.write("Enter two sentences to calculate similarity. Or start with sample sentences below.") | |
| # change radio button to randomize sentences and show sample sentences | |
| if st.button("Randomize Sentences"): | |
| sample_sentences = chart_data['Headline'].sample(10).tolist() | |
| for sentence in sample_sentences: | |
| # show sample sentences in small font | |
| st.write(sentence) | |
| elif selected_tab == "Clustering Results": | |
| st.title("Clustering Results Tab") | |
| # Display PNG image | |
| st.subheader("Static PNG File") | |
| st.image(image, use_column_width=False, caption='Static PNG File',width=750) | |
| altair_chart = alt.Chart(chart_data).mark_circle().encode( | |
| x='x', | |
| y='y', | |
| color='labels', | |
| tooltip='Headline' | |
| ).properties( | |
| width=750, | |
| height=500 | |
| ).interactive() | |
| # Display chart | |
| st.subheader("Interactive Chart for top clusters") | |
| st.altair_chart(altair_chart, use_container_width=False, theme="streamlit") | |
| # Dropdown functionality to update DataFrame | |
| st.subheader("Select a cluster") | |
| unique_clusters = chart_data['labels'].unique().tolist() | |
| selected_value = st.selectbox("Select Value", unique_clusters) | |
| # Filter and display results based on selected cluster | |
| if selected_value: | |
| filtered_data = chart_data[chart_data['labels'].str.contains(selected_value, case=False)].sample(10)[['Headline']].reset_index(drop=True) | |
| st.dataframe(filtered_data,width=750) | |
| else: | |
| st.write("Select a cluster to display results.") | |