Update app.py

app.py

@@ -18,7 +18,7 @@ if not API_TOKEN or not APP_PASSWORD:
 
 ACCOUNT_ID = "255333"  # PREMIUM account
 BASE_URL = "https://api.leadfeeder.com"
-PAGE_SIZE = 50 
+PAGE_SIZE = 100 
 
 HEADERS = {
     "Authorization": f"Token token={API_TOKEN}",
@@ -26,7 +26,7 @@ HEADERS = {
 }
 
 # =====================================================
-# PRESETS (DASHBOARD & TRENDS)
+# PRESETS
 # =====================================================
 DASHBOARD_PRESETS = {
     "Total Visits by Industry": ("primary_industry", "total_visits", "sum"),
@@ -39,7 +39,6 @@ DASHBOARD_PRESETS = {
     "Visits by Assignee": ("assignee", "total_visits", "sum"),
 }
 
-# --- RESTORED TREND PRESETS ---
 TREND_PRESETS = {
     "Total Visits Trend": ("total_visits", "sum", None),
     "Active Accounts Trend": ("company_name", "count", None),
@@ -144,21 +143,17 @@ def fetch_basic_leads(start_date, end_date):
     return df
 
 def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=gr.Progress()):
-    """ 
-    Step 2: Go back and fetch the specific Visit URLs for each company.
-    Includes logic to parse 'visit_route' for the correct Exit Page.
-    """
+    """ Step 2: Deep enrich with visit_route parsing for Exit Page """
     if df.empty:
         return df
 
     target_df = df.head(max_rows) if max_rows else df
     total = len(target_df)
     
-    print(f"🕵️ Deep enriching {total} rows with visit data ({start_date} to {end_date})...")
+    print(f"🕵️ Deep enriching {total} rows ({start_date} to {end_date})...")
     
     for index, row in target_df.iterrows():
         lead_id = row["lead_id"]
-        
         try:
             visit_data = make_request(
                 f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads/{lead_id}/visits",
@@ -172,7 +167,6 @@ def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=g
             
             visits = visit_data.get("data", [])
             included = visit_data.get("included", [])
-            
             landing = None
             exit_p = None
             
@@ -180,23 +174,17 @@ def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=g
                 visit = visits[0]
                 v_attrs = visit.get("attributes", {})
                 
-                # A. Direct Attributes (Often has Landing, rarely Exit)
+                # A. Direct Attributes
                 landing = v_attrs.get("landing_page_path") or v_attrs.get("landing_page_url")
                 
-                # B. Parse 'visit_route' (CRITICAL FIX for Exit Page)
+                # B. Parse 'visit_route'
                 visit_route = v_attrs.get("visit_route", [])
                 if visit_route and isinstance(visit_route, list):
-                    # Last item = Exit Page
                     last_step = visit_route[-1]
-                    if not exit_p:
-                        exit_p = last_step.get("page_path") or last_step.get("page_url")
-                    
-                    # First item = Fallback Landing Page
-                    if not landing:
-                        first_step = visit_route[0]
-                        landing = first_step.get("page_path") or first_step.get("page_url")
+                    if not exit_p: exit_p = last_step.get("page_path") or last_step.get("page_url")
+                    if not landing: landing = visit_route[0].get("page_path") or visit_route[0].get("page_url")
 
-                # C. Deep Link via 'included' Page Views (Backup)
+                # C. Deep Link via 'included' Page Views
                 if not landing or not exit_p:
                     pv_map = {p["id"]: p["attributes"] for p in included if p["type"] == "page_views"}
                     pv_ids = [r["id"] for r in visit.get("relationships", {}).get("page_views", {}).get("data", [])]
@@ -205,14 +193,12 @@ def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=g
                         if not landing:
                             first_pv = pv_map.get(pv_ids[0])
                             if first_pv: landing = first_pv.get("url") or first_pv.get("path")
-                        
                         if not exit_p:    
                             last_pv = pv_map.get(pv_ids[-1])
                             if last_pv: exit_p = last_pv.get("url") or last_pv.get("path")
 
                 df.at[index, "landing_page_path"] = landing
                 df.at[index, "exit_page_path"] = exit_p
-        
         except Exception as e:
             print(f"Failed to enrich lead {lead_id}: {e}")
             
@@ -222,7 +208,7 @@ def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=g
     return df
 
 # =====================================================
-# DEBUGGER TOOL
+# DEBUGGER
 # =====================================================
 def inspect_raw_json(lead_id, start_date, end_date):
     if not lead_id: return "Please enter a Lead ID"
@@ -240,14 +226,12 @@ def inspect_raw_json(lead_id, start_date, end_date):
         return str(e)
 
 # =====================================================
-# UI WRAPPERS
+# DATA WRAPPERS
 # =====================================================
 def load_preview(start, end):
     df = fetch_basic_leads(start, end)
-    if df.empty:
-        return df, pd.DataFrame(), "⚠️ No data found."
+    if df.empty: return df, pd.DataFrame(), "⚠️ No data found."
     
