Spaces:
Sleeping
Sleeping
File size: 3,559 Bytes
d06496c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
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.")
|