UI

app.py

@@ -1,5 +1,4 @@
 import logging
-import os
 from typing import Optional, Tuple
 
 import gradio as gr
@@ -44,7 +43,7 @@ def initialize_system():
             _processor.raw_data["students_yearly"]
         )
 
-        logger.info("✓ System initialized successfully")
+        logger.info("System initialized successfully")
         return True
     except Exception as e:
         logger.error(f"Failed to initialize system: {e}", exc_info=True)
@@ -75,13 +74,13 @@ def generate_predictions(
     try:
         if semester not in [1, 2]:
             return (
-                "❌ Error: Semester must be 1 (Ganjil) or 2 (Genap)",
+                "Error: Semester must be 1 (Ganjil) or 2 (Genap)",
                 None,
                 None,
             )
 
         if year < 2020 or year > 2030:
-            return "❌ Error: Year must be between 2020 and 2030", None, None
+            return "Error: Year must be between 2020 and 2030", None, None
 
         if (
             _config is None
@@ -91,7 +90,7 @@ def generate_predictions(
             or _elective_codes is None
         ):
             return (
-                "❌ Error: System not initialized. Please restart the app.",
+                "Error: System not initialized. Please restart the app.",
                 None,
                 None,
             )
@@ -109,11 +108,11 @@ def generate_predictions(
 
         if has_actual_data:
             logger.info(
-                f"✓ Found actual enrollment data for {year} Semester {semester} - will compare predictions vs actual"
+                f"Found actual enrollment data for {year} Semester {semester} - will compare predictions vs actual"
             )
         else:
             logger.info(
-                f"ℹ No actual data for {year} Semester {semester} - generating future predictions"
+                f"No actual data for {year} Semester {semester} - generating future predictions"
             )
 
         if _backtest_metrics is None:
@@ -182,80 +181,78 @@ def generate_predictions(
                 total_actual = courses_with_actual["actual_enrollment"].sum()
                 total_predicted = courses_with_actual["predicted_enrollment"].sum()
 
-                summary = f"""## 📊 {year} Semester {semester_name} - Validation Against Actual Data
+                summary = f"""## {year} Semester {semester_name} - Validation Against Actual Data
 
-<div style='background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 20px; border-radius: 10px; color: white; margin-bottom: 20px;'>
-<h3 style='margin-top: 0; color: white;'>✅ Historical Validation</h3>
-<p style='margin-bottom: 5px;'><strong>Status:</strong> Comparing predictions against actual enrollment data</p>
-<p style='margin-bottom: 0;'><strong>Courses Validated:</strong> {len(courses_with_actual)} courses</p>
-</div>
+### Historical Validation
+- **Status:** Comparing predictions against actual enrollment data
+- **Courses Validated:** {len(courses_with_actual)} courses
 
-### 📈 Prediction Accuracy
+### Prediction Accuracy
 | Metric | Value |
 |--------|-------|
-| **Prediction MAE** | {comparison_mae:.2f} students |
-| **Prediction RMSE** | {comparison_rmse:.2f} students |
-| **Overall Accuracy** | {(1 - abs(total_predicted - total_actual) / total_actual) * 100:.1f}% |
+| Prediction MAE | {comparison_mae:.2f} students |
+| Prediction RMSE | {comparison_rmse:.2f} students |
+| Overall Accuracy | {(1 - abs(total_predicted - total_actual) / total_actual) * 100:.1f}% |
 
-### 📊 Enrollment Summary
+### Enrollment Summary
 | Category | Actual | Predicted | Difference |
 |----------|--------|-----------|------------|
-| **Total Students** | {int(total_actual)} | {int(total_predicted)} | {int(total_predicted - total_actual):+d} |
-| **Courses Analyzed** | {len(courses_with_actual)} | {len(courses_with_actual)} | - |
+| Total Students | {int(total_actual)} | {int(total_predicted)} | {int(total_predicted - total_actual):+d} |
+| Courses Analyzed | {len(courses_with_actual)} | {len(courses_with_actual)} | - |
 
-### 🎯 Model Baseline (Cross-Validation)
-- **Backtest MAE**: {metrics["mae"]:.2f} students
-- **Backtest RMSE**: {metrics["rmse"]:.2f} students
-
-### 💡 Recommendation Summary
-- **Courses Recommended to Open**: {total_to_open}
-- **Total Quota Needed**: {total_seats} seats
-
----
-💡 **Tip:** Scroll down to see the detailed comparison table showing prediction accuracy for each course.
+### Model Baseline (Cross-Validation)
+| Metric | Value |
+|--------|-------|
+| Backtest MAE | {metrics["mae"]:.2f} students |
+| Backtest RMSE | {metrics["rmse"]:.2f} students |
+
+### Recommendation Summary
+| Item | Value |
+|------|-------|
+| Courses Recommended to Open | {total_to_open} |
+| Total Quota Needed | {total_seats} seats |
 """
             else:
-                summary = f"""## 📊 {year} Semester {semester_name}
-
-<div style='background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 20px; border-radius: 10px; color: white; margin-bottom: 20px;'>
-<h3 style='margin-top: 0; color: white;'>⚠️ Limited Validation Data</h3>
-<p style='margin-bottom: 0;'>Actual semester data exists, but no matching elective courses found for comparison</p>
-</div>
+                summary = f"""## {year} Semester {semester_name}
 
