app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
from google import genai

# 🔑 ADD YOUR GEMINI API KEY
client = genai.Client(api_key="AIzaSyCvvewIh6BVyULlEmw1QE75vWsEmu8QNH0")


# ---------- READ FILE ----------
def read_file(file):
    try:
        if file is None:
            return pd.DataFrame()

        if file.name.endswith(".xlsx"):
            return pd.read_excel(file.name)
        else:
            return pd.read_csv(file.name, engine="python")
    except:
        return pd.DataFrame()


# ---------- PREVIEW ----------
def preview(file):
    df = read_file(file)
    if df.empty:
        return pd.DataFrame({"Message": ["Could not read file"]})
    return df.head(20)


# ---------- CONVERSION ----------
def convert(file):
    df = read_file(file)

    if df.empty:
        return pd.DataFrame({"Message": ["No data"]})

    if "Time in Session (minutes)" not in df.columns:
        return pd.DataFrame({"Message": ["No recognizable webinar structure"]})

    df["Time in Session (minutes)"] = pd.to_numeric(
        df["Time in Session (minutes)"], errors="coerce"
    )

    df = df.dropna()

    if df.empty:
        return pd.DataFrame({"Message": ["No usable data"]})

    total = len(df)
    completed = df[df["Time in Session (minutes)"] > 60]

    completion = (len(completed) / total) * 100
    avg_time = df["Time in Session (minutes)"].mean()

    if avg_time > 150:
        satisfaction, nps = 4.6, 75
    elif avg_time > 100:
        satisfaction, nps = 4.2, 65
    elif avg_time > 60:
        satisfaction, nps = 3.9, 55
    else:
        satisfaction, nps = 3.5, 40

    result = pd.DataFrame({
        "Course Name": ["Webinar Course"],
        "NPS Score": [nps],
        "Completion Rate (%)": [round(completion, 2)],
        "Satisfaction (1-5)": [satisfaction]
    })

    result["Health Score"] = (
        (result["NPS Score"] * 0.4) +
        (result["Completion Rate (%)"] * 0.3) +
        (result["Satisfaction (1-5)"] * 20 * 0.3)
    )

    return result


# ---------- CHART ----------
def make_chart(df):
    if df is None or df.empty or "Course Name" not in df.columns:
        return None

    fig, ax = plt.subplots()
    ax.bar(df["Course Name"], df["Health Score"])
    plt.xticks(rotation=45)
    plt.title("Course Health Score")

    return fig


# ---------- AI CHAT ----------
def chat_with_data(file, message, history):
    if history is None:
        history = []

    try:
        df = read_file(file)
        preview_text = df.head(20).to_string()

        prompt = f"""
        You are a data assistant.

        Dataset preview:
        {preview_text}

        User question:
        {message}

        Explain:
        - what this dataset is
        - what issues exist
        - how to structure it into course metrics
        """

        response = client.models.generate_content(
            model="gemini-1.5-flash",
            contents=prompt
        )

        reply = response.text

    except Exception as e:
        reply = str(e)

    # ✅ OLD FORMAT (COMPATIBLE)
    history.append((message, reply))

    return history, history


# ---------- UI ----------
with gr.Blocks() as app:
    gr.Markdown("# 📊 Course Quality Tracker + AI Assistant")

    file_input = gr.File(label="Upload CSV / Excel")

    gr.Markdown("## 🔍 Data Preview")
    preview_table = gr.Dataframe()

    gr.Markdown("## ⚙️ Auto Conversion")
    result_table = gr.Dataframe()
    chart = gr.Plot()

    gr.Markdown("## 🤖 Ask AI about your data")
    chatbot = gr.Chatbot()   # ✅ FIXED (no type)
    msg = gr.Textbox(label="Ask anything about your dataset")

    file_input.change(
        fn=preview,
        inputs=file_input,
        outputs=preview_table
    )

    file_input.change(
        fn=convert,
        inputs=file_input,
        outputs=result_table
    )

    result_table.change(
        fn=make_chart,
        inputs=result_table,
        outputs=chart
    )

    msg.submit(
        fn=chat_with_data,
        inputs=[file_input, msg, chatbot],
        outputs=[chatbot, chatbot]
    )

app.launch()