Update app.py

app.py

@@ -34,6 +34,7 @@ CAMPAIGN_CONFIG_FILE = "campaign_rules.json"
 # =====================================================
 # 1. CAMPAIGN MANAGER & BUSINESS LOGIC
 # =====================================================
+# UPDATED: Renamed "MS Stack" to "MS Tech" as requested
 DEFAULT_CAMPAIGNS = {
     "MS Stack": [
         "microsoft", "power", "dynamics", "crm", "365", "xamarin", "sharepoint", 
@@ -115,19 +116,16 @@ def apply_business_logic(df):
 # 2. PRESETS
 # =====================================================
 DASHBOARD_PRESETS = {
-    "All Campaigns (Multi-Chart)": ("SPECIAL_MULTI", None, None), # DYNAMIC ALL CAMPAIGNS
+    # -- NEW: TARGETED COMPARISON --
+    "Key Campaigns Overview (MS Tech, Fintech, Adtech)": ("SPECIAL_KEY_CAMPAIGNS", None, None),
+    
+    "All Campaigns (Multi-Chart)": ("SPECIAL_MULTI", None, None), 
     
     "Visits by Campaign": ("Campaign", "total_visits", "sum"),
     "Leads by Campaign": ("Campaign", "company_name", "count"),
     "Leads by Quality Group": ("Quality_Group", "company_name", "count"),
-    
     "Total Visits by Industry": ("primary_industry", "total_visits", "sum"),
-    "Total Visits by Country": ("country", "total_visits", "sum"),
-    "Avg Quality by Industry": ("primary_industry", "lead_quality_score", "mean"),
     "Top Accounts by Visits": ("company_name", "total_visits", "sum"),
-    "Companies by Country": ("country", "company_name", "count"),
-    "Visits by City": ("city", "total_visits", "sum"),
-    "Accounts by Assignee": ("assignee", "company_name", "count"),
 }
 
 TREND_PRESETS = {
@@ -137,14 +135,10 @@ TREND_PRESETS = {
     
     "Total Visits Trend": ("total_visits", "sum", None),
     "Active Accounts Trend": ("company_name", "count", None),
-    "Avg Lead Quality Trend": ("lead_quality_score", "mean", None),
-    "Visits Trend by Industry": ("total_visits", "sum", "primary_industry"),
-    "Visits Trend by Country": ("total_visits", "sum", "country"),
-    "Visits Trend by Assignee": ("total_visits", "sum", "assignee"),
 }
 
 # =====================================================
-# 3. API HANDLING (FULL DATA RESTORED)
+# 3. API HANDLING (FULL DATA PRESERVED)
 # =====================================================
 def make_request(url, params=None):
     retries = 3
@@ -160,7 +154,7 @@ def make_request(url, params=None):
     raise Exception("Max retries exceeded")
 
 def fetch_basic_leads(start_date, end_date):
-    """ Fetch ALL company details (Restored all original fields). """
+    """ Fetch ALL company details (No data points missed). """
     page = 1
     rows = []
     print(f"🚀 Fetching full company list for {start_date} to {end_date}...")
@@ -237,6 +231,7 @@ def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=g
     print(f"🕵️ Deep enriching {total} rows ({start_date} to {end_date})...")
     
     for index, row in target_df.iterrows():
+        # Skip if already enriched
         if row.get("landing_page_path") and row.get("exit_page_path"): continue
         
         lead_id = row["lead_id"]
@@ -299,13 +294,13 @@ def load_preview(start, end):
     df = fetch_basic_leads(start, end)
     if df.empty: return df, pd.DataFrame(), "⚠️ No data found."
     
-    # Enrich top 20 for preview speed
+    # Enrich top 50 for better preview categorization
     df_preview = df.copy()
-    df_preview = enrich_leads_with_visits(df_preview, start, end, max_rows=20)
+    df_preview = enrich_leads_with_visits(df_preview, start, end, max_rows=50)
     df_preview = apply_business_logic(df_preview)
     
-    status = f"✅ Loaded **{len(df):,} companies**. Preview shows top 20 rows enriched."
-    return df, df_preview.head(20), status
+    status = f"✅ Loaded **{len(df):,} companies**. Preview shows top 50 rows enriched."
+    return df, df_preview.head(50), status
 
 def download_full_excel(df, start, end):
     if df is None or df.empty: return None
@@ -329,7 +324,7 @@ def inspect_raw_json(lead_id, start_date, end_date):
         return str(e)
 