-### 🎯 Model Performance (Backtest)
-- **MAE**: {metrics["mae"]:.2f} students
-- **RMSE**: {metrics["rmse"]:.2f} students
+### Limited Validation Data
+Actual semester data exists, but no matching elective courses found for comparison.
 
-### 💡 Recommendations
-- **Courses to Open**: {total_to_open}
-| **Total Seats** | {total_seats} |
-| **Estimated Total Students** | {int(predictions["predicted_enrollment"].sum())} |
+### Model Performance (Backtest)
+| Metric | Value |
+|--------|-------|
+| MAE | {metrics["mae"]:.2f} students |
+| RMSE | {metrics["rmse"]:.2f} students |
+
+### Recommendations
+| Item | Value |
+|------|-------|
+| Courses to Open | {total_to_open} |
+| Total Seats | {total_seats} |
+| Estimated Total Students | {int(predictions["predicted_enrollment"].sum())} |
 """
         else:
-            summary = f"""## 📊 {year} Semester {semester_name} - Future Prediction
+            summary = f"""## {year} Semester {semester_name} - Future Prediction
 
-<div style='background: linear-gradient(135deg, #fa709a 0%, #fee140 100%); padding: 20px; border-radius: 10px; color: white; margin-bottom: 20px;'>
-<h3 style='margin-top: 0; color: white;'>🔮 Forward-Looking Forecast</h3>
-<p style='margin-bottom: 5px;'><strong>Status:</strong> No actual enrollment data available</p>
-<p style='margin-bottom: 0;'><strong>Type:</strong> Predictive forecast based on historical trends</p>
-</div>
+### Forward-Looking Forecast
+- **Status:** No actual enrollment data available
+- **Type:** Predictive forecast based on historical trends
 
-### 🎯 Model Performance (Historical Backtest)
+### Model Performance (Historical Backtest)
 | Metric | Value |
 |--------|-------|
-| **MAE** | {metrics["mae"]:.2f} students |
-| **RMSE** | {metrics["rmse"]:.2f} students |
+| MAE | {metrics["mae"]:.2f} students |
+| RMSE | {metrics["rmse"]:.2f} students |
 
-### 📋 Forecast Summary
+### Forecast Summary
 | Category | Value |
 |----------|-------|
-| **Courses to Open** | {total_to_open} |
-| **Total Seats** | {total_seats} |
-| **Estimated Total Students** | {int(predictions["predicted_enrollment"].sum())} |
+| Courses to Open | {total_to_open} |
+| Total Seats | {total_seats} |
+| Estimated Total Students | {int(predictions["predicted_enrollment"].sum())} |
 """
 
         # Prepare all predictions display
-        # All predictions
         all_predictions_display = predictions[
             [
                 "kode_mk",
@@ -281,9 +278,9 @@ def generate_predictions(
         ].round(1)
         all_predictions_display["Quota"] = all_predictions_display["Quota"].astype(int)
 
-        # Add status emoji
+        # Map status to plain text
         all_predictions_display["Status"] = all_predictions_display["Status"].map(
-            {"BUKA": "✅ OPEN", "TUTUP": "❌ CLOSE"}
+            {"BUKA": "OPEN", "TUTUP": "CLOSE"}
         )
 
         all_predictions_display = all_predictions_display.sort_values(
@@ -369,22 +366,22 @@ def generate_predictions(
                 )
 
                 logger.info(
-                    f"✓ Comparison table created with {len(comparison_display)} courses"
+                    f"Comparison table created with {len(comparison_display)} courses"
                 )
             else:
                 logger.warning(
-                    "⚠️ Actual data exists but no matching courses found for comparison"
+                    "Actual data exists but no matching courses found for comparison"
                 )
                 logger.warning(f"Predicted courses: {predictions['kode_mk'].tolist()}")
                 logger.warning(f"Actual courses: {actual_data['kode_mk'].tolist()}")
 
         logger.info(
-            f"✓ Predictions generated successfully (comparison_display: {comparison_display is not None})"
+            f"Predictions generated successfully (comparison_display: {comparison_display is not None})"
         )
         return summary, all_predictions_display, comparison_display
 
     except Exception as e:
-        error_msg = f"❌ Error generating predictions: {str(e)}"
+        error_msg = f"Error generating predictions: {str(e)}"
         logger.error(error_msg, exc_info=True)
         return error_msg, None, None
 
