Upload RandomForest_UI.py

RandomForest_UI.py

@@ -1,8 +1,93 @@
+from RandomForest.RandomForestClass import My_RandomForest 
 import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
 
 class RandomForest_UI():
+    def __init__(self):
+        self.rf_model = My_RandomForest()
+        self.rf_model.train_model("Male")
+        self.rf_model.train_model("Female")
+        self.rf_model.train_model("Unspecified")
+
     def get_interface(self) -> gr.Blocks:
-        with gr.Blocks() as interface:
-            gr.Markdown("hello world!")
+        with gr.Blocks(theme=gr.themes.Soft()) as interface:
+            gr.Markdown("## Experience Level Prediction through Random Forest Classifier" )
+            gr.Markdown("")
+            gr.Markdown(
+    "Welcome to the **Experience Level Prediction** section. Here, you can determine an individual's experience level based on a set of input parameters."
+                            )
+            gr.Markdown(
+    "The prediction output will be categorized as follows:"
+    "\n\n- **1**: Low experience level"
+    "\n- **2**: Medium experience level"
+    "\n- **3**: High experience level"
+                                    )
+            gr.Markdown(
+    "Provide details such as workout frequency, session duration, and water intake to make a prediction. "
+    "You may also specify gender as an additional parameter. "
+    "Generally, higher values for these inputs indicate a higher experience level."
+                )   
+
+            
+            with gr.Row():
+                workout_frequency = gr.Number(label="Workout Frequency (days/week)")
+                session_duration = gr.Number(label="Session Duration (hours)")
+                water_intake = gr.Number(label="Water Intake (liters)")
+                gender = gr.Radio(["Male", "Female", "Unspecified"], label="Gender")
+            
+            predict_btn = gr.Button("Predict")
+            gr.Markdown(
+                 "### Prediction Output"
+                 "\nAfter entering the required inputs, the predicted experience level will be displayed along with the model's accuracy. "
+                 "The prediction categorizes the experience level into one of three categories (1: Low, 2: Medium, 3: High) and provides an accuracy percentage to indicate the confidence of the model's output."
+                    )
+            output = gr.Textbox(
+                label="Prediction",
+                interactive=False,
+                lines=2
+            )
+            gr.Markdown(
+    "### Feature Importance Plot"
+    "\nThe **Feature Importance Plot** provides insights into which input parameters contribute the most to determining the experience level. "
+    "This visualization highlights the factors that play a significant role in increasing the predicted experience level, helping you better understand the model's decision-making process."
+                            )
+            plot_output = gr.Plot()
+
+            predict_btn.click(
+                fn=self.make_prediction_and_plot,
+                inputs=[workout_frequency, session_duration, water_intake, gender],
+                outputs=[output, plot_output]
+            )
+
+        return interface
+
+    def make_prediction_and_plot(self, workout_frequency, session_duration, water_intake, gender):
+        # Generate prediction
+        input_data = pd.DataFrame({
+            "Workout_Frequency (days/week)": [workout_frequency],
+            "Session_Duration (hours)": [session_duration],
+            "Water_Intake (liters)": [water_intake]
+        })
+
+        prediction = self.rf_model.predict(input_data, gender=gender)
+        accuracy = self.rf_model.accuracies[gender]
+
+        prediction_text = f"Predicted Experience Level: {prediction[0]} with an accuracy of {accuracy*100:.4f}%"
+
+        # Generate plot
+        model = self.rf_model.models[gender]
+        features = self.rf_model.selected_features[gender]
+        feature_importances = pd.Series(model.feature_importances_, index=features)
+        feature_importances = feature_importances.sort_values(ascending=False)
+
+        plt.figure(figsize=(10, 6))
+        feature_importances.plot(kind='bar')
+        plt.title(f"Feature Importances plot for {gender} Model")
+        plt.xlabel("Features")
+        plt.ylabel("Importance")
+        plt.tight_layout()
+
+        return prediction_text, plt
+
 
-        return interface