Update app.py

app.py

@@ -25,6 +25,30 @@ HEADERS = {
     "Accept": "application/json"
 }
 
+# =====================================================
+# PRESETS (DASHBOARD & TRENDS)
+# =====================================================
+DASHBOARD_PRESETS = {
+    "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"),
+    "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),
+    "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"),
+}
+
 # =====================================================
 # UTILS: RATE LIMIT HANDLER
 # =====================================================
@@ -45,9 +69,7 @@ def make_request(url, params=None):
 # DATA FETCHING
 # =====================================================
 def fetch_basic_leads(start_date, end_date):
-    """ 
-    Step 1: Fetch ALL company details (fast fetch). 
-    """
+    """ Step 1: Fetch ALL company details (fast fetch). """
     page = 1
     rows = []
     
@@ -69,7 +91,6 @@ def fetch_basic_leads(start_date, end_date):
             if not data:
                 break
                 
-            # Map Included Locations
             included = js.get("included", [])
             loc_map = {str(i["id"]): i["attributes"] for i in included if i["type"] == "locations"}
             
@@ -125,7 +146,7 @@ def fetch_basic_leads(start_date, end_date):
 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.
-    FIX: Now specifically parses 'visit_route' to find the Exit Page.
+    Includes logic to parse 'visit_route' for the correct Exit Page.
     """
     if df.empty:
         return df
@@ -159,25 +180,23 @@ def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=g
                 visit = visits[0]
                 v_attrs = visit.get("attributes", {})
                 
-                # --- STRATEGY 1: Direct Attributes ---
+                # A. Direct Attributes (Often has Landing, rarely Exit)
                 landing = v_attrs.get("landing_page_path") or v_attrs.get("landing_page_url")
                 
-                # --- STRATEGY 2: Parse 'visit_route' (The FIX for Exit Page) ---
-                # The visit_route is a list of all pages visited. 
-                # Last item in list = Exit Page.
+                # B. Parse 'visit_route' (CRITICAL FIX for Exit Page)
                 visit_route = v_attrs.get("visit_route", [])
                 if visit_route and isinstance(visit_route, list):
-                    # Get Last Item for Exit Page
+                    # 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")
                     
-                    # Fallback for Landing Page (First Item)
+                    # 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")
 
-                # --- STRATEGY 3: Deep Link via 'included' Page Views (Backup) ---
+                # C. Deep Link via 'included' Page Views (Backup)
                 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", [])]
@@ -221,13 +240,14 @@ def inspect_raw_json(lead_id, start_date, end_date):
         return str(e)
 
 # =====================================================
-# UI LOGIC
+# UI WRAPPERS
 # =====================================================
 def load_preview(start, end):
     df = fetch_basic_leads(start, end)
     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)
     
@@ -236,26 +256,15 @@ def load_preview(start, end):
 
 def download_full_excel(df, start, end):
     if df is None or df.empty: return None
-    
     print("⏳ Starting full enrichment for Excel export...")
     enriched_df = enrich_leads_with_visits(df.copy(), start, end) 
-    
     path = "/tmp/leadfeeder_company_data_full.xlsx"
     enriched_df.to_excel(path, index=False)
     return path
 
 # =====================================================
-# DASHBOARD LOGIC (KPIs)
+# CHART ENGINES
 # =====================================================
-DASHBOARD_PRESETS = {
-    "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"),
-}
-
 def build_kpis(df):
     if df is None or df.empty: return 0, 0, 0, 0, 0, 0
     return (
@@ -276,6 +285,36 @@ 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):
+    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
+    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)
+
 # =====================================================
 # UI LAYOUT
 # =====================================================
@@ -318,7 +357,16 @@ 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: DEBUGGER ---
+        # --- TAB 3: TRENDS (RESTORED) ---
+        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")
+            
+            plot = gr.Plot()
+            gr.Button("Show Trend", variant="primary").click(build_trend, [df_state, trend_view, grain], 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)")