Update app.py

app.py

@@ -43,22 +43,22 @@ try:
 
     # Load the models
     loaded_rf_model = load(rf_model_path)
-    print("랜덤 포레스트 모델이 성공적으로 로드되었습니다.")
+    print("Random Forest model loaded successfully.")
     loaded_mlp_model = load_model(mlp_model_path)
-    print("MLP 모델이 성공적으로 로드되었습니다.")
+    print("MLP model loaded successfully.")
     loaded_meta_model = load(meta_model_path)
-    print("메타 모델이 성공적으로 로드되었습니다.")
+    print("Meta model loaded successfully.")
 except Exception as e:
-    print(f"모델 또는 스케일러 로드 중 오류 발생: {e}")
+    print(f"Error loading models or scaler: {e}")
 
 def predict_and_plot(velocity, temperature, precipitation, humidity):
     try:
         # Prepare the example data
         example_data = pd.DataFrame({
-            '속도(mph)': [velocity],
-            '온도(°C)': [temperature],
-            '강수량(inch)': [precipitation],
-            '습도(%)': [humidity]
+            'Velocity(mph)': [velocity],
+            'Temperature': [temperature],
+            'Precipitation': [precipitation],
+            'Humidity': [humidity]
         })
 
         # Scale the example data
@@ -113,20 +113,20 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
         cleaning_times = calculate_cleaning_time(time_intervals, simulated_contamination_levels)
 
         # Lidar names
-        lidar_names = ['앞/왼쪽', '앞/오른쪽', '왼쪽', '오른쪽', '지붕', '뒤']
+        lidar_names = ['F/L', 'F/R', 'Left', 'Right', 'Roof', 'Rear']
 
         # Plot the graph
         fig, ax = plt.subplots(figsize=(12, 8))
 
         for i in range(simulated_contamination_levels.shape[1]):
             ax.plot(time_intervals, simulated_contamination_levels[:, i], label=f'{lidar_names[i]}')
-            ax.axhline(y=0.4, color='r', linestyle='--', label='오염 임계값' if i == 0 else "")
+            ax.axhline(y=0.4, color='r', linestyle='--', label='Contamination Threshold' if i == 0 else "")
             if i < len(cleaning_times):
                 ax.scatter(cleaning_times[i], 0.4, color='k')  # Mark the cleaning time point
 
-        ax.set_title('각 LiDAR에 대한 시간 경과에 따른 오염 수준')
-        ax.set_xlabel('시간 (초)')
-        ax.set_ylabel('오염 수준')
+        ax.set_title('Contamination Levels Over Time for Each Lidar')
+        ax.set_xlabel('Time (seconds)')
+        ax.set_ylabel('Contamination Level')
         ax.legend()
         ax.grid(True)
         
@@ -139,80 +139,80 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
         return [plot_output] + contamination_output + gradients_output + cleaning_time_output
 
     except Exception as e:
-        print(f"Gradio 인터페이스 오류 발생: {e}")
-        return [plt.figure()] + ["오류"] * 18
+        print(f"Error in Gradio interface: {e}")
+        return [plt.figure()] + ["Error"] * 18
 
 inputs = [
-    gr.Slider(minimum=0, maximum=100, value=50, step=0.05, label="속도 (mph)"),
-    gr.Slider(minimum=-2, maximum=30, value=0, step=0.5, label="온도 (°C)"),
-    gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="강수량 (inch)"),
-    gr.Slider(minimum=0, maximum=100, value=50, label="습도 (%)")
+    gr.Slider(minimum=0, maximum=100, value=50, step=0.05, label="Velocity (mph)"),
+    gr.Slider(minimum=-2, maximum=30, value=0, step=0.5, label="Temperature (°C)"),
+    gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Precipitation (inch)"),
+    gr.Slider(minimum=0, maximum=100, value=50, label="Humidity (%)")
 ]
 
 contamination_outputs = [
-    gr.Textbox(label="앞 왼쪽 오염도"),
-    gr.Textbox(label="앞 오른쪽 오염도"),
-    gr.Textbox(label="왼쪽 오염도"),
-    gr.Textbox(label="오른쪽 오염도"),
-    gr.Textbox(label="지붕 오염도"),
-    gr.Textbox(label="뒤 오염도")
+    gr.Textbox(label="Front Left Contamination"),
+    gr.Textbox(label="Front Right Contamination"),
+    gr.Textbox(label="Left Contamination"),
+    gr.Textbox(label="Right Contamination"),
+    gr.Textbox(label="Roof Contamination"),
+    gr.Textbox(label="Rear Contamination")
 ]
 
