Update app.py

app.py

@@ -90,30 +90,37 @@ def get_campaign_match(text, rules):
 
 def apply_business_logic(df):
     if df is None or df.empty: return df
-    
+        
     # 1. Quality Group
     if "lead_quality_score" in df.columns:
         df["Quality_Group"] = df["lead_quality_score"].apply(categorize_quality)
-        
+            
     # 2. Campaign Logic (Smart Fallback)
     rules = load_campaign_rules()
-    
+        
     def resolve_campaign(row):
         # Priority 1: Landing Page
         if row.get("landing_page_path"):
             match = get_campaign_match(row["landing_page_path"], rules)
             if match: return match
-        # Priority 2: Website URL
+            
+        # Priority 2: Exit Page (NEW LOGIC)
+        if row.get("exit_page_path"):
+            match = get_campaign_match(row["exit_page_path"], rules)
+            if match: return match
+
+        # Priority 3: Website URL
         if row.get("website_url"):
             match = get_campaign_match(row["website_url"], rules)
             if match: return match
-        # Priority 3: Industry
+
+        # Priority 4: Industry
         if row.get("primary_industry"):
             match = get_campaign_match(row["primary_industry"], rules)
             if match: return match
-            
+                
         return "Uncategorized"
-
+        
     df["Campaign"] = df.apply(resolve_campaign, axis=1)
     return df
 
@@ -274,7 +281,7 @@ def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=g
                     if not landing:
                         first_step = visit_route[0]
                         landing = first_step.get("page_path") or first_step.get("page_url")
-                        
+                
                 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", [])]
@@ -350,10 +357,12 @@ def build_dashboard(df, preset, top_n):
         target = ["MS Tech", "Fintech", "Adtech"]
         filtered = df[df["Campaign"].isin(target)].copy()
         if filtered.empty: return px.bar(title="No Data for Key Campaigns")
+        
         agg = filtered.groupby("Campaign").agg(
             Leads_Count=("company_name", "count"),
             Total_Visits=("total_visits", "sum")
         ).reset_index()
+        
         fig = go.Figure()
         fig.add_trace(go.Bar(x=agg["Campaign"], y=agg["Leads_Count"], name="No. of Leads", marker_color="#00C49F"))
         fig.add_trace(go.Bar(x=agg["Campaign"], y=agg["Total_Visits"], name="Total Visits", marker_color="#FFBB28"))
@@ -365,6 +374,7 @@ def build_dashboard(df, preset, top_n):
             Leads_Count=("company_name", "count"),
             Total_Visits=("total_visits", "sum")
         ).reset_index().sort_values("Leads_Count", ascending=False)
+        
         fig = make_subplots(specs=[[{"secondary_y": True}]])
         fig.add_trace(go.Bar(x=agg["Campaign"], y=agg["Leads_Count"], name="Leads", marker_color="indigo"), secondary_y=False)
         fig.add_trace(go.Scatter(x=agg["Campaign"], y=agg["Total_Visits"], name="Visits", mode="lines+markers", line=dict(color="orange", width=3)), secondary_y=True)
@@ -376,11 +386,13 @@ def build_dashboard(df, preset, top_n):
         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")
+        
     return px.bar(grouped.sort_values("value", ascending=False).head(top_n), x=dim, y="value", title=preset, color=dim)
 
 def build_trend(df, preset, grain, filter_values):
     if df is None or df.empty: return px.line(title="No Data")
     metric, agg, segment = TREND_PRESETS[preset]
+    
     df_t = df.dropna(subset=["last_visit_date"]).copy()
     
     if segment and segment not in df_t.columns: 
@@ -472,6 +484,7 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
                 grain = gr.Radio(["Daily", "Weekly", "Monthly"], value="Daily", label="Granularity")
             filter_dropdown = gr.Dropdown(multiselect=True, visible=False, label="Filter Segments")
             plot = gr.Plot()
+            
             trend_view.change(get_trend_filter_options, [df_state, trend_view], filter_dropdown)
             gr.Button("Show Trend", variant="primary").click(build_trend, [df_state, trend_view, grain, filter_dropdown], plot)
             
@@ -512,7 +525,7 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
                 
                 if not target_camp:
                     return "❌ Error: Campaign name cannot be empty.", gr.update()
-                
+                    
                 # 1. Update the dictionary
                 rules[target_camp] = clean_kws
                 
@@ -537,10 +550,10 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
                         status_message = f"⚠️ Saved locally, but failed to push to Hugging Face (Check HF_TOKEN). Error: {e}"
                 elif not HF_TOKEN:
                     status_message = f"⚠️ Saved locally. To make this permanent on Hugging Face, add an HF_TOKEN secret in your Space settings."
-                
+                    
                 updated_choices = list(rules.keys()) + ["+ Create New Campaign"]
                 return status_message, gr.update(choices=updated_choices, value=target_camp)
-
+                
             save_config_btn.click(save_easy_config, inputs=[camp_dropdown, new_camp_name, kw_input], outputs=[config_status, camp_dropdown])
             
         # --- TAB 5: DEBUGGER ---