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