app.py

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

# ------------------ Authentication ------------------
VALID_USERS = {
    "phonnie@carfind.co.za": "Pass.123",
    "monique@carfind.co.za": "Pass.123",
    "andrew@lortechnologies.com": "Pass.123",
}

def login():
    st.title("🔐 Login Required")
    email = st.text_input("Email")
    password = st.text_input("Password", type="password")
    if st.button("Login"):
        if VALID_USERS.get(email) == password:
            st.session_state.authenticated = True
            st.rerun()
        else:
            st.error("❌ Incorrect email or password.")

if "authenticated" not in st.session_state:
    st.session_state.authenticated = False

if not st.session_state.authenticated:
    login()
    st.stop()

# === Google Sheets Auth ===
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)

# === Load and clean sheet data ===
sheet_url = "https://docs.google.com/spreadsheets/d/1bpeFS6yihb6niCavpwjWmVEypaSkGxONGg2jZfKX_sA"
sheet = client.open_by_url(sheet_url).worksheet("Calls")
data = sheet.get_all_records()
df = pd.DataFrame(data)

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_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')
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

# === Helper: All unique reps in dataset ===
all_reps = sorted(df['Rep Name'].dropna().unique())

# === Tab 1: Summary ===
def generate_summary(date_str):
    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

# === Tab 2: KAMs ===
def get_reps(date_str):
    reps = df[df['Date'] == date_str]['Rep Name'].dropna().unique()
    return gr.update(choices=sorted(reps))

def show_map(date_str, rep):
    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}")

    # Final table
    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'
    })

    # Add summary row
    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 UI ===
with gr.Blocks() as app:
    gr.Markdown("## 🗂️ Carfind Rep Tracker")

    with gr.Tab("📊 Summary"):
        date_summary = gr.Dropdown(label="Select Date", choices=sorted(df['Date'].unique(), reverse=True))
        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=sorted(df['Date'].unique(), reverse=True))
                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])

app.launch()