Update app.py

app.py

@@ -1,8 +1,8 @@
 import pandas as pd
 import gspread
 import gradio as gr
+import plotly.express as px
 from oauth2client.service_account import ServiceAccountCredentials
-from datetime import datetime
 
 # ------------------ AUTH ------------------
 VALID_USERS = {
@@ -13,83 +13,146 @@ VALID_USERS = {
 
 # ------------------ GOOGLE SHEET SETUP ------------------
 scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.json", scope)
+creds = ServiceAccountCredentials.from_json_keyfile_name("tough-star.json", scope)
 client = gspread.authorize(creds)
-sheet_file = client.open("userAccess")
-
-# ------------------ HELPERS ------------------
-def load_tab(sheet_name):
-    return pd.DataFrame(sheet_file.worksheet(sheet_name).get_all_records())
-
-# ------------------ FIELD SALES ------------------
-def load_field_sales():
-    df = load_tab("Field Sales")
-    df['Date'] = pd.to_datetime(df.get("Date", datetime.today()), errors='coerce')
-    df = df.dropna(subset=["Date"])
-    df['DateStr'] = df['Date'].dt.date.astype(str)
-    df['Order Value'] = pd.to_numeric(df.get("Order Value", 0), errors='coerce').fillna(0)
+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 = load_field_sales()
-    all_reps = sorted(df['Rep'].dropna().unique())
-    day_df = df[df['DateStr'] == date_str]
-
-    total_visits = day_df.groupby("Rep").size().reset_index(name="Total Visits")
-    current = day_df[day_df["Current/Prospect Custor"] == "Current"]
-    prospect = day_df[day_df["Current/Prospect Custor"] == "Prospect"]
-    breakdown = pd.DataFrame({
-        "Rep": all_reps,
-        "Current": [len(current[current["Rep"] == rep]) for rep in all_reps],
-        "Prospect": [len(prospect[prospect["Rep"] == rep]) for rep in all_reps]
-    })
-    active_list = total_visits['Rep'].tolist()
+    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 total_visits, breakdown, inactive_df
-
-def get_order_summary(date_str):
-    df = load_field_sales()
-    day_df = df[df['DateStr'] == date_str]
-    rep_group = day_df.groupby("Rep").agg({
-        "Order Received": lambda x: (x == "Yes").sum(),
-        "Order Value": "sum"
-    }).reset_index().rename(columns={
-        "Order Received": "Orders Received",
-        "Order Value": "Total Order Value"
+    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'
     })
-    return rep_group.sort_values(by="Total Order Value", ascending=False)
-
-def get_escalations():
-    df = load_field_sales()
-    flagged = df[df["Customer Type"].str.contains("Second", na=False)]
-    return flagged if not flagged.empty else pd.DataFrame([["No second-hand dealerships flagged."]], columns=["Status"])
-
-# ------------------ TELESALeS ------------------
-def get_telesales_summary():
-    df = load_tab("TeleSales")
-    df["Date"] = pd.to_datetime(df.get("Date", datetime.today()), errors='coerce')
-    df["DateStr"] = df["Date"].dt.date.astype(str)
-    return df.groupby(["Rep Email", "DateStr"]).size().reset_index(name="Calls Made")
-
-# ------------------ OEM VISITS ------------------
-def get_oem_summary():
-    df = load_tab("OEM Visit")
-    df["Date"] = pd.to_datetime(df.get("Date", datetime.today()), errors='coerce')
-    df["DateStr"] = df["Date"].dt.date.astype(str)
-    return df.groupby(["Rep", "DateStr"]).size().reset_index(name="OEM Visits")
-
-# ------------------ CUSTOMER REQUESTS ------------------
-def get_requests():
-    return load_tab("Customer Requests")
-
-# ------------------ CUSTOMER LISTINGS ------------------
-def get_listings():
-    return load_tab("CustomerListings")
-
-# ------------------ USERS ------------------
-def get_users():
-    return load_tab("Users")
+
+    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:
@@ -102,68 +165,43 @@ with gr.Blocks() as app:
             login_msg = gr.Markdown("")
 
         with gr.Column(visible=False) as main_ui:
-            gr.Markdown("## 🗂️ CarMat Dashboard")
-
-            # preload dates for field sales
-            df_initial = load_field_sales()
-            unique_dates = sorted(df_initial["DateStr"].unique(), reverse=True)
+            gr.Markdown("## 🗂️ Carfind Rep Tracker")
+            df_initial = refresh_data()
+            unique_dates = sorted(df_initial['Date'].unique(), reverse=True)
 
-            # --- Summary Tab ---
             with gr.Tab("📊 Summary"):
                 date_summary = gr.Dropdown(label="Select Date", choices=unique_dates)
-                visits = gr.Dataframe(label="✅ Total Visits")
-                breakdown = gr.Dataframe(label="🏷️ Current vs. Prospect")
-                inactive = gr.Dataframe(label="⚠️ Inactive Reps")
-                date_summary.change(fn=generate_summary, inputs=date_summary, outputs=[visits, breakdown, inactive])
-
-            # --- Orders Tab ---
-            with gr.Tab("📦 Orders"):
-                order_date = gr.Dropdown(label="Select Date", choices=unique_dates)
-                order_table = gr.Dataframe(label="💰 Orders Summary")
-                order_date.change(fn=get_order_summary, inputs=order_date, outputs=order_table)
-
-            # --- Escalations ---
-            with gr.Tab("🚨 Escalations"):
-                esc_table = gr.Dataframe(value=get_escalations, label="Second-hand Dealerships")
-                esc_btn = gr.Button("🔄 Refresh")
-                esc_btn.click(fn=get_escalations, outputs=esc_table)
-
-            # --- TeleSales ---
-            with gr.Tab("📞 TeleSales"):
-                ts_table = gr.Dataframe(value=get_telesales_summary, label="TeleSales Summary")
-                ts_refresh = gr.Button("🔄 Refresh TeleSales")
-                ts_refresh.click(fn=get_telesales_summary, outputs=ts_table)
-
-            # --- OEM Visits ---
-            with gr.Tab("🏭 OEM Visits"):
-                oem_table = gr.Dataframe(value=get_oem_summary, label="OEM Visit Summary")
-                oem_refresh = gr.Button("🔄 Refresh OEM")
-                oem_refresh.click(fn=get_oem_summary, outputs=oem_table)
-
-            # --- Requests ---
-            with gr.Tab("📬 Customer Requests"):
-                req_table = gr.Dataframe(value=get_requests, label="Customer Requests", interactive=False)
-                req_refresh = gr.Button("🔄 Refresh Requests")
-                req_refresh.click(fn=get_requests, outputs=req_table)
-
-            # --- Dealerships ---
-            with gr.Tab("📋 Dealership Directory"):
-                listings_table = gr.Dataframe(value=get_listings, label="Customer Listings")
-                listings_refresh = gr.Button("🔄 Refresh Listings")
-                listings_refresh.click(fn=get_listings, outputs=listings_table)
-
-            # --- Users ---
-            with gr.Tab("👤 Users"):
-                users_table = gr.Dataframe(value=get_users, label="Users")
-                users_refresh = gr.Button("🔄 Refresh Users")
-                users_refresh.click(fn=get_users, outputs=users_table)
+                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 login."
+            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()
+app.launch()