update app file

app.py

@@ -350,65 +350,65 @@ with gr.Blocks(css=css) as demo:
                                 )
                 # clear_btn.click(fn=lambda value: gr.update(value=""), inputs=clear_btn, outputs=translated_output)
 
-        gr.Markdown("""
-            ## 4. Plot the generated code  ☕️
-            Inference time is about ~20-30 seconds, when it's your turn 😬
-            """
-            )
-        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")
-                ]
-                outputs = gr.Plot()
-
-                demo2 = gr.Interface(
-                    fn = outbreak,
-                    inputs = inputs,
-                    outputs = outputs,
-                    title="Employee-Experience: Data Correlation and Pattern Visualization",
-                    allow_flagging=False
-                )
+        # gr.Markdown("""
+        #     ## 4. Plot the generated code  ☕️
+        #     Inference time is about ~20-30 seconds, when it's your turn 😬
+        #     """
+        #     )
+        # 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")
+        #         ]
+        #         outputs = gr.Plot()
+
+        #         demo2 = gr.Interface(
+        #             fn = outbreak,
+        #             inputs = inputs,
+        #             outputs = outputs,
+        #             title="Employee-Experience: Data Correlation and Pattern Visualization",
+        #             allow_flagging=False
+        #         )
 
 
         gr.Markdown("""