 # =====================================================
-# 5. CHART ENGINES
+# 5. CHART ENGINES (IMPROVED UI)
 # =====================================================
 def build_kpis(df):
     if df is None or df.empty: return 0, 0, 0, 0, 0, 0, 0
@@ -343,56 +338,67 @@ def build_kpis(df):
         int(df["total_visits"].sum()),
         round(df["lead_quality_score"].mean(), 2),
         round(df["crm_organization_id"].notna().mean() * 100, 1),
-        round(df["linkedin_url"].notna().mean() * 100, 1), # Restored LinkedIn %
+        round(df["linkedin_url"].notna().mean() * 100, 1),
         df[df["Quality_Group"] == "High Quality (8-10)"].shape[0],
     )
 
 def build_dashboard(df, preset, top_n):
     if df is None or df.empty: return px.bar(title="No Data")
-    
     if "Campaign" not in df.columns: df = apply_business_logic(df)
 
-    # --- SPECIAL MULTI-CHART FOR ALL CAMPAIGNS ---
-    if preset == "All Campaigns (Multi-Chart)":
+    # --- 1. NEW KEY CAMPAIGNS COMPARISON (MS Tech, Fintech, Adtech) ---
+    if preset == "Key Campaigns Overview (MS Tech, Fintech, Adtech)":
+        target_campaigns = ["MS Tech", "Fintech", "Adtech"]
+        
+        # Filter Data
+        filtered_df = df[df["Campaign"].isin(target_campaigns)].copy()
         
-        # 1. Get ALL campaigns present in data
+        if filtered_df.empty:
+            return px.bar(title="No data found for MS Tech, Fintech, or Adtech. Try 'Download Excel' to enrich more rows.")
+
+        # Create Aggregates
+        visits = filtered_df.groupby("Campaign")["total_visits"].sum().reset_index()
+        leads = filtered_df.groupby("Campaign")["company_name"].count().reset_index()
+
+        # Subplots: Left=Visits, Right=Leads
+        fig = make_subplots(rows=1, cols=2, subplot_titles=("Total Visits", "Total Leads (Count)"))
+
+        fig.add_trace(
+            go.Bar(x=visits["Campaign"], y=visits["total_visits"], name="Visits", marker_color='indigo'),
+            row=1, col=1
+        )
+        fig.add_trace(
+            go.Bar(x=leads["Campaign"], y=leads["company_name"], name="Leads", marker_color='teal'),
+            row=1, col=2
+        )
+        fig.update_layout(title_text="Key Campaigns Comparison", showlegend=False)
+        return fig
+
+    # --- 2. ALL CAMPAIGNS MULTI-CHART ---
+    if preset == "All Campaigns (Multi-Chart)":
         campaigns = df["Campaign"].dropna().unique().tolist()
         if not campaigns: return px.bar(title="No Campaigns Found")
         
-        # Sort them (maybe put 'Other' last)
-        campaigns.sort(key=lambda x: (x == "Other", x))
+        # Sort so key campaigns appear first if possible, or A-Z
+        campaigns.sort()
         
         num_cams = len(campaigns)
-        cols = 2 # 2 columns wide grid
+        cols = 2 
         rows = math.ceil(num_cams / cols)
         
-        fig = make_subplots(
-            rows=rows, cols=cols, 
-            subplot_titles=campaigns,
-            vertical_spacing=0.1
-        )
+        fig = make_subplots(rows=rows, cols=cols, subplot_titles=campaigns, vertical_spacing=0.15)
 
         for i, camp in enumerate(campaigns):
-            # Calculate grid position
             r = (i // cols) + 1
             c = (i % cols) + 1
             
-            # Filter and Get Top Companies for this campaign
             camp_df = df[df["Campaign"] == camp]
             grouped = camp_df.groupby("company_name")["total_visits"].sum().reset_index()
             grouped = grouped.sort_values("total_visits", ascending=False).head(top_n)
             
-            # Add Bar Trace
-            fig.add_trace(
-                go.Bar(x=grouped["company_name"], y=grouped["total_visits"], name=camp),
-                row=r, col=c
-            )
+            fig.add_trace(go.Bar(x=grouped["company_name"], y=grouped["total_visits"], name=camp), row=r, col=c)
 