 gradients_outputs = [
-    gr.Textbox(label="앞 왼쪽 기울기"),
-    gr.Textbox(label="앞 오른쪽 기울기"),
-    gr.Textbox(label="왼쪽 기울기"),
-    gr.Textbox(label="오른쪽 기울기"),
-    gr.Textbox(label="지붕 기울기"),
-    gr.Textbox(label="뒤 기울기")
+    gr.Textbox(label="Front Left Gradient"),
+    gr.Textbox(label="Front Right Gradient"),
+    gr.Textbox(label="Left Gradient"),
+    gr.Textbox(label="Right Gradient"),
+    gr.Textbox(label="Roof Gradient"),
+    gr.Textbox(label="Rear Gradient")
 ]
 
 cleaning_time_outputs = [
-    gr.Textbox(label="앞 왼쪽 청소 시간"),
-    gr.Textbox(label="앞 오른쪽 청소 시간"),
-    gr.Textbox(label="왼쪽 청소 시간"),
-    gr.Textbox(label="오른쪽 청소 시간"),
-    gr.Textbox(label="지붕 청소 시간"),
-    gr.Textbox(label="뒤 청소 시간")
+    gr.Textbox(label="Front Left Cleaning Time"),
+    gr.Textbox(label="Front Right Cleaning Time"),
+    gr.Textbox(label="Left Cleaning Time"),
+    gr.Textbox(label="Right Cleaning Time"),
+    gr.Textbox(label="Roof Cleaning Time"),
+    gr.Textbox(label="Rear Cleaning Time")
 ]
 
 with gr.Blocks(css=".column-container {height: 100%; display: flex; flex-direction: column; justify-content: space-between;}") as demo:
-    gr.Markdown("<h1 style='text-align: center;'>환경 요인 기반 오염도, 기울기 및 청소 시간 예측</h1>")
-    gr.Markdown("이 애플리케이션은 속도, 온도, 강수량 및 습도와 같은 환경 요인을 기반으로 자동차의 LiDAR 시스템의 다양한 부위에 대한 오염도, 기울기 및 청소 시간을 예측합니다.")
+    gr.Markdown("<h1 style='text-align: center;'>Environmental Factor-Based Contamination, Gradient, & Cleaning Time Prediction</h1>")
+    gr.Markdown("This application predicts the contamination levels, gradients, and cleaning times for different parts of a car's LiDAR system based on environmental factors such as velocity, temperature, precipitation, and humidity.")
     
     # Top Section: Inputs and Car Image
     with gr.Row():
         with gr.Column(scale=2, elem_classes="column-container"):
-            gr.Markdown("### 입력 매개변수")
+            gr.Markdown("### Input Parameters")
             for inp in inputs:
                 inp.render()
-            submit_button = gr.Button(value="제출", variant="primary")
-            clear_button = gr.Button(value="초기화")
+            submit_button = gr.Button(value="Submit", variant="primary")
+            clear_button = gr.Button(value="Clear")
 
         with gr.Column(scale=1):
-            gr.Markdown("### LiDAR 위치")
+            gr.Markdown("### Location of LiDARs")
             gr.Image(image_path)
 
     # Bottom Section: Outputs (Three columns)
     with gr.Row():
         with gr.Column(scale=2):
-            gr.Markdown("### 오염도 예측")
+            gr.Markdown("### Contamination Predictions")
             for out in contamination_outputs:
                 out.render()
 
         with gr.Column(scale=2):
-            gr.Markdown("### 기울기 예측")
+            gr.Markdown("### Gradient Predictions")
             for out in gradients_outputs:
                 out.render()
 
         with gr.Column(scale=2):
-            gr.Markdown("### 청소 시간 예측")
+            gr.Markdown("### Cleaning Time Predictions")
             for out in cleaning_time_outputs:
                 out.render()
 
     # Graph below the outputs
     with gr.Row():
-        plot_output = gr.Plot(label="시간 경과에 따른 오염도")
+        plot_output = gr.Plot(label="Contamination Levels Over Time")
 
     submit_button.click(
         fn=predict_and_plot,