Update app.py

app.py

@@ -101,48 +101,79 @@ def apply_business_logic(df):
     if "lead_quality_score" in df.columns:
         df["Quality_Group"] = df["lead_quality_score"].apply(categorize_quality)
         
-    # 2. Campaign Logic (Strict Landing Page Only)
+    # 2. Campaign Logic (Hybrid Strict Match)
     rules = load_campaign_rules()
     
     def resolve_campaign(row):
-        path = row.get("landing_page_path")
-        if path and path not in ["", "/", "/home"]:
+        # Clean path to be safe
+        path = str(row.get("landing_page_path", "")).strip().lower()
+        
+        # SCENARIO 1: We HAVE a specific landing page path (e.g., /blog/..., /services/...)
+        # We strictly enforce keyword matching here to avoid generic pages getting grouped.
+        if path and path not in ["", "/", "/home", "nan", "none"]:
             match = get_campaign_match(path, rules)
-            if match:
-                return match
-        return "Uncategorized"
+            return match if match else "Uncategorized"
+            
+        # SCENARIO 2: The path is missing (unenriched preview) OR it's a generic Homepage
+        # Here we fallback to website and industry so your dashboard actually populates!
+        website = str(row.get("website_url", "")).strip().lower()
+        industry = str(row.get("primary_industry", "")).strip().lower()
+        
+        match = get_campaign_match(website, rules)
+        if match: return match
+        
+        match = get_campaign_match(industry, rules)
+        if match: return match
         
+        return "Uncategorized"
+
     df["Campaign"] = df.apply(resolve_campaign, axis=1)
     return df
 
 # =====================================================
-# 2. PRESETS 
+# 2. PRESETS (UPDATED WITH COUNTRY/CITY/INDUSTRY)
 # =====================================================
 DASHBOARD_PRESETS = {
+    # -- SPECIAL VIEWS --
     "Key Campaigns Bifurcation (MS Tech, Fintech, Adtech)": ("SPECIAL_KEY_BIFURCATION", None, None),
     "All Campaigns Performance": ("SPECIAL_ALL_PERFORMANCE", None, None),
+    
+    # -- CAMPAIGN & QUALITY --
     "Visits by Campaign": ("Campaign", "total_visits", "sum"),
     "Leads by Campaign": ("Campaign", "company_name", "count"),
     "Leads by Quality Group": ("Quality_Group", "company_name", "count"),
+    
+    # -- GEOGRAPHY (NEW) --
     "Visits by Country": ("country", "total_visits", "sum"),
     "Leads by Country": ("country", "company_name", "count"),
     "Visits by City": ("city", "total_visits", "sum"),
     "Leads by City": ("city", "company_name", "count"),
+    
+    # -- INDUSTRY (NEW) --
     "Visits by Industry": ("primary_industry", "total_visits", "sum"),
     "Leads by Industry": ("primary_industry", "company_name", "count"),
+    
+    # -- OTHERS --
     "Top Accounts by Visits": ("company_name", "total_visits", "sum"),
 }
 
 TREND_PRESETS = {
+    # -- CAMPAIGN & QUALITY --
     "Visits Trend by Campaign": ("total_visits", "sum", "Campaign"),
     "Leads Trend by Campaign": ("company_name", "count", "Campaign"),
     "Visits Trend by Quality": ("total_visits", "sum", "Quality_Group"),
+    
+    # -- GEOGRAPHY (NEW) --
     "Visits Trend by Country": ("total_visits", "sum", "country"),
     "Leads Trend by Country": ("company_name", "count", "country"),
     "Visits Trend by City": ("total_visits", "sum", "city"),
     "Leads Trend by City": ("company_name", "count", "city"),
+    
+    # -- INDUSTRY (NEW) --
     "Visits Trend by Industry": ("total_visits", "sum", "primary_industry"),
     "Leads Trend by Industry": ("company_name", "count", "primary_industry"),
+    
+    # -- GENERAL --
     "Total Visits Trend": ("total_visits", "sum", None),
     "Active Accounts Trend": ("company_name", "count", None),
 }
@@ -358,26 +389,6 @@ 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")
-        
-    # --- NEW: Automatically render a Choropleth Map for country data ---
-    if dim == "country":
-        # Sort to put the highest values on top
-        grouped = grouped.sort_values("value", ascending=False)
-        
-        fig = px.choropleth(
-            grouped,
-            locations=dim,                  # Maps directly to the "country" column
-            locationmode="country names",   # Plotly automatically identifies country names
-            color="value",                  # The heat map scale
-            hover_name=dim,
-            title=preset,
-            color_continuous_scale="Blues"  # A clean blue heat map gradient
-        )
-        # Update layout to remove margins and make the map look crisp
-        fig.update_layout(margin={"r":0,"t":40,"l":0,"b":0})
-        return fig
-    # -------------------------------------------------------------------
-        
     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):
@@ -463,7 +474,7 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
             gr.Markdown("### 📊 Charts")
             with gr.Row():
                 preset = gr.Dropdown(choices=list(DASHBOARD_PRESETS.keys()), label="Chart View", value="Key Campaigns Bifurcation (MS Tech, Fintech, Adtech)")
-                top_n = gr.Slider(5, 50, value=10, label="Top N Items (Not applicable to Maps)")
+                top_n = gr.Slider(5, 50, value=10, label="Top N Items")
             chart = gr.Plot()
             gr.Button("Build View").click(build_dashboard, [df_state, preset, top_n], chart)
             
@@ -480,7 +491,7 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
         # --- TAB 4: SETTINGS ---
         with gr.Tab("⚙️ Campaign Settings"):
             gr.Markdown("### Manage Campaign Groups")
-            gr.Markdown("Select a campaign below and paste your keywords separated by commas. Changes take effect on the next data load.")
+            gr.Markdown("Select a campaign below and paste your keywords separated by commas.")
             
             init_rules = load_campaign_rules()
             camp_choices = list(init_rules.keys()) + ["+ Create New Campaign"]