Create exclude-executives-app.py

exclude-executives-app.py

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+# exclude Executive
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from transformers import pipeline
+
+# Load the sentiment analysis model (supports neutral feedback)
+pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+
+# Function to process CSV and generate dropdown options
+# Function to process CSV and generate dropdown options
+def process_csv(file):
+    df = pd.read_csv(file.name)
+    
+    # Exclude the 'Executive' department from the department list
+    departments = ['All'] + [dept for dept in df['Department'].unique().tolist() if dept != 'Executive']
+    
+    # Include all questions
+    questions = ['All'] + df['Question'].unique().tolist()
+    
+    return gr.update(choices=departments), gr.update(choices=questions)
+
+
+# Function to analyze sentiments and generate a styled HTML table
+# Function to analyze sentiments and generate a styled HTML table
+def get_analysis(department, question, file):
+    df = pd.read_csv(file.name)
+
+    # Exclude the Executive department
+    df = df[df['Department'] != 'Executive']
+
+    # Filter by department and question
+    if department != 'All':
+        df = df[df['Department'] == department]
+    if question != 'All':
+        df = df[df['Question'] == question]
+
+    # Perform sentiment analysis
+    df['Sentiment'] = df['Feedback'].apply(lambda x: pipe(x)[0]['label'])
+    
+    # Count employees per department
+    employee_counts = df.groupby('Department')['Employee'].nunique().reset_index()
+    employee_counts.columns = ['Department', 'Total Employees']
+
+    # Count positive, neutral, and negative feedback per department
+    sentiment_counts = df.groupby(['Department', 'Sentiment']).size().unstack(fill_value=0)
+    sentiment_counts = sentiment_counts.reindex(columns=['positive', 'neutral', 'negative'], fill_value=0)
+
+    # Merge employee counts with sentiment counts
+    summary_df = employee_counts.merge(sentiment_counts, on="Department", how="left")
+
+    # Add a row for total counts
+    total_row = pd.DataFrame({
+        'Department': ['Total'],
+        'Total Employees': [df['Employee'].nunique()],
+        'positive': [summary_df['positive'].sum()],
+        'neutral': [summary_df['neutral'].sum()],
+        'negative': [summary_df['negative'].sum()]
+    })
+
+    summary_df = pd.concat([summary_df, total_row], ignore_index=True)
+
+    # **Generate HTML Table with Colors**
+    def generate_html_table(df):
+        html = """<style>
+            table {width: 100%; border-collapse: collapse; font-family: Arial, sans-serif; text-align: center;}
+            th {background-color: gray; padding: 10px;}
+            td, th {border: 1px solid #ddd; padding: 8px;}
+            tr:nth-child(even) {background-color: #f2f2f2;}
+            .total {background-color: lightgray; font-weight: bold;}
+            .positive {color: green; font-weight: bold;}
+            .neutral {color: gray; font-weight: bold;}
+            .negative {color: red; font-weight: bold;}
+        </style>
+        <table>
+        <tr>
+            <th>Department</th><th>Total Employees</th>
+            <th>Positive</th><th>Neutral</th><th>Negative</th>
+        </tr>"""
+        for _, row in df.iterrows():
+            row_class = "total" if row["Department"] == "Total" else ""
+            html += f"""
+            <tr class="{row_class}">
+                <td>{row["Department"]}</td>
+                <td>{row["Total Employees"]}</td>
+                <td class="positive">{row["positive"]}</td>
+                <td class="neutral">{row["neutral"]}</td>
+                <td class="negative">{row["negative"]}</td>
+            </tr>"""
+        html += "</table>"
+        return html
+
+    table_html = generate_html_table(summary_df)
+
+    # **Plot department-wise sentiment distribution**
+    plt.figure(figsize=(10, 6))
+    sentiment_counts.plot(kind='bar', stacked=False, color=['green', 'gray', 'red'], width=0.6)
+    plt.xlabel("Department")
+    plt.ylabel("Count")
+    plt.title("Department-wise Sentiment Analysis")
+    plt.xticks(rotation=45)
+    plt.legend(title="Sentiment", loc="upper right")
+    plt.grid(axis='y', linestyle='--', alpha=0.7)
+    plt.tight_layout()
+
+    return table_html, plt.gcf()
+
+# **Gradio UI**
+with gr.Blocks() as demo:
+    gr.Markdown("### Employee Sentiment Analysis")
+
+    file_input = gr.File(label="Upload CSV", file_types=[".csv"])
+    process_button = gr.Button("Process CSV")
+
+    dept_dropdown = gr.Dropdown(label="Department", choices=["All"], interactive=True)
+    question_dropdown = gr.Dropdown(label="Question", choices=["All"], interactive=True)
+
+    analyze_button = gr.Button("Get Analysis")
+
+    output_table = gr.HTML(label="Department-wise Sentiment Summary")
+    output_plot = gr.Plot(label="Department-wise Sentiment Distribution")
+
+    process_button.click(process_csv, inputs=file_input, outputs=[dept_dropdown, question_dropdown])
+    analyze_button.click(get_analysis, inputs=[dept_dropdown, question_dropdown, file_input], outputs=[output_table, output_plot])
+
+demo.launch(share=True)