Create app.py

app.py

@@ -0,0 +1,48 @@
+import io
+import pandas as pd
+import numpy as np
+import gradio as gr
+import plotly.express as px
+
+def process_file(file_obj):
+    if file_obj is None:
+        return "Please upload a dataset.", None, None
+
+    # Read file (CSV or Excel)
+    try:
+        if file_obj.name.endswith(".xlsx"):
+            df = pd.read_excel(file_obj)
+        else:
+            df = pd.read_csv(file_obj)
+    except Exception as e:
+        return f"Error reading file: {e}", None, None
+
+    # Auto-clean: drop empty rows, normalize numeric columns
+    df = df.dropna(how="all")
+    for col in df.select_dtypes(include="object").columns:
+        df[col] = df[col].str.strip()
+
+    # Example: compute a QualityScore if columns exist
+    if {"NPS","CompletionRate","LearnerSatisfaction","ContentQuality"}.issubset(df.columns):
+        nps_norm = (df["NPS"].astype(float) + 100) / 2
+        score = 0.3*nps_norm + 0.3*(df["CompletionRate"].astype(float)*100) \
+                + 0.2*(df["LearnerSatisfaction"].astype(float)/5*100) \
+                + 0.2*df["ContentQuality"].astype(float)
+        df["QualityScore"] = score.round(1)
+
+        bar = px.bar(df, x="CourseName", y="QualityScore", title="Course Quality Score")
+        heat = px.imshow(df[["NPS","CompletionRate","LearnerSatisfaction","ContentQuality"]].T,
+                         x=df["CourseName"], aspect="auto", title="Metrics Heatmap")
+        return df, bar, heat
+    else:
+        return "Dataset missing expected columns (NPS, CompletionRate, etc.)", None, None
+
+demo = gr.Interface(
+    fn=process_file,
+    inputs=gr.File(label="Upload CSV/XLSX"),
+    outputs=[gr.Dataframe(), gr.Plot(), gr.Plot()],
+    title="Course Quality Tracker"
+)
+
+if __name__ == "__main__":
+    demo.launch()