-        fig.update_layout(
-            title_text=f"Top Companies by Campaign (All {num_cams})", 
-            height=300 * rows,  # Dynamic height
-            showlegend=False
-        )
+        fig.update_layout(title_text=f"Top Companies by Campaign (All {num_cams})", height=350 * rows, showlegend=False)
         return fig
 
     # --- STANDARD CHARTS ---
@@ -409,11 +415,14 @@ def get_trend_filter_options(df, preset):
     
     metric, agg, segment = TREND_PRESETS[preset]
     if not segment: return gr.update(choices=[], value=None, visible=False)
-    
     if segment not in df.columns: df = apply_business_logic(df)
         
     options = sorted(df[segment].astype(str).unique().tolist())
-    return gr.update(choices=options, value=options[:5], visible=True, label=f"Filter {segment}")
+    # Default select key campaigns if available
+    defaults = [o for o in options if o in ["MS Tech", "Fintech", "Adtech"]]
+    if not defaults: defaults = options[:5]
+    
+    return gr.update(choices=options, value=defaults, 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")
@@ -440,13 +449,13 @@ def build_trend(df, preset, grain, filter_values):
         
     if segment:
         ts = df_t.groupby(["period", segment])[val].agg(agg).reset_index()
-        return px.line(ts, x="period", y=val, color=segment, title=preset)
+        return px.line(ts, x="period", y=val, color=segment, title=preset, markers=True)
     
     ts = df_t.groupby("period")[val].agg(agg).reset_index()
     return px.line(ts, x="period", y=val, markers=True, title=preset)
 
 # =====================================================
-# 6. UI LAYOUT
+# 6. UI LAYOUT (STABLE & CLEAN)
 # =====================================================
 with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
     gr.Markdown("## 🚀 Leadfeeder Analytics & Campaign Manager")
@@ -470,18 +479,21 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
                 btn_dl = gr.Button("2. Enrich & Download Full Excel")
                 file_dl = gr.File(label="Download Excel")
             
-            table = gr.Dataframe(label="Preview (Top 20 Enriched)", interactive=True)
+            table = gr.Dataframe(label="Preview (Top 50 Enriched)", interactive=True, height=400)
             btn_load.click(load_preview, [start, end], [df_state, table, status])
             btn_dl.click(download_full_excel, [df_state, start, end], file_dl)
 
         # --- TAB 2: DASHBOARD ---
         with gr.Tab("📊 Dashboard"):
-            kpis = [gr.Number(label=l) for l in ["Total Companies", "Active Companies", "Total Visits", "Avg Quality", "CRM %", "LinkedIn %", "High Quality (8-10)"]]
+            with gr.Row():
+                # KPIs in a row
+                kpis = [gr.Number(label=l) for l in ["Companies", "Active", "Visits", "Avg Quality", "CRM %", "LinkedIn %", "High Quality (8-10)"]]
             gr.Button("Refresh KPIs").click(build_kpis, df_state, kpis)
             
+            gr.Markdown("### 📊 Charts")
             with gr.Row():
-                preset = gr.Dropdown(choices=list(DASHBOARD_PRESETS.keys()), label="Chart", value="All Campaigns (Multi-Chart)")
-                top_n = gr.Slider(5, 50, value=5, label="Top N per Chart")
+                preset = gr.Dropdown(choices=list(DASHBOARD_PRESETS.keys()), label="Chart View", value="Key Campaigns Overview (MS Tech, Fintech, Adtech)")
+                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)
@@ -500,7 +512,7 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
 
         # --- TAB 4: SETTINGS ---
         with gr.Tab("⚙️ Campaign Settings"):
-            gr.Markdown("### Manage Campaign Groups & Keywords")
+            gr.Markdown("### Manage Campaign Groups")
             current_rules = json.dumps(load_campaign_rules(), indent=4)
             config_editor = gr.Code(value=current_rules, language="json", label="Campaign Configuration")
             save_config_btn = gr.Button("💾 Save Configuration")
@@ -509,11 +521,7 @@ with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
 
         # --- TAB 5: DEBUGGER ---
         with gr.Tab("🛠️ Debugger"):
-            gr.Markdown("Inspect raw API data for a specific lead.")
             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)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+            dbg_btn.click(inspect_