@@ -395,37 +392,42 @@ def get_data_info() -> str:
 
     try:
         if _processor is None or _config is None:
-            return "❌ System not initialized"
+            return "System not initialized"
 
         courses = _processor.raw_data.get("courses")
         students = _processor.raw_data.get("students_yearly")
 
         if courses is None or students is None:
-            return "❌ Data not loaded"
+            return "Data not loaded"
 
         # Get elective courses
         elective_courses = courses[courses["kategori_mk"] == "P"]
 
         info = f"""
-## 📁 Dataset Information
+## Dataset Information
 
 ### Course Catalog
-- **Total Courses**: {len(courses)}
-- **Elective Courses**: {len(elective_courses)}
-- **Mandatory Courses**: {len(courses) - len(elective_courses)}
+| Item | Value |
+|------|-------|
+| Total Courses | {len(courses)} |
+| Elective Courses | {len(elective_courses)} |
+| Mandatory Courses | {len(courses) - len(elective_courses)} |
 
 ### Student Population
-- **Years Available**: {students["thn"].min()} - {students["thn"].max()}
-- **Total Records**: {len(students)}
+| Item | Value |
+|------|-------|
+| Years Available | {students["thn"].min()} - {students["thn"].max()} |
+| Total Records | {len(students)} |
 
 ### Data Source
-- File: `{_config.data.FILE_PATH}`
-- Last Updated: October 8, 2025
+| Item | Value |
+|------|-------|
+| File | {_config.data.FILE_PATH} |
 """
         return info
 
     except Exception as e:
-        return f"❌ Error getting data info: {str(e)}"
+        return f"Error getting data info: {str(e)}"
 
 
 # Initialize system at startup
@@ -437,121 +439,63 @@ if not init_success:
 
 # Create Gradio Interface
 with gr.Blocks(title="SKS Enrollment Predictor") as demo:
-    # Show disclaimer banner if using demo data
-    if os.getenv("DEMO_MODE", "false").lower() == "true":
-        gr.Markdown(
-            """
-            <div style='padding: 15px; background-color: #fff3cd; border-left: 5px solid #ffc107; margin-bottom: 20px;'>
-            <h3 style='margin-top: 0; color: #856404;'>⚠️ Demo Version - Anonymized Data</h3>
-            <p style='margin-bottom: 0; color: #856404;'>
-            This demonstration uses <strong>anonymized enrollment data</strong> to protect student privacy.
-            All predictions and functionality are identical to the production version.
-            </p>
-            <details style='margin-top: 10px;'>
-            <summary style='cursor: pointer; color: #856404;'><strong>Changes made for demo:</strong></summary>
-            <ul style='color: #856404;'>
-                <li>Student IDs replaced with anonymous codes (STU000001, STU000002, ...)</li>
-                <li>Population counts have ±3% random noise added</li>
-                <li>Course information and enrollment patterns fully preserved</li>
-            </ul>
-            </details>
-            </div>
-            """,
-            sanitize_html=False,
-        )
-
-    # Header
-    gr.Markdown(
-        """
-        # 🎓 SKS Course Enrollment Prediction System
-        ### Intelligent forecasting for elective course planning
-        """
-    )
-
-    with gr.Tabs():
-        with gr.Tab("📊 Predictions", id="predictions"):
-            with gr.Row():
-                with gr.Column(scale=1, min_width=300):
-                    gr.Markdown("### 🎯 Select Target Semester")
-
-                    year_input = gr.Number(
-                        label="Year",
-                        value=2025,
-                        precision=0,
-                        minimum=2020,
-                        maximum=2030,
-                    )
-
-                    semester_input = gr.Radio(
-                        choices=[1, 2],
-                        label="Semester",
-                        value=2,
-                        info="1 = Ganjil (Odd) | 2 = Genap (Even)",
-                    )
-
-                    predict_btn = gr.Button(
-                        "🚀 Generate Predictions",
-                        variant="primary",
-                        size="lg",
-                        scale=1,
-                    )
-
-                    gr.Markdown(
-                        """
-                        ---
-                        **💡 Tips:**
-                        - Historical semesters show validation results
-                        - Future semesters show forecasts
-                        - Use filters in tables to find specific courses
-                        """
-                    )
-
-                with gr.Column(scale=2):
-                    summary_output = gr.Markdown(
-                        value="""
-                        <div style='text-align: center; padding: 40px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 10px; color: white;'>
-                        <h2 style='color: white; margin-top: 0;'>👈 Select Parameters</h2>
-                        <p style='margin-bottom: 0;'>Choose a year and semester, then click "Generate Predictions"</p>
-                        </div>
-                        """
-                    )
+    # Clean header
+    gr.Markdown("# Course Enrollment Predictor")
+
+    with gr.Row(equal_height=True):
+        # Left panel - Controls
+        with gr.Column(scale=1, min_width=280):
+            gr.Markdown("#### Target Semester")
+
+            year_input = gr.Number(
+                label="Year",
+                value=2025,
+                precision=0,
+                minimum=2020,
+                maximum=2030,
+            )
 
