update app file

app.py

@@ -79,45 +79,7 @@ def translate(audio):
         tr_flag = flag.flag(transcription.language)
     return tr_flag, transcription.text, translation.text
 
-    def outbreak(plot_type):
-        df = pd.read_csv('emp_experience_data.csv')
-        data_encoded = df.copy(deep=True)
-        categorical_column = ['Attrition', 'Gender', 'BusinessTravel', 'Education', 'EmployeeExperience', 'EmployeeFeedbackSentiments', 'Designation',
-                                'SalarySatisfaction', 'HealthBenefitsSatisfaction', 'UHGDiscountProgramUsage', 'HealthConscious', 'CareerPathSatisfaction', 'Region']
-        label_encoding = LabelEncoder()
-        for col in categorical_column:
-            data_encoded[col] = label_encoding.fit_transform(data_encoded[col])
-
-        if plot_type == "Find Data Correlation":
-            fig = plt.figure()
-            data_correlation = data_encoded.corr()
-            sns.heatmap(data_correlation, xticklabels = data_correlation.columns, yticklabels = data_correlation.columns)
-            return fig
-        if plot_type == "Filter Correlation Data":
-            fig = plt.figure()
-            filtered_df = df[['EmployeeExperience', 'EmployeeFeedbackSentiments', 'Age', 'SalarySatisfaction', 'BusinessTravel', 'HealthBenefitsSatisfaction']]
-            correlation_filter_data = filtered_df.corr()
-            sns.heatmap(correlation_filter_data, xticklabels = filtered_df.columns, yticklabels = filtered_df.columns)
-            return fig
-        if plot_type == "Age vs Attrition":
-            fig = plt.figure()
-            plt.hist(data_encoded['Age'], bins=np.arange(0,80,10), alpha=0.8, rwidth=0.9, color='red')
-            plt.xlabel("Age")
-            plt.ylabel("Count")
-            plt.title("Age vs Attrition")
-            return fig
-        if plot_type == "Business Travel vs Attrition":
-            fig = plt.figure()
-            ax = sns.countplot(x="BusinessTravel", hue="Attrition", data=data_encoded)
-            for p in ax.patches:
-                ax.annotate('{}'.format(p.get_height()), (p.get_x(), p.get_height()+1))
-            return fig
-        if plot_type == "Employee Experience vs Attrition":
-            fig = plt.figure()
-            ax = sns.countplot(x="EmployeeExperience", hue="Attrition", data=data_encoded)
-            for p in ax.patches:
-                ax.annotate('{}'.format(p.get_height()), (p.get_x(), p.get_height()+1))
-            return fig
+
 ### ————————————————————————————————————————
 
 css = """
@@ -395,7 +357,45 @@ with gr.Blocks(css=css) as demo:
             )
         with gr.Column():
             with gr.Row():
-
+            def outbreak(plot_type):
+                df = pd.read_csv('emp_experience_data.csv')
+                data_encoded = df.copy(deep=True)
+                categorical_column = ['Attrition', 'Gender', 'BusinessTravel', 'Education', 'EmployeeExperience', 'EmployeeFeedbackSentiments', 'Designation',
+                                        'SalarySatisfaction', 'HealthBenefitsSatisfaction', 'UHGDiscountProgramUsage', 'HealthConscious', 'CareerPathSatisfaction', 'Region']
+                label_encoding = LabelEncoder()
+                for col in categorical_column:
+                    data_encoded[col] = label_encoding.fit_transform(data_encoded[col])
+
+                if plot_type == "Find Data Correlation":
+                    fig = plt.figure()
+                    data_correlation = data_encoded.corr()
+                    sns.heatmap(data_correlation, xticklabels = data_correlation.columns, yticklabels = data_correlation.columns)
+                    return fig
+                if plot_type == "Filter Correlation Data":
+                    fig = plt.figure()
+                    filtered_df = df[['EmployeeExperience', 'EmployeeFeedbackSentiments', 'Age', 'SalarySatisfaction', 'BusinessTravel', 'HealthBenefitsSatisfaction']]
+                    correlation_filter_data = filtered_df.corr()
+                    sns.heatmap(correlation_filter_data, xticklabels = filtered_df.columns, yticklabels = filtered_df.columns)
+                    return fig
+                if plot_type == "Age vs Attrition":
+                    fig = plt.figure()
+                    plt.hist(data_encoded['Age'], bins=np.arange(0,80,10), alpha=0.8, rwidth=0.9, color='red')
+                    plt.xlabel("Age")
+                    plt.ylabel("Count")
+                    plt.title("Age vs Attrition")
+                    return fig
+                if plot_type == "Business Travel vs Attrition":
+                    fig = plt.figure()
+                    ax = sns.countplot(x="BusinessTravel", hue="Attrition", data=data_encoded)
+                    for p in ax.patches:
+                        ax.annotate('{}'.format(p.get_height()), (p.get_x(), p.get_height()+1))
+                    return fig
+                if plot_type == "Employee Experience vs Attrition":
+                    fig = plt.figure()
+                    ax = sns.countplot(x="EmployeeExperience", hue="Attrition", data=data_encoded)
+                    for p in ax.patches:
+                        ax.annotate('{}'.format(p.get_height()), (p.get_x(), p.get_height()+1))
+                    return figure
 
                 inputs = [
                     gr.Dropdown(["Find Data Correlation", "Filter Correlation Data", "Business Travel vs Attrition", "Employee Experience vs Attrition", "Age vs Attrition",], label="Data Correlation and Visualization")