Update app.py

app.py

@@ -12,7 +12,7 @@ API_TOKEN = os.getenv("LEADFEEDER_API_TOKEN")
 APP_PASSWORD = os.getenv("APP_PASSWORD")
 
 if not API_TOKEN or not APP_PASSWORD:
-    raise RuntimeError("Missing required secrets")
+    raise RuntimeError("Missing required secrets: LEADFEEDER_API_TOKEN or APP_PASSWORD")
 
 ACCOUNT_ID = "255333"  # PREMIUM account
 BASE_URL = "https://api.leadfeeder.com"
@@ -64,81 +64,96 @@ def authenticate(pwd):
     return True
 
 # =====================================================
-# DATA FETCH (WORKING)
+# DATA FETCH (FIXED)
 # =====================================================
 def fetch_company_data(start_date, end_date):
-    page, rows = 1, []
-
+    page = 1
+    rows = []
+    
     while True:
-        r = requests.get(
-            f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads",
-            headers=HEADERS,
-            params={
-                "start_date": start_date,
-                "end_date": end_date,
-                "page[number]": page,
-                "page[size]": PAGE_SIZE
-            },
-            timeout=30
-        )
-        r.raise_for_status()
-        js = r.json()
-
-        data = js.get("data", [])
-        if not data:
+        try:
+            r = requests.get(
+                f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads",
+                headers=HEADERS,
+                params={
+                    "start_date": start_date,
+                    "end_date": end_date,
+                    "page[number]": page,
+                    "page[size]": PAGE_SIZE
+                },
+                timeout=30
+            )
+            r.raise_for_status()
+            js = r.json()
+            data = js.get("data", [])
+            
+            if not data:
+                break
+            
+            # Create a lookup map for included location objects
+            loc_map = {
+                i["id"]: i["attributes"]
+                for i in js.get("included", [])
+                if i["type"] == "locations"
+            }
+            
+            for lead in data:
+                a = lead["attributes"]
+                
+                # Get location ID safely
+                loc_id = lead.get("relationships", {}).get("location", {}).get("data", {}).get("id")
+                loc = loc_map.get(loc_id, {})
+                
+                rows.append({
+                    "lead_id": lead.get("id"),
+                    "company_name": a.get("name"),
+                    "website_url": a.get("website_url"),
+                    "phone": a.get("phone"),
+                    "business_id": a.get("business_id"),
+                    "primary_industry": a.get("industry"),
+                    "all_industries": ", ".join(
+                        [i.get("name") for i in a.get("industries", [])]
+                    ) if a.get("industries") else None,
+                    "first_visit_date": a.get("first_visit_date"),
+                    "last_visit_date": a.get("last_visit_date"),
+                    "total_visits": a.get("visits"),
+                    "lead_quality_score": a.get("quality"),
+                    "employee_count": a.get("employee_count"),
+                    "employees_min": a.get("employees_range", {}).get("min") 
+                        if a.get("employees_range") else None,
+                    "employees_max": a.get("employees_range", {}).get("max") 
+                        if a.get("employees_range") else None,
+                    "revenue": a.get("revenue"),
+                    "assignee": a.get("assignee"),
+                    "emailed_to": a.get("emailed_to"),
+                    "crm_lead_id": a.get("crm_lead_id"),
+                    "crm_organization_id": a.get("crm_organization_id"),
+                    "tags": ", ".join(a.get("tags", [])) if a.get("tags") else None,
+                    "linkedin_url": a.get("linkedin_url"),
+                    "twitter_handle": a.get("twitter_handle"),
+                    "facebook_url": a.get("facebook_url"),
+                    
+                    # --- FIXED SECTION START ---
+                    # Using .get() to avoid NameError. 
+                    "landing_page_path": a.get("landing_page_path"),
+                    "exit_page_path": a.get("exit_page_path"),
+                    # --- FIXED SECTION END ---
+                    
+                    "country": loc.get("country"),
+                    "region": loc.get("region"),
+                    "city": loc.get("city"),
+                    "leadfeeder_url": a.get("view_in_leadfeeder"),
+                })
+            
+            page += 1
+            
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching page {page}: {e}")
             break
 
