stream_lit_plotly.py

import os
from collections import Counter
import streamlit as st
import pandas as pd
import plotly.express as px

# Streamlit app title
# st.title('Interactive Tag Frequency Visualization')

# File uploader to select folder
folder_path = "/home/caimera-prod/eye_tagged_data"

if folder_path:
    # Initialize a Counter to count tag frequency
    tag_counter = Counter()

    # Iterate through each .txt file in the folder
    for file_name in os.listdir(folder_path):
        if file_name.endswith('.txt'):
            file_path = os.path.join(folder_path, file_name)
            with open(file_path, 'r') as file:
                tags = file.read().strip().split(',')
                # Clean and count each tag
                tags = [tag.strip().lower() for tag in tags]
                tag_counter.update(tags)

    # Convert the Counter to a DataFrame for better display
    tag_data = pd.DataFrame(tag_counter.items(), columns=['Tag', 'Count'])
    tag_data = tag_data.sort_values(by='Count', ascending=False).reset_index(drop=True)

    # Display the DataFrame as a table in Streamlit
    st.subheader('Tag Frequency Table')
    st.dataframe(tag_data)

    # Create an interactive bar chart using Plotly
    st.subheader('Interactive Tag Frequency Bar Chart')
    fig = px.bar(tag_data, x='Tag', y='Count', title='Tag Frequency', labels={'Count': 'Frequency'}, height=600)
    fig.update_layout(xaxis_title='Tags', yaxis_title='Frequency')
    st.plotly_chart(fig)