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

    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

    # Clean
    df = df.dropna(how="all")

    required = {"CourseName","NPS","CompletionRate","LearnerSatisfaction","ContentQuality"}
    if not required.issubset(df.columns):
        return "Dataset missing expected columns (NPS, CompletionRate, etc.)", None, None

    # Compute QualityScore
    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 "Dataset processed successfully!", df, bar, heat

demo = gr.Interface(
    fn=process_file,
    inputs=gr.File(label="Upload CSV/XLSX"),
    outputs=[
        gr.Textbox(label="Status"),   # for messages
        gr.Dataframe(label="Cleaned Data"),
        gr.Plot(label="Quality Score Bar"),
        gr.Plot(label="Metrics Heatmap")
    ],
    title="Course Quality Tracker"
)

if __name__ == "__main__":
    demo.launch()