| import gradio as gr |
| import pandas as pd |
| import plotly.express as px |
|
|
| def clean_and_analyze(file): |
| if file is None: |
| return None, "Please upload a file.", gr.update(choices=[]) |
| |
| |
| try: |
| if file.name.endswith('.csv'): |
| df_raw = pd.read_csv(file.name, header=None) |
| else: |
| df_raw = pd.read_excel(file.name, header=None) |
| |
| |
| non_null_counts = df_raw.notnull().sum(axis=1) |
| header_idx = non_null_counts.idxmax() |
| |
| df = df_raw.iloc[header_idx + 1:].reset_index(drop=True) |
| df.columns = [str(c).strip() for c in df_raw.iloc[header_idx].values] |
| df = df.dropna(axis=1, how='all').dropna(axis=0, how='all') |
|
|
| |
| for col in df.columns: |
| numeric_conv = pd.to_numeric(df[col], errors='coerce') |
| if numeric_conv.notnull().sum() > (len(df) * 0.4): |
| df[col] = numeric_conv |
|
|
| cols = df.columns.tolist() |
| summary = f"✅ Successfully cleaned! Found {len(df)} rows and {len(cols)} columns." |
| |
| return df, summary, gr.update(choices=cols, value=cols[0]), gr.update(choices=cols, value=cols[-1]) |
| except Exception as e: |
| return None, f"Error: {str(e)}", gr.update(choices=[]), gr.update(choices=[]) |
|
|
| def create_plot(df, x_col, y_col): |
| if df is None: |
| return None |
| fig = px.bar(df, x=x_col, y=y_col, color=y_col, |
| title=f"{y_col} Analysis", template="plotly_white") |
| return fig |
|
|
| |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: |
| gr.Markdown("# 📊 Course Quality Tracker (Product Ops)") |
| gr.Markdown("Upload raw exports (Zoom, LMS, CSV) and generate instant quality reports.") |
| |
| current_data = gr.State() |
| |
| with gr.Row(): |
| file_input = gr.File(label="Upload Messy CSV or Excel") |
| with gr.Column(): |
| status_msg = gr.Textbox(label="System Status", interactive=False) |
| x_sel = gr.Dropdown(label="Select Course/Identity Column") |
| y_sel = gr.Dropdown(label="Select Quality Metric (Numeric)") |
| plot_btn = gr.Button("Generate Insights", variant="primary") |
|
|
| with gr.Tabs(): |
| with gr.TabItem("Visualization"): |
| plot_output = gr.Plot() |
| with gr.TabItem("Cleaned Data"): |
| table_output = gr.DataFrame() |
|
|
| |
| file_input.change( |
| clean_and_analyze, |
| inputs=[file_input], |
| outputs=[current_data, status_msg, x_sel, y_sel] |
| ).then(lambda df: df, inputs=[current_data], outputs=[table_output]) |
|
|
| plot_btn.click( |
| create_plot, |
| inputs=[current_data, x_sel, y_sel], |
| outputs=[plot_output] |
| ) |
|
|
| demo.launch() |
