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 = {
@@ -13,146 +13,83 @@ VALID_USERS = {
 
 # ------------------ 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)
+creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.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
-
+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)
     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()
+    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()
     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'
+    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"
     })
-
-    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
+    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")
 
 # ------------------ GRADIO APP ------------------
 with gr.Blocks() as app:
@@ -165,42 +102,67 @@ with gr.Blocks() as app:
             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)
+            gr.Markdown("## 🗂️ CarMat Dashboard")
+
+            # preload dates for field sales
+            df_initial = load_field_sales()
+            unique_dates = sorted(df_initial["DateStr"].unique(), reverse=True)
 
+            # --- Summary Tab ---
             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)
+                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)
 
     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."
+            return gr.update(visible=True), gr.update(visible=False), "❌ Invalid login."
 
     login_btn.click(fn=do_login, inputs=[email, password], outputs=[login_ui, main_ui, login_msg])