Upload 3 files

Add visualisation of some data analytics

analysis_results.json

@@ -0,0 +1,763 @@
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+}

app.py

@@ -1,43 +1,63 @@
 import streamlit as st
 import pandas as pd
+import json
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
 # Set page config
-st.set_page_config(layout="wide", page_title="Macrocosmos HF Dataset Explorer")
+st.set_page_config(layout="wide", page_title="Macrocosmos HF Dataset Explorer", page_icon="🌌")
 
-# Custom CSS
+# Custom CSS (keep existing styles)
 st.markdown("""
 <style>
     .stApp {
         max-width: 1200px;
         margin: 0 auto;
+        font-family: 'Helvetica Neue', Arial, sans-serif;
     }
-    .st-emotion-cache-1r6slb0 {
-        background-color: #f0f2f6;
+    .cta-container {
+        background-color: #f0f8ff;
         border-radius: 10px;
         padding: 20px;
-        margin-bottom: 20px;
+        margin-top: 30px;
+        margin-bottom: 30px;
+        border: 2px solid #1e90ff;
+        text-align: center;
     }
-    .st-emotion-cache-1wivap2 {
-        background-color: #ffffff;
-        border-radius: 10px;
-        padding: 20px;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    .cta-title {
+        color: #1e90ff;
+        font-size: 24px;
+        font-weight: bold;
+        margin-bottom: 10px;
     }
-    .company-logo {
-        max-width: 200px;
+    .cta-description {
+        color: #333;
+        font-size: 16px;
         margin-bottom: 20px;
     }
+    .stButton > button {
+        background-color: #1e90ff;
+        color: white;
+        font-size: 18px;
+        font-weight: bold;
+        padding: 10px 24px;
+        border-radius: 5px;
+        border: none;
+        transition: all 0.3s ease;
+    }
+    .stButton > button:hover {
+        background-color: #0066cc;
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    }
 </style>
 """, unsafe_allow_html=True)
 
-# Company logo
-st.image("macrocosmos-black.png", use_column_width=False, width=200)
-
 # Title and description
-st.title("🤗 Hugging Face Large Dataset Explorer")
+st.title("🌌 Macrocosmos HF Dataset Explorer")
 st.markdown("Explore massive datasets hosted on Hugging Face, totaling approximately 100GB of data.")
 
 # Function to load dataset information
+@st.cache_data
 def load_datasets():
     return [
         {"Source": "X", "DataSet repo link": "https://huggingface.co/datasets/icedwind/x_dataset_19", "Number of rows": "332 MLN"},
@@ -57,12 +77,14 @@ datasets = load_datasets()
 df = pd.DataFrame(datasets)
 
 # Display statistics
-col1, col2 = st.columns(2)
+col1, col2, col3 = st.columns(3)
 with col1:
     total_rows = sum(float(str(rows).split()[0].replace(',', '')) for rows in df['Number of rows'])
-    st.metric("Total Rows", f"{total_rows:.2f} Million")
+    st.metric("Total Rows", f"{total_rows:.2f}M")
 with col2:
     st.metric("Total Datasets", len(df))
+with col3:
+    st.metric("Total Data Size", "100GB+")
 
 # Display the dataset table
 st.subheader("Dataset Overview")
@@ -77,8 +99,91 @@ st.dataframe(
     use_container_width=True
 )
 
