Update app.py

app.py

@@ -47,34 +47,79 @@ elif option == "Plots":
     # 1. Job Category Distribution Bar Plot
     st.subheader("Job Category Distribution")
     category_counts = jobs_df[category_column].value_counts()
-    fig1, ax1 = plt.subplots()
-    ax1.bar(category_counts.index, category_counts.values)
+
+    # Create the bar plot
+    fig1, ax1 = plt.subplots(figsize=(10, 6))  # Set figure size
+    bars = ax1.bar(category_counts.index, category_counts.values, color='skyblue')
+
     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")
+
+    # Add labels on the right side of the bars
+    for bar in bars:
+        yval = bar.get_height()
+        ax1.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(fig1)
 
+
     # 2. Pie Chart for Category Distribution
     st.subheader("Job Category Proportions")
-    fig2, ax2 = plt.subplots(figsize=(10, 25))
-    ax2.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140)
-    ax2.axis('equal')  # Equal aspect ratio ensures the pie chart is circular.
+    fig2, ax2 = plt.subplots(figsize=(10, 10))  # Adjust the size as needed
+    wedges, texts, autotexts = ax2.pie(
+      category_counts, 
+      labels=category_counts.index, 
+      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
+   # 4. Top Job Titles Bar Plot
     st.subheader("Top Job Titles")
     top_job_titles = jobs_df[job_title_column].value_counts().head(10)
-    fig4, ax4 = plt.subplots()
-    ax4.bar(top_job_titles.index, top_job_titles.values)
+
+# 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()))