app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from streamlit.components.v1 import html
import nbformat
from nbconvert import HTMLExporter
from wordcloud import WordCloud

# Load the CSV data
file_path = 'category upwork jobs (1).csv'
jobs_df = pd.read_csv(file_path)

# Adjust column names as per the CSV
category_column = 'category'  # Replace with the actual column name for category
job_title_column = 'title'  # Replace with the actual column name for job title
description_column = 'description'
key_column = 'key'
date_column = 'Date'

# Sidebar menu
st.sidebar.title("Navigation")
option = st.sidebar.radio("Go to", ["Home", "Plots", "Notebook","Download Datasets"])
# Home Page: Display data with category filter
if option == "Home":
    st.title("Jobs Dashboard")

    # Filter Jobs by Category
    st.sidebar.header("Filter Jobs by Category")
    categories = jobs_df[category_column].unique()  # Extract unique categories
    selected_category = st.sidebar.selectbox("Choose a category:", categories)

    # Filter jobs based on the selected category
    filtered_jobs = jobs_df[jobs_df[category_column] == selected_category]

    # Display filtered jobs with additional columns
    st.write(f"Showing jobs in category: **{selected_category}**")
    st.dataframe(filtered_jobs[['title','key','description','date','extracted_budget']])

    # Optional: Show a count of jobs in the selected category
    st.write(f"Total jobs in this category: {len(filtered_jobs)}")

# Plots Page: Display category distribution plot
elif option == "Plots":
    st.title("Job Visualization")

    # 1. Job Category Distribution Bar Plot
    st.subheader("Job Category Distribution")
    category_counts = jobs_df[category_column].value_counts()

    # Create a color palette for the bars
    colors = plt.cm.Paired.colors  # You can choose any colormap you prefer

    # Create the bar plot
    fig1, ax1 = plt.subplots(figsize=(10, 6))  # Set figure size
    bars = ax1.bar(range(len(category_counts)), category_counts.values, color=colors[:len(category_counts)])

    ax1.set_xlabel("Job Category")
    ax1.set_ylabel("Number of Jobs")
    ax1.set_title("Distribution of Jobs Across Categories")
    plt.xticks(rotation=45, ha="right")

    # Create a legend with job categories and their counts
    legend_labels = [f"{category}: {int(count)}" for category, count in zip(category_counts.index, category_counts.values)]
    ax1.legend(bars, legend_labels, title="Job Categories", loc="upper left", bbox_to_anchor=(1, 1))  # Position the legend

    # No text labels on the bars and no labels above the bars

    # Adjust layout to give space for the legend
    plt.subplots_adjust(right=0.75)  # Adjust right margin for legend space

    st.pyplot(fig1)




    # 2. Pie Chart for Category Distribution
    st.subheader("Job Category Proportions")
    fig2, ax2 = plt.subplots(figsize=(10, 10))  # Adjust the size as needed
    wedges, texts, autotexts = ax2.pie(
      category_counts,  
      autopct='%1.1f%%', 
      startangle=140,
      colors=plt.cm.Paired.colors  # Optional: Change colors for better aesthetics
     )

    ax2.axis('equal')  # Equal aspect ratio ensures the pie chart is circular.

    # Customize the text labels
    for text in texts: 
       text.set_fontsize(10)  # Adjust font size for labels
    for autotext in autotexts: 
       autotext.set_color('white')  # Change the color of the percentage text
       autotext.set_fontsize(10)  # Adjust font size for percentage

    # Add a legend to the right of the pie chart
    ax2.legend(wedges, category_counts.index, title="Job Categories", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))

    st.pyplot(fig2)

    

    # 4. Top Job Titles Bar Plot
    st.subheader("Top Job Titles")
    top_job_titles = jobs_df[job_title_column].value_counts().head(10)

    # Create the bar plot
    fig4, ax4 = plt.subplots(figsize=(10, 6))  # Adjust figure size for better readability
    bars = ax4.bar(top_job_titles.index, top_job_titles.values, color='lightcoral')

    ax4.set_xlabel("Job Title")
    ax4.set_ylabel("Count")
    ax4.set_title("Top 10 Job Titles")
    plt.xticks(rotation=45, ha="right")

    # Add labels on the right side of the bars
    for bar in bars:
        yval = bar.get_height()
        ax4.text(bar.get_x() + bar.get_width() / 2, yval, int(yval),ha='center', va='bottom', color='black')  # Centered above the bar

    # Adjust layout to give space for labels
    plt.subplots_adjust(right=0.85)  # Adjust right margin for space

    st.pyplot(fig4)


    # 5. Word Cloud for Job Descriptions
    st.subheader("Word Cloud for Job Descriptions")
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(jobs_df[description_column].dropna()))
    fig5, ax5 = plt.subplots(figsize=(10, 5))
    ax5.imshow(wordcloud, interpolation='bilinear')
    ax5.axis('off')  # Turn off the axis
    st.pyplot(fig5)

elif option == "Notebook":
    st.title("Jupyter Notebook")

    # Load and convert the notebook to HTML
    notebook_path = 'upwork_dashboard.ipynb'  # Update with the actual path to your notebook
    with open(notebook_path) as f:
        notebook_content = nbformat.read(f, as_version=4)

    # Create a new markdown cell with the link to the Google Colab notebook
    colab_link = "[Open in Google Colab](https://colab.research.google.com/drive/1qoTldQ-Kr6DgePRNYgdlQqqHq5JQax0h?usp=sharing)"
    new_cell = nbformat.v4.new_markdown_cell(colab_link)

    # Insert the new cell at the top of the notebook
    notebook_content.cells.insert(0, new_cell)

    # Export the notebook to HTML
    html_exporter = HTMLExporter()
    html_exporter.exclude_input = False  # Include code cells in the notebook display
    notebook_html, _ = html_exporter.from_notebook_node(notebook_content)

    # Display the notebook HTML in Streamlit
    html(notebook_html, height=800, scrolling=True)
    # Notebook Page: Render the Jupyter Notebook
elif option == "Download Datasets":
    st.title("Download Datasets")
    d=pd.read_csv("category upwork jobs (1).csv")
    d1=pd.read_csv("jobs.csv")
    # Download links for the datasets
    st.markdown("Click the links below to download the datasets:")
    
    # Link for category upwork jobs dataset
    with open("category upwork jobs.csv", 'rb') as f:
        st.download_button(
            label="Download Category Upwork Jobs Dataset",
            data=f,
            file_name='category_upwork_jobs.csv',
            mime='text/csv'
        )
    st.dataframe(d)
    # Link for the original dataset
    with open("jobs.csv", 'rb') as f:
        st.download_button(
            label="Download previous Dataset",
            data=f,
            file_name='previous Dataset data.csv',
            mime='text/csv'
        )
        st.dataframe(d1)