app.py

import pandas as pd
import gspread
import gradio as gr
import plotly.express as px
from oauth2client.service_account import ServiceAccountCredentials

# ------------------ AUTH ------------------
VALID_USERS = {
    "[email protected]": "Pass.123",
    "[email protected]": "Pass.123",
    "[email protected]": "Pass.123"
}

# ------------------ GOOGLE SHEET SETUP ------------------
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
creds = ServiceAccountCredentials.from_json_keyfile_name("tough-star.json", scope)
client = gspread.authorize(creds)
sheet_url = "https://docs.google.com/spreadsheets/d/1bpeFS6yihb6niCavpwjWmVEypaSkGxONGg2jZfKX_sA"

# ------------------ DATA REFRESH FUNCTION ------------------
def refresh_data():
    sheet = client.open_by_url(sheet_url).worksheet("Calls")
    data = sheet.get_all_records()
    df = pd.DataFrame(data)

    # Timestamp parsing
    df['Timestamp'] = pd.to_datetime(df['Timestamp'], dayfirst=True, errors='coerce')
    df['Date'] = df['Timestamp'].dt.date.astype(str)
    df['Time'] = df['Timestamp'].dt.time

    # Location parsing
    location_split = df['Location'].str.split(',', expand=True)
    df['Latitude'] = pd.to_numeric(location_split[0], errors='coerce')
    df['Longitude'] = pd.to_numeric(location_split[1], errors='coerce')

    # Data cleaning
    df = df.dropna(subset=['Date', 'Rep Name', 'Latitude', 'Longitude'])
    df = df[(df['Latitude'] != 0) & (df['Longitude'] != 0)]
    df = df.sort_values(by=['Rep Name', 'Timestamp'])
    df['Time Diff (min)'] = df.groupby(['Rep Name', 'Date'])['Timestamp'].diff().dt.total_seconds().div(60).fillna(0)
    df['Visit Order'] = df.groupby(['Rep Name', 'Date']).cumcount() + 1

    return df

# ------------------ DEALER ESCALATIONS DATA FUNCTION ------------------
def get_dealer_escalations():
    dealers_sheet = client.open_by_url(sheet_url).worksheet("Dealers")
    dealers_data = dealers_sheet.get_all_records()
    dealers_df = pd.DataFrame(dealers_data)

    # Standardize column names (in case of different casing/spacing)
    dealers_df.columns = [c.strip() for c in dealers_df.columns]

    # Filter for rows where Escalate Dealer == 'yes' (case-insensitive)
    mask = dealers_df['Escalate Dealer'].str.strip() == 'Yes'
    filtered_df = dealers_df.loc[mask, [
        'Dealership Name',
        'Rep Name',
        'Escalate Dealer',
        'Escalation Comment'
    ]]

    # Optional: Sort by Rep Name and Dealership Name
    filtered_df = filtered_df.sort_values(by=['Rep Name', 'Dealership Name'])

    # If there are no escalations, show a friendly empty DataFrame
    if filtered_df.empty:
        filtered_df = pd.DataFrame(
            [["No dealer escalations found.", "", "", ""]],
            columns=['Dealership Name', 'Rep Name', 'Escalate Dealer', 'Escalation Comment']
        )

    return filtered_df

# ------------------ DASHBOARD FUNCTIONS ------------------
def generate_summary(date_str):
    df = refresh_data()
    all_reps = sorted(df['Rep Name'].dropna().unique())
    day_df = df[df['Date'] == date_str]
    active = day_df.groupby('Rep Name').size().reset_index(name='Total Visits')
    active_list = active['Rep Name'].tolist()
    inactive_list = [rep for rep in all_reps if rep not in active_list]
    inactive_df = pd.DataFrame({'Inactive Reps': inactive_list})
    return active, inactive_df

def get_reps(date_str):
    df = refresh_data()
    reps = df[df['Date'] == date_str]['Rep Name'].dropna().unique()
    return gr.update(choices=sorted(reps))

def show_map(date_str, rep):
    df = refresh_data()
    subset = df[(df['Date'] == date_str) & (df['Rep Name'] == rep)]
    if subset.empty:
        return "No valid data", None

    subset = subset.sort_values(by='Timestamp').copy()
    subset['Visit Order'] = range(1, len(subset) + 1)
    center_lat = subset['Latitude'].mean()
    center_lon = subset['Longitude'].mean()

