Update app.py

app.py

@@ -1,7 +1,6 @@
 import pandas as pd
 import gspread
 import gradio as gr
-import plotly.express as px
 from oauth2client.service_account import ServiceAccountCredentials
 
 # ------------------ AUTH ------------------
@@ -14,114 +13,41 @@ VALID_USERS = {
 
 # ------------------ GOOGLE SHEET SETUP ------------------
 scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-creds = ServiceAccountCredentials.from_json_keyfile_name("bid4carsappsheetdash.json", scope)
+creds = ServiceAccountCredentials.from_json_keyfile_name("deep-mile-461309-t8-0e90103411e0.json", scope)
 client = gspread.authorize(creds)
-sheet_url = "https://docs.google.com/spreadsheets/d/1eUXhcfWd3jtNmZC6U_Dr2F7obQcK81I4YrK-fKEbkyU"
+sheet_url = "https://docs.google.com/spreadsheets/d/1if4KoVQvw5ZbhknfdZbzMkcTiPfsD6bz9V3a1th-bwQ"
 
-# ------------------ DATA REFRESH FUNCTION ------------------
-def refresh_data():
-    sheet = client.open_by_url(sheet_url).worksheet("Calls2")
+# ------------------ SHEET REFRESH FUNCTIONS ------------------
+def load_sheet(sheet_name):
+    sheet = client.open_by_url(sheet_url).worksheet(sheet_name)
     data = sheet.get_all_records()
     df = pd.DataFrame(data)
+    return df
 
-    df['Timestamp'] = pd.to_datetime(df['Date'].astype(str) + " " + df['Time'].astype(str), errors='coerce')
+# ------------------ REPORTS TAB ------------------
+def filter_calls_by_date(date):
+    df = load_sheet("Calls")
     df['Date'] = pd.to_datetime(df['Date'], errors='coerce').dt.date.astype(str)
-    df['Time'] = pd.to_datetime(df['Time'], format='%H:%M:%S', errors='coerce').dt.time
-
-    gps_split = df['GPS'].astype(str).str.split(',', expand=True)
-    df['Latitude'] = pd.to_numeric(gps_split[0], errors='coerce')
-    df['Longitude'] = pd.to_numeric(gps_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
+    return df[df['Date'] == date]
 
-    return df
-
-# ------------------ SUMMARY GENERATION ------------------
-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))
-
-# ------------------ MAP & TABLE VIEW ------------------
-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",
-        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",
-        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', 'Time', 'Time Diff (min)', 
-        'Interaction Type', 'Product'
-    ]].rename(columns={
-        'Dealership': 'Dealer',
-        'Time': 'Time',
-        'Time Diff (min)': 'Time Spent (min)',
-        'Interaction Type': 'Interaction',
-        'Product': 'Product Type'
-    })
-
-    total_time = round(table['Time Spent (min)'].sum(), 2)
-    summary_row = pd.DataFrame([{
-        'Visit Order': '',
-        'Dealer': f"Total Time: {total_time} min",
-        'Time': '',
-        'Time Spent (min)': '',
-        'Interaction': '',
-        'Product Type': ''
-    }])
-    table = pd.concat([table, summary_row], ignore_index=True)
-    return table, fig
+def filter_appointments_by_date(date):
+    df = load_sheet("Appointments")
+    df['Date'] = pd.to_datetime(df['Date'], errors='coerce').dt.date.astype(str)
+    return df[df['Date'] == date]
+
+# ------------------ APPOINTED LEADS ------------------
+def appointed_leads_table():
+    df = load_sheet("Appointed Leads")
+    grouped = df.groupby('Rep')['Customer Name'].apply(list).reset_index()
+    return grouped
+
+# ------------------ INTERACTIVE QUERY VIEW ------------------
+def search_table(sheet_name, field, keyword):
+    df = load_sheet(sheet_name)
+    if field not in df.columns:
+        return pd.DataFrame(), "Field not found."
+    results = df[df[field].astype(str).str.contains(keyword, case=False, na=False)]
+    return results, f"Found {len(results)} results."
 
