Update app.py

app.py

@@ -0,0 +1,78 @@
+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()