    fig = px.line_mapbox(
        subset,
        lat="Latitude", lon="Longitude",
        hover_name="Dealership Name",
        hover_data={"Time": True, "Time Diff (min)": True, "Visit Order": True},
        height=700,
        zoom=13,
        center={"lat": center_lat, "lon": center_lon}
    )

    scatter = px.scatter_mapbox(
        subset,
        lat="Latitude", lon="Longitude",
        color="Visit Order",
        hover_name="Dealership Name",
        hover_data=["Time", "Time Diff (min)"],
        color_continuous_scale="Bluered"
    )
    for trace in scatter.data:
        fig.add_trace(trace)

    fig.add_trace(px.scatter_mapbox(
        pd.DataFrame([subset.iloc[0]]),
        lat="Latitude", lon="Longitude",
        text=["Start"], color_discrete_sequence=["green"]).data[0])
    fig.add_trace(px.scatter_mapbox(
        pd.DataFrame([subset.iloc[-1]]),
        lat="Latitude", lon="Longitude",
        text=["End"], color_discrete_sequence=["red"]).data[0])

    fig.update_layout(mapbox_style="open-street-map", title=f"📍 {rep}'s Route on {date_str}")

    table = subset[[ 
        'Visit Order', 'Dealership Name', 'Time', 'Time Diff (min)', 
        'Type of call', 'Sales or service'
    ]].rename(columns={
        'Dealership Name': '🧭 Dealer',
        'Time': '🕒 Time',
        'Time Diff (min)': '⏱️ Time Spent',
        'Type of call': '📞 Call Type',
        'Sales or service': '💼 Category'
    })

    total_time = round(table['⏱️ Time Spent'].sum(), 2)
    summary_row = pd.DataFrame([{
        'Visit Order': '',
        '🧭 Dealer': f"🧮 Total Time: {total_time} min",
        '🕒 Time': '',
        '⏱️ Time Spent': '',
        '📞 Call Type': '',
        '💼 Category': ''
    }])
    table = pd.concat([table, summary_row], ignore_index=True)
    return table, fig

# ------------------ GRADIO APP ------------------
with gr.Blocks() as app:
    with gr.Row():
        with gr.Column(visible=True) as login_ui:
            gr.Markdown("## 🔐 Login Required")
            email = gr.Textbox(label="Email")
            password = gr.Textbox(label="Password", type="password")
            login_btn = gr.Button("Login")
            login_msg = gr.Markdown("")

        with gr.Column(visible=False) as main_ui:
            gr.Markdown("## 🗂️ Carfind Rep Tracker")
            df_initial = refresh_data()
            unique_dates = sorted(df_initial['Date'].unique(), reverse=True)

            with gr.Tab("📊 Summary"):
                date_summary = gr.Dropdown(label="Select Date", choices=unique_dates)
                active_table = gr.Dataframe(label="✅ Active Reps (with total visits)")
                inactive_table = gr.Dataframe(label="⚠️ Inactive Reps")
                date_summary.change(fn=generate_summary, inputs=date_summary, outputs=[active_table, inactive_table])

            with gr.Tab("👤 KAM's"):
                with gr.Row():
                    with gr.Column(scale=1):
                        date_picker = gr.Dropdown(label="Select Date", choices=unique_dates)
                        rep_picker = gr.Dropdown(label="Select Rep")
                        btn = gr.Button("Show Route")
                    with gr.Column(scale=2):
                        table = gr.Dataframe(label="Call Table")

                map_plot = gr.Plot(label="Map")
                date_picker.change(fn=get_reps, inputs=date_picker, outputs=rep_picker)
                btn.click(fn=show_map, inputs=[date_picker, rep_picker], outputs=[table, map_plot])

            with gr.Tab("🚨 Dealer Escalations"):
                gr.Markdown("### 🚨 Dealer Escalations (Only showing escalated dealers)")
                escalations_df = gr.Dataframe(value=get_dealer_escalations, label="Escalated Dealers", interactive=False)
                refresh_btn = gr.Button("🔄 Refresh Escalations")

                # Refreshes the dataframe on button click
                refresh_btn.click(fn=get_dealer_escalations, outputs=escalations_df)

    def do_login(user, pw):
        if VALID_USERS.get(user) == pw:
            return gr.update(visible=False), gr.update(visible=True), ""
        else:
            return gr.update(visible=True), gr.update(visible=False), "❌ Invalid email or password."

    login_btn.click(fn=do_login, inputs=[email, password], outputs=[login_ui, main_ui, login_msg])

app.launch()