-        loc_map = {
-            i["id"]: i["attributes"]
-            for i in js.get("included", [])
-            if i["type"] == "locations"
-        }
-
-        for lead in data:
-            a = lead["attributes"]
-            loc_id = lead.get("relationships", {}).get("location", {}).get("data", {}).get("id")
-            loc = loc_map.get(loc_id, {})
-
-            rows.append({
-                "lead_id": lead.get("id"),
-                "company_name": a.get("name"),
-                "website_url": a.get("website_url"),
-                "phone": a.get("phone"),
-                "business_id": a.get("business_id"),
-                "primary_industry": a.get("industry"),
-                "all_industries": ", ".join(
-                    [i.get("name") for i in a.get("industries", [])]
-                ) if a.get("industries") else None,
-                "first_visit_date": a.get("first_visit_date"),
-                "last_visit_date": a.get("last_visit_date"),
-                "total_visits": a.get("visits"),
-                "lead_quality_score": a.get("quality"),
-                "employee_count": a.get("employee_count"),
-                "employees_min": a.get("employees_range", {}).get("min")
-                    if a.get("employees_range") else None,
-                "employees_max": a.get("employees_range", {}).get("max")
-                    if a.get("employees_range") else None,
-                "revenue": a.get("revenue"),
-                "assignee": a.get("assignee"),
-                "emailed_to": a.get("emailed_to"),
-                "crm_lead_id": a.get("crm_lead_id"),
-                "crm_organization_id": a.get("crm_organization_id"),
-                "tags": ", ".join(a.get("tags", [])) if a.get("tags") else None,
-                "linkedin_url": a.get("linkedin_url"),
-                "twitter_handle": a.get("twitter_handle"),
-                "facebook_url": a.get("facebook_url"),
-                "landing_page_path": landing,
-                "country": loc.get("country"),
-                "exit_page_path": exit_page,
-                "region": loc.get("region"),
-                "city": loc.get("city"),
-                "leadfeeder_url": a.get("view_in_leadfeeder"),
-            })
-
-        page += 1
-
     df = pd.DataFrame(rows)
-    df["last_visit_date"] = pd.to_datetime(df["last_visit_date"], errors="coerce")
+    if not df.empty:
+        df["last_visit_date"] = pd.to_datetime(df["last_visit_date"], errors="coerce")
     return df
 
 # =====================================================
@@ -146,6 +161,9 @@ def fetch_company_data(start_date, end_date):
 # =====================================================
 def load_data_with_preview(start_date, end_date):
     df = fetch_company_data(start_date, end_date)
+    if df.empty:
+        return df, pd.DataFrame(), "⚠️ No data found for this date range."
+        
     preview = df.head(500)
     status = f"✅ Loaded **{len(df):,} companies** (showing first 500 rows)"
     return df, preview, status
@@ -154,6 +172,8 @@ def load_data_with_preview(start_date, end_date):
 # EXCEL EXPORT
 # =====================================================
 def export_excel(df):
+    if df is None or df.empty:
+        return None
     path = "/tmp/leadfeeder_company_data.xlsx"
     df.to_excel(path, index=False)
     return path
@@ -162,6 +182,9 @@ def export_excel(df):
 # KPI CARDS
 # =====================================================
 def build_kpis(df):
+    if df is None or df.empty:
+        return 0, 0, 0, 0, 0, 0
+        
     return (
         len(df),
         df["total_visits"].gt(0).sum(),
@@ -175,12 +198,16 @@ def build_kpis(df):
 # DASHBOARD ENGINE
 # =====================================================
 def build_dashboard(df, preset, top_n):
+    if df is None or df.empty:
+        return px.bar(title="No Data Loaded")
+        
     dim, metric, agg = DASHBOARD_PRESETS[preset]
+    
     if agg == "count":
         grouped = df.groupby(dim, dropna=False).size().reset_index(name="value")
     else:
         grouped = df.groupby(dim, dropna=False)[metric].agg(agg).reset_index(name="value")
-
+        
     grouped = grouped.sort_values("value", ascending=False).head(top_n)
     return px.bar(grouped, x=dim, y="value", template="plotly_white", title=preset)
 