-    # Preview enrichment
     df_preview = df.copy()
     df_preview = enrich_leads_with_visits(df_preview, start, end, max_rows=10)
     
@@ -263,7 +247,7 @@ def download_full_excel(df, start, end):
     return path
 
 # =====================================================
-# CHART ENGINES
+# DASHBOARD LOGIC
 # =====================================================
 def build_kpis(df):
     if df is None or df.empty: return 0, 0, 0, 0, 0, 0
@@ -285,16 +269,41 @@ def build_dashboard(df, preset, top_n):
         grouped = df.groupby(dim, dropna=False)[metric].agg(agg).reset_index(name="value")
     return px.bar(grouped.sort_values("value", ascending=False).head(top_n), x=dim, y="value", title=preset)
 
-# --- RESTORED TREND ENGINE ---
-def build_trend(df, preset, grain):
+# =====================================================
+# TREND ENGINE (UPDATED WITH FILTERING)
+# =====================================================
+def get_trend_filter_options(df, preset):
+    """Dynamically returns available options for the selected segment"""
+    if df is None or df.empty:
+        return gr.update(choices=[], value=None, visible=False)
+        
+    metric, agg, segment = TREND_PRESETS[preset]
+    
+    if not segment:
+        return gr.update(choices=[], value=None, visible=False)
+        
+    # Get unique values for the segment (e.g. all Industries)
+    options = sorted(df[segment].dropna().unique().tolist())
+    
+    # Default to top 5 most frequent to avoid clutter
+    top_5 = df[segment].value_counts().head(5).index.tolist()
+    
+    return gr.update(choices=options, value=top_5, visible=True, label=f"Filter {segment}")
+
+def build_trend(df, preset, grain, filter_values):
     if df is None or df.empty:
         return px.line(title="No Data Loaded")
         
     metric, agg, segment = TREND_PRESETS[preset]
-    # Ensure date column is valid
     df = df.dropna(subset=["last_visit_date"]).copy()
     
-    # Create granularity column
+    # --- APPLY FILTER IF SEGMENT EXISTS ---
+    if segment and filter_values:
+        df = df[df[segment].isin(filter_values)]
+        if df.empty:
+            return px.line(title="No data for selected filters")
+
+    # Grain Logic
     if grain == "Weekly":
         df["period"] = df["last_visit_date"].dt.to_period("W").astype(str)
     elif grain == "Monthly":
@@ -308,9 +317,11 @@ def build_trend(df, preset, grain):
     else:
         val = metric
         
+    # Plotting
     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)
+        title = f"{preset} (Filtered by {len(filter_values)} items)" if filter_values else preset
+        return px.line(ts, x="period", y=val, color=segment, template="plotly_white", title=title)
     
     ts = df.groupby("period")[val].agg(agg).reset_index()
     return px.line(ts, x="period", y=val, markers=True, template="plotly_white", title=preset)
@@ -357,19 +368,31 @@ with gr.Blocks(title="Leadfeeder Pro Analytics") as demo:
             chart = gr.Plot()
             gr.Button("Build View").click(build_dashboard, [df_state, preset, top_n], chart)
 
-        # --- TAB 3: TRENDS (RESTORED) ---
+        # --- TAB 3: TRENDS (UPDATED) ---
         with gr.Tab("📈 Trends"):
             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")
             
+            # Dynamic Filter Dropdown (Hidden initially)
+            filter_dropdown = gr.Dropdown(multiselect=True, visible=False, label="Filter Segments")
+            
             plot = gr.Plot()
-            gr.Button("Show Trend", variant="primary").click(build_trend, [df_state, trend_view, grain], plot)
+            
+            # Logic: When trend view changes -> Update filter options
+            trend_view.change(get_trend_filter_options, [df_state, trend_view], filter_dropdown)
+            
+            # Logic: When Button Clicked -> Build Chart with Filters
+            gr.Button("Show Trend", variant="primary").click(
+                build_trend, 
+                [df_state, trend_view, grain, filter_dropdown], 
+                plot
+            )
 
         # --- TAB 4: DEBUGGER ---
         with gr.Tab("🛠️ Debugger"):
             gr.Markdown("Use this to inspect raw API data for a specific lead if columns are empty.")
-            dbg_id = gr.Textbox(label="Lead ID (Copy from Data tab)")
+            dbg_id = gr.Textbox(label="Lead ID")
             dbg_btn = gr.Button("Inspect Raw JSON")
             dbg_out = gr.Code(language="json")
             dbg_btn.click(inspect_raw_json, [dbg_id, start, end], dbg_out)