 # ------------------ GRADIO UI ------------------
 with gr.Blocks() as app:
@@ -134,89 +60,31 @@ with gr.Blocks() as app:
             login_msg = gr.Markdown("")
 
         with gr.Column(visible=False) as main_ui:
-            gr.Markdown("## 🗂️ Bid4Cars FieldApp 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("📄 Reports"):
-                with gr.Row():
-                    report_type = gr.Radio(choices=["Daily", "Weekly", "Monthly"], label="Report Type", value="Daily")
-                    report_date = gr.Textbox(label="Enter Date (YYYY-MM-DD)", placeholder="2025-05-27")
-                    download_btn = gr.Button("📥 Download CSV")
-
-                report_table = gr.Dataframe(label="📋 Report Summary")
-                report_info = gr.Markdown()
-                download_file = gr.File(label="📁 Download Link")
-
-                def generate_report(report_type, report_date):
-                    df = refresh_data()
-                    try:
-                        date_obj = pd.to_datetime(str(report_date), errors='coerce')
-                    except:
-                        return pd.DataFrame(), "⚠️ Invalid date format.", None
-
-                    if pd.isnull(date_obj):
-                        return pd.DataFrame(), "⚠️ Date could not be parsed.", None
-
-                    if report_type == "Daily":
-                        title = f"📆 Report for {date_obj.strftime('%B %d, %Y')}"
-                        mask = df['Date'] == str(date_obj.date())
-                    elif report_type == "Weekly":
-                        start = date_obj - pd.Timedelta(days=date_obj.weekday())
-                        end = start + pd.Timedelta(days=6)
-                        title = f"📆 Week of {start.strftime('%b %d')} – {end.strftime('%b %d')}, {start.year}"
-                        mask = (pd.to_datetime(df['Date']) >= start) & (pd.to_datetime(df['Date']) <= end)
-                    elif report_type == "Monthly":
-                        title = f"📆 Report for {date_obj.strftime('%B %Y')}"
-                        mask = pd.to_datetime(df['Date']).dt.to_period("M") == pd.to_datetime(date_obj).to_period("M")
-
-                    filtered = df[mask]
-
-                    if filtered.empty:
-                        return pd.DataFrame(), "⚠️ No data found for that range.", None
-
-                    summary = filtered[[ 
-                        'Date', 'Rep Name', 'Dealership', 'Time', 
-                        'Interaction Type', 'Product', 'Time Diff (min)'
-                    ]].sort_values(by=["Rep Name", "Date"])
-
-                    insights = f"""
-### {title}
-### 📊 Insights:
-- **Total Visits:** {len(filtered)}
-- **Unique Reps:** {filtered['Rep Name'].nunique()}
-- **Most Active Rep:** {filtered['Rep Name'].value_counts().idxmax()}
-- **Most Visited Dealership:** {filtered['Dealership'].value_counts().idxmax()}
-- **Avg Time Between Visits:** {round(filtered['Time Diff (min)'].mean(), 2)} min
-                    """
-
-                    filename = f"Bid4Cars_Report_{report_type}_{date_obj.strftime('%Y-%m-%d')}.csv".replace(" ", "_")
-                    summary.to_csv(filename, index=False)
-
-                    return summary, insights, filename
-
-                report_date.change(fn=generate_report, inputs=[report_type, report_date], outputs=[report_table, report_info, download_file])
-                report_type.change(fn=generate_report, inputs=[report_type, report_date], outputs=[report_table, report_info, download_file])
-                download_btn.click(fn=generate_report, inputs=[report_type, report_date], outputs=[report_table, report_info, download_file])
+            gr.Markdown("## 🗂️ Graffiti Admin Dashboard")
+
+            with gr.Tab("📅 Calls Report"):
+                calls_date = gr.Textbox(label="Enter Date (YYYY-MM-DD)")
+                calls_table = gr.Dataframe()
+                calls_date.change(lambda d: filter_calls_by_date(d), inputs=calls_date, outputs=calls_table)
+
+            with gr.Tab("📅 Appointments Report"):
+                appt_date = gr.Textbox(label="Enter Date (YYYY-MM-DD)")
+                appt_table = gr.Dataframe()
+                appt_date.change(lambda d: filter_appointments_by_date(d), inputs=appt_date, outputs=appt_table)
+
+            with gr.Tab("🧑 Appointed Leads"):
+                leads_btn = gr.Button("View Appointed Leads")
+                leads_output = gr.Dataframe()
+                leads_btn.click(fn=appointed_leads_table, outputs=leads_output)
+
+            with gr.Tab("🔍 Query Live Sheets"):
+                sheet_choice = gr.Dropdown(choices=["LiveQuotes", "LiveCustomer", "LiveJobBags"], label="Select Sheet")
+                field_input = gr.Textbox(label="Field (column name)")
+                keyword_input = gr.Textbox(label="Keyword to search")
+                query_btn = gr.Button("Search")
+                query_table = gr.Dataframe()
+                query_info = gr.Markdown()
+                query_btn.click(fn=search_table, inputs=[sheet_choice, field_input, keyword_input], outputs=[query_table, query_info])
 
     def do_login(user, pw):
         if VALID_USERS.get(user) == pw:
@@ -226,4 +94,4 @@ with gr.Blocks() as app:
 
     login_btn.click(fn=do_login, inputs=[email, password], outputs=[login_ui, main_ui, login_msg])
 
-app.launch()
+app.launch()