Update app.py

app.py

@@ -1,23 +1,100 @@
+
 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
 
 # Load the CSV data
 file_path = 'category upwork jobs.csv'
 jobs_df = pd.read_csv(file_path)
 
-# Sidebar: Select Category
-st.sidebar.header("Filter Jobs by Category")
-categories = jobs_df['category'].unique()  # Extract unique categories
-selected_category = st.sidebar.selectbox("Choose a category:", categories)
+# 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']])
+
+    # 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 Category Distribution")
+    
+    # Count occurrences of each category
+    category_counts = jobs_df[category_column].value_counts()
+
+    # Create a bar plot using matplotlib
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.bar(category_counts.index, category_counts.values)
+    ax.set_xlabel("Job Category")
+    ax.set_ylabel("Number of Jobs")
+    ax.set_title("Distribution of Jobs Across Categories")
+    plt.xticks(rotation=45, ha="right")
+    plt.tight_layout()
+
+    # Display the plot in Streamlit
+    st.pyplot(fig)
+# Notebook Page: Render the Jupyter Notebook
+elif option == "Notebook":
+    st.title("Jupyter Notebook")
 
-# Filter jobs based on the selected category
-filtered_jobs = jobs_df[jobs_df['category'] == selected_category]
+    # 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)
+    
+    html_exporter = HTMLExporter()
+    html_exporter.exclude_input = False  # Include code cells in the notebook display
+    notebook_html, _ = html_exporter.from_notebook_node(notebook_content)
 
-# Main: Display filtered jobs
-st.title("Jobs Dashboard")
-st.write(f"Showing jobs in category: **{selected_category}**")
-#st.dataframe(filtered_jobs[['title']])  # Adjust columns as needed
-st.dataframe(filtered_jobs[['title','key','description','date']])
+    # Display the notebook HTML in Streamlit
+    html(notebook_html, height=800, scrolling=True)
+elif option == "Download Datasets":
+    st.title("Download Datasets")
+    d=pd.read_csv("category upwork jobs.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 Original Dataset",
+            data=f,
+            file_name='origina scraped data.csv',
+            mime='text/csv'
+        )
+        st.dataframe(d1)
 
-# Optional: Show a count of jobs in the selected category
-st.write(f"Total jobs in this category: {len(filtered_jobs)}")