Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+def plot_likert_scores(file):
+    # Load the Excel file into pandas
+    df = pd.read_excel(file.name, sheet_name=0, header=None)
+    
+    # Extract pre and post survey blocks (edit row indices if needed)
+    pre_df = df.iloc[1:5, :6].copy()
+    post_df = df.iloc[12:15, :6].copy()
+
+    pre_df.columns = ['identifier', 'Degree', 'Confidence', 'Feedback', 'Preparedness', 'Enjoyment']
+    post_df.columns = ['identifier', 'Degree', 'Confidence', 'Feedback', 'Preparedness', 'Enjoyment']
+
+    # Clean identifier
+    def clean_identifier(x):
+        try:
+            return int(str(x).strip())
+        except:
+            return np.nan
+
+    pre_df['identifier'] = pre_df['identifier'].apply(clean_identifier)
+    post_df['identifier'] = post_df['identifier'].apply(clean_identifier)
+    pre_df = pre_df.dropna(subset=['identifier'])
+    post_df = post_df.dropna(subset=['identifier'])
+    pre_df['identifier'] = pre_df['identifier'].astype(int)
+    post_df['identifier'] = post_df['identifier'].astype(int)
+
+    # Filter for identifier 7
+    pre_7 = pre_df[pre_df['identifier'] == 7]
+    post_7 = post_df[post_df['identifier'] == 7]
+
+    # If data for identifier 7 is missing
+    if pre_7.empty or post_7.empty:
+        return "Identifier 7 is missing from pre or post survey data. Please check your file."
+
+    # Map Likert values
+    likert_map = {
+        'Strongly Disagree': 1,
+        'Disagree': 2,
+        'Agree': 3,
+        'Strongly Agree': 4
+    }
+    questions = ['Confidence', 'Feedback', 'Enjoyment']
+    pretty_labels = ['Confidence', 'Feedback', 'Enjoyment']
+
+    pre_scores = [likert_map.get(pre_7[q].values[0], np.nan) for q in questions]
+    post_scores = [likert_map.get(post_7[q].values[0], np.nan) for q in questions]
+
+    # Plot
+    x = np.arange(len(questions))
+    width = 0.35
+
+    fig, ax = plt.subplots(figsize=(7, 5))
+    bars1 = ax.bar(x - width/2, pre_scores, width, label='Pre-survey', color='#3182bd', edgecolor='black')
+    bars2 = ax.bar(x + width/2, post_scores, width, label='Post-survey', color='#fdae6b', edgecolor='black')
+
+    # Add value labels
+    for bar in bars1 + bars2:
+        height = bar.get_height()
+        if not np.isnan(height):
+            ax.annotate(f'{int(height)}',
+                        xy=(bar.get_x() + bar.get_width() / 2, height),
+                        xytext=(0, 5),
+                        textcoords="offset points",
+                        ha='center', va='bottom', fontsize=12, fontweight='bold')
+
+    ax.set_xticks(x)
+    ax.set_xticklabels(pretty_labels, fontsize=13, fontweight='bold')
+    ax.set_ylim(0.5, 4.5)
+    ax.set_yticks([1, 2, 3, 4])
+    ax.set_yticklabels(['Strongly Disagree', 'Disagree', 'Agree', 'Strongly Agree'], fontsize=12)
+    ax.set_ylabel('Likert Score', fontsize=13, fontweight='bold')
+    ax.set_title('Identifier 7: Pre vs Post Likert Scores', fontsize=15, fontweight='bold')
+    ax.legend(fontsize=12)
+    ax.grid(axis='y', linestyle='--', alpha=0.7)
+
+    plt.tight_layout()
+
+    return fig
+
+# Gradio interface
+demo = gr.Interface(
+    fn=plot_likert_scores,
+    inputs=gr.File(label="Upload your Excel file (.xlsx)"),
+    outputs=gr.Plot(label="Likert Plot for Identifier 7"),
+    title="Pre vs Post Likert Plot (Identifier 7)",
+    description="Upload your survey Excel file. This tool compares pre/post Likert scores for Confidence, Feedback, and Enjoyment for respondent 7.",
+    allow_flagging='never'
+)
+
+if __name__ == "__main__":
+    demo.launch()