-            gr.Markdown("---")
+            semester_input = gr.Radio(
+                choices=[("Ganjil (Odd)", 1), ("Genap (Even)", 2)],
+                label="Semester",
+                value=2,
+            )
 
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("### 📋 All Course Predictions")
-                    gr.Markdown(
-                        "*Complete list of all elective courses with recommendations*"
-                    )
-                    all_predictions_output = gr.Dataframe(
-                        label="Predictions",
-                        wrap=True,
-                        interactive=False,
-                    )
+            predict_btn = gr.Button(
+                "Generate Predictions",
+                variant="primary",
+                size="lg",
+            )
 
-            gr.Markdown("---")
+            # Data info section
+            with gr.Accordion("Dataset Info", open=False):
+                data_info_output = gr.Markdown()
+                demo.load(fn=get_data_info, inputs=[], outputs=data_info_output)
 
-            with gr.Accordion(
-                "📈 Prediction vs Actual Comparison", open=False
-            ) as comparison_accordion:
-                comparison_info = gr.Markdown(
-                    value="*This section will show validation data when actual enrollment is available*",
-                )
-                comparison_output = gr.Dataframe(
-                    label="Detailed Comparison",
-                    wrap=True,
-                    interactive=False,
-                )
-
-        with gr.Tab("ℹ️ Data Info", id="info"):
-            gr.Markdown("### 📁 Dataset Information")
+        # Right panel - Results
+        with gr.Column(scale=3):
+            summary_output = gr.Markdown(
+                value="Select year and semester, then click Generate Predictions"
+            )
 
-            data_info_btn = gr.Button("🔄 Refresh Data Info", variant="secondary")
-            data_info_output = gr.Markdown()
+    # Predictions table
+    gr.Markdown("#### Predictions")
+    all_predictions_output = gr.Dataframe(
+        label="",
+        wrap=True,
+        interactive=False,
+    )
 
-            data_info_btn.click(fn=get_data_info, inputs=[], outputs=data_info_output)
-            demo.load(fn=get_data_info, inputs=[], outputs=data_info_output)
+    # Comparison section
+    with gr.Accordion("Validation Results", open=False) as comparison_accordion:
+        comparison_info = gr.Markdown(
+            value="Validation data appears when actual enrollment is available",
+        )
+        comparison_output = gr.Dataframe(
+            label="",
+            wrap=True,
+            interactive=False,
+        )
 
     def update_ui_with_predictions(year, semester):
         """Wrapper to handle UI updates based on whether comparison data exists."""
@@ -566,9 +510,9 @@ with gr.Blocks(title="SKS Enrollment Predictor") as demo:
             return (
                 summary,
                 all_predictions,
-                gr.update(open=True),  # Open accordion
+                gr.update(open=True),
                 gr.update(
-                    value=f"**✅ Validated against {len(comparison)} courses with actual enrollment data**\n\nThe table below shows prediction accuracy for each course:",
+                    value=f"Validated against {len(comparison)} courses",
                 ),
                 gr.update(value=comparison),
             )
@@ -577,9 +521,9 @@ with gr.Blocks(title="SKS Enrollment Predictor") as demo:
             return (
                 summary,
                 all_predictions,
-                gr.update(open=False),  # Keep accordion closed
+                gr.update(open=False),
                 gr.update(
-                    value="*No actual enrollment data available for this semester (future prediction)*",
+                    value="No validation data available for future predictions",
                 ),
                 gr.update(value=None),
             )
@@ -596,27 +540,12 @@ with gr.Blocks(title="SKS Enrollment Predictor") as demo:
         ],
     )
 
-    # Footer
-    gr.Markdown("---")
-    if os.getenv("DEMO_MODE", "false").lower() == "true":
-        gr.Markdown(
-            """
-            <div style='text-align: center; padding: 20px; background: #f8f9fa; border-radius: 10px;'>
-            <p style='margin: 0; color: #666;'>📊 <strong>Demo Version</strong> with Anonymized Data | For Educational Purposes</p>
-            </div>
-            """,
-            sanitize_html=False,
-        )
-    else:
-        gr.Markdown(
-            """
-            <div style='text-align: center; padding: 20px; background: #f8f9fa; border-radius: 10px;'>
-            <p style='margin: 0; color: #666;'>🔒 <strong>Private & Confidential</strong> | For Authorized Use Only</p>
-            </div>
-            """,
-            sanitize_html=False,
-        )
 
 # Launch the app
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, show_error=True)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True,
+    )