@@ -188,26 +215,29 @@ def build_dashboard(df, preset, top_n):
 # TREND ENGINE
 # =====================================================
 def build_trend(df, preset, grain):
+    if df is None or df.empty:
+        return px.line(title="No Data Loaded")
+        
     metric, agg, segment = TREND_PRESETS[preset]
     df = df.dropna(subset=["last_visit_date"]).copy()
-
+    
     if grain == "Weekly":
         df["period"] = df["last_visit_date"].dt.to_period("W").astype(str)
     elif grain == "Monthly":
         df["period"] = df["last_visit_date"].dt.to_period("M").astype(str)
     else:
         df["period"] = df["last_visit_date"].dt.date
-
+        
     if agg == "count":
         df["_v"] = df[metric].notna().astype(int)
         val = "_v"
     else:
         val = metric
-
+        
     if segment:
         ts = df.groupby(["period", segment])[val].agg(agg).reset_index()
         return px.line(ts, x="period", y=val, color=segment, template="plotly_white", title=preset)
-
+    
     ts = df.groupby("period")[val].agg(agg).reset_index()
     return px.line(ts, x="period", y=val, markers=True, template="plotly_white", title=preset)
 
@@ -216,30 +246,32 @@ def build_trend(df, preset, grain):
 # =====================================================
 with gr.Blocks(title="Leadfeeder Company-Level Analytics") as demo:
     gr.Markdown("## 🔐 Login")
-    pwd = gr.Textbox(type="password")
+    pwd = gr.Textbox(type="password", label="Enter App Password")
     gr.Button("Login").click(authenticate, pwd, None)
 
     df_state = gr.State()
     status = gr.Markdown()
 
     with gr.Tabs():
-
         # ================= DATA =================
         with gr.Tab("📋 Data"):
-            start = gr.Textbox(value=(date.today() - timedelta(days=30)).isoformat())
-            end = gr.Textbox(value=date.today().isoformat())
-
-            load_btn = gr.Button("Load Data")
-            preview_table = gr.Dataframe(interactive=True)
-            download_btn = gr.Button("⬇️ Download Excel")
-            download_file = gr.File()
-
+            with gr.Row():
+                start = gr.Textbox(label="Start Date", value=(date.today() - timedelta(days=30)).isoformat())
+                end = gr.Textbox(label="End Date", value=date.today().isoformat())
+            
+            load_btn = gr.Button("Load Data", variant="primary")
+            
+            with gr.Row():
+                download_btn = gr.Button("⬇️ Download Excel")
+                download_file = gr.File(label="Download")
+            
+            preview_table = gr.Dataframe(interactive=True, label="Data Preview")
+            
             load_btn.click(
                 load_data_with_preview,
                 [start, end],
                 [df_state, preview_table, status]
             )
-
             download_btn.click(export_excel, df_state, download_file)
 
         # ================= DASHBOARD =================
@@ -251,21 +283,24 @@ with gr.Blocks(title="Leadfeeder Company-Level Analytics") as demo:
                 k4 = gr.Number(label="Avg Quality")
                 k5 = gr.Number(label="% CRM Coverage")
                 k6 = gr.Number(label="% LinkedIn Coverage")
-
+            
             gr.Button("Refresh KPIs").click(build_kpis, df_state, [k1,k2,k3,k4,k5,k6])
-
-            preset = gr.Dropdown(choices=list(DASHBOARD_PRESETS.keys()))
-            top_n = gr.Slider(5, 50, value=10, step=5)
+            
+            with gr.Row():
+                preset = gr.Dropdown(choices=list(DASHBOARD_PRESETS.keys()), label="Select Chart")
+                top_n = gr.Slider(5, 50, value=10, step=5, label="Top N")
+            
             chart = gr.Plot()
-
-            gr.Button("Build View").click(build_dashboard, [df_state, preset, top_n], chart)
+            gr.Button("Build View", variant="primary").click(build_dashboard, [df_state, preset, top_n], chart)
 
         # ================= TRENDS =================
         with gr.Tab("📈 Trends"):
-            trend_view = gr.Dropdown(choices=list(TREND_PRESETS.keys()))
-            grain = gr.Radio(["Daily", "Weekly", "Monthly"], value="Daily")
+            with gr.Row():
+                trend_view = gr.Dropdown(choices=list(TREND_PRESETS.keys()), label="Select Trend")
+                grain = gr.Radio(["Daily", "Weekly", "Monthly"], value="Daily", label="Granularity")
+            
             plot = gr.Plot()
+            gr.Button("Show Trend", variant="primary").click(build_trend, [df_state, trend_view, grain], plot)
 
-            gr.Button("Show Trend").click(build_trend, [df_state, trend_view, grain], plot)
-
-demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)