File size: 2,829 Bytes
74e6fc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | 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=[])
# Load data
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)
# Smart Header Detection (Finding the 'real' table start)
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')
# Convert numeric columns automatically
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
# --- Gradio UI Layout ---
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()
# Logic Flows
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() |