Update app.py

app.py

@@ -247,7 +247,7 @@ def analyze_aerodynamics():
 def predict_performance(front_wing, rear_wing, ride_height, suspension, downforce, drag_coeff, track_temp, wind_speed, track_grip):
     """Predict lap time for given setup"""
     if not predictor.is_trained:
-        return "⚠️ Please run the analysis first to train the model!"
+        return "Please run the analysis first to train the model!"
     
     setup_params = [front_wing, rear_wing, ride_height, suspension, downforce, drag_coeff, track_temp, wind_speed, track_grip]
     lap_time = predictor.predict_lap_time(setup_params)
@@ -257,18 +257,18 @@ def predict_performance(front_wing, rear_wing, ride_height, suspension, downforc
 def optimize_car_setup(track_temp, wind_speed, track_grip):
     """Optimize car setup for given conditions"""
     if not predictor.is_trained:
-        return "⚠️ Please run the analysis first to train the model!"
+        return "Please run the analysis first to train the model!"
     
     track_conditions = [track_temp, wind_speed, track_grip]
     result = predictor.optimize_setup(track_conditions)
     
     if result is None:
-        return "❌ Optimization failed"
+        return "Optimization failed"
     
     optimal_setup, optimal_lap_time = result
     
     setup_report = f"""
-    ## 🏆 Optimal Car Setup
+    ## Optimal Car Setup
     
     **Predicted Lap Time: {optimal_lap_time:.3f} seconds**
     
@@ -290,10 +290,10 @@ def optimize_car_setup(track_temp, wind_speed, track_grip):
 
 # Create Gradio interface
 with gr.Blocks(title="F1 Aerodynamic Performance Predictor", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🏎️ F1 Aerodynamic Performance Predictor")
+    gr.Markdown("# F1 Aerodynamic Performance Predictor")
     gr.Markdown("AI-powered aerodynamic analysis and setup optimization for Formula 1 racing.")
     
-    with gr.Tab("📊 Aerodynamic Analysis"):
+    with gr.Tab("Aerodynamic Analysis"):
         gr.Markdown("### Analyze aerodynamic performance data")
         analyze_btn = gr.Button("🔍 Analyze Aerodynamics", variant="primary")
         
@@ -308,7 +308,7 @@ with gr.Blocks(title="F1 Aerodynamic Performance Predictor", theme=gr.themes.Sof
             outputs=[aero_plot, aero_report]
         )
     
-    with gr.Tab("🎯 Performance Prediction"):
+    with gr.Tab("Performance Prediction"):
         gr.Markdown("### Predict lap time for specific setup")
         gr.Markdown("*Note: Run the analysis first to train the model*")
         
@@ -339,7 +339,7 @@ with gr.Blocks(title="F1 Aerodynamic Performance Predictor", theme=gr.themes.Sof
             outputs=[lap_time_output]
         )
     
-    with gr.Tab("🏆 Setup Optimization"):
+    with gr.Tab("Setup Optimization"):
         gr.Markdown("### Optimize car setup for track conditions")
         gr.Markdown("*Uses genetic algorithm to find optimal aerodynamic configuration*")
         
@@ -361,13 +361,13 @@ with gr.Blocks(title="F1 Aerodynamic Performance Predictor", theme=gr.themes.Sof
             outputs=[optimization_output]
         )
     
-    with gr.Tab("ℹ️ About"):
+    with gr.Tab("About"):
         gr.Markdown("""
         ## About This Tool
         
         This F1 Aerodynamic Performance Predictor uses advanced machine learning and optimization techniques:
         
-        **🎯 Performance Prediction:**
+        **Performance Prediction:**
         - Random Forest model predicts lap times based on car setup and track conditions
         - Considers aerodynamic efficiency, wing configurations, and environmental factors
         - Trained on realistic F1 aerodynamic data
@@ -377,19 +377,19 @@ with gr.Blocks(title="F1 Aerodynamic Performance Predictor", theme=gr.themes.Sof
         - Balances downforce vs drag for maximum performance
         - Considers track-specific conditions for tailored setups
         
-        **📊 Key Features:**
+        **Key Features:**
         - Aerodynamic efficiency analysis (downforce/drag ratio)
         - Wing angle optimization for different track types
         - Environmental impact assessment (temperature, wind, grip)
         - Sensitivity analysis for setup parameters
         
-        **🏗️ Technical Implementation:**
+        **Technical Implementation:**
         - Random Forest Regressor for non-linear relationships
         - Differential Evolution for global optimization
         - StandardScaler for feature normalization
         - Advanced visualization of aerodynamic trade-offs
         
-        **🏁 Racing Applications:**
+        **Racing Applications:**
         - Pre-race setup optimization
         - Strategy planning for different track conditions
         - Understanding aerodynamic trade-offs