-# Add a note about the size of the datasets
-st.info("⚠️ These datasets are very large, totaling approximately 100GB. They are not available for direct download through this interface. Please visit the individual dataset links for more information on accessing the data.")
+# Call-to-action section with styled button
+st.markdown("""
+<div class="cta-container">
+    <div class="cta-title">🔍 Explore Dataset Insights</div>
+    <div class="cta-description">
+        Dive deep into the rich analytics of our dataset. Uncover trends, distributions, and key metrics that will enhance your understanding and guide your research.
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+# Centered button
+col1, col2, col3 = st.columns([1,2,1])
+with col2:
+    show_analysis = st.button("Reveal Dataset Analysis", use_container_width=True)
+
+# Display dataset analysis if the button was clicked
+if show_analysis:
+    # Load analysis results
+    @st.cache_data
+    def load_analysis_results():
+        with open('analysis_results.json', 'r') as f:
+            return json.load(f)
+
+    analysis_results = load_analysis_results()
+
+    st.subheader("Analysis of a Sample Reddit Dataset")
+    st.write("This analysis is based on a sample from one of the Reddit datasets.")
+
+    # Display Dataset Structure
+    st.subheader("Dataset Structure")
+    structure = analysis_results['structure']
+    col1, col2, col3, col4 = st.columns(4)
+    col1.metric("Total Partitions", structure['total_partitions'])
+    col2.metric("Total Rows", f"{structure['total_rows']:,}")
+    col3.metric("Number of Columns", len(structure['columns']))
+    col4.metric("Date Range", f"{structure['date_range'][0]} to {structure['date_range'][1]}")
+
+    with st.expander("Show Columns"):
+        st.write(", ".join(structure['columns']))
+
+    # Display Top Communities
+    st.subheader("Top Communities")
+    communities_df = pd.DataFrame(analysis_results['communities'])
+    fig = go.Figure(data=[go.Bar(
+        x=communities_df['communityName'],
+        y=communities_df['count'],
+        text=communities_df['percentage'].apply(lambda x: f'{x:.2%}'),
+        textposition='auto',
+        marker_color='#1e88e5'
+    )])
+    fig.update_layout(title_text='Top Communities Distribution')
+    fig.update_traces(marker_line_color='rgb(8,48,107)', marker_line_width=1.5, opacity=0.6)
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Display Time Distribution
+    st.subheader("Time Distribution")
+    time_df = pd.DataFrame(analysis_results['time_distribution'])
+    time_df['date'] = pd.to_datetime(time_df['date'])
+    fig = go.Figure(data=[go.Scatter(x=time_df['date'], y=time_df['count'], mode='lines+markers')])
+    fig.update_layout(title_text='Posts Over Time')
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Display Sentiment Distribution
+    st.subheader("Sentiment Distribution")
+    sentiment_df = pd.DataFrame(analysis_results['sentiment_distribution'])
+    fig = go.Figure(data=[go.Pie(labels=sentiment_df['sentiment'], values=sentiment_df['count'], textinfo='percent+label')])
+    fig.update_layout(title_text='Sentiment Distribution')
+    fig.update_traces(marker=dict(colors=['#4CAF50', '#FFC107', '#F44336']))
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Display Data Type Distribution
+    st.subheader("Data Type Distribution")
+    data_type_df = pd.DataFrame(analysis_results['data_type_distribution'])
+    fig = go.Figure(data=[go.Pie(labels=data_type_df['dataType'], values=data_type_df['count'], textinfo='percent+label')])
+    fig.update_layout(title_text='Data Type Distribution')
+    fig.update_traces(marker=dict(colors=['#2196F3', '#FF9800']))
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Display Top Topics
+    st.subheader("Top Topics")
+    topics_df = pd.DataFrame(analysis_results['top_topics'])
+    st.dataframe(topics_df, use_container_width=True)
+
+    # Display Average Text Length
+    st.metric("Average Text Length", f"{analysis_results['avg_text_length']:.2f} characters")
 
 # Add instructions for using the datasets
 st.subheader("How to Use These Datasets")
@@ -99,4 +204,4 @@ st.markdown("""
 
 # Footer
 st.markdown("---")
-st.markdown("Created by Macrocosmos with ❤️ ")
+st.markdown("Created by Macrocosmos with ❤️")

requirements.txt

@@ -1,3 +1,5 @@
 pandas
 streamlit
 streamlit-card
+plotly
+streamlit-plotly-events