Update app.py

app.py

@@ -1,6 +1,4 @@
 import os
-import time
-import random
 import requests
 import pandas as pd
 import gradio as gr
@@ -25,23 +23,6 @@ HEADERS = {
     "Accept": "application/json"
 }
 
-# =====================================================
-# SAFE REQUEST (RATE LIMIT HANDLING)
-# =====================================================
-def safe_get(url, params=None, max_retries=5):
-    for attempt in range(max_retries):
-        r = requests.get(url, headers=HEADERS, params=params, timeout=30)
-
-        if r.status_code == 429:
-            sleep_time = (2 ** attempt) + random.uniform(0.5, 1.5)
-            time.sleep(sleep_time)
-            continue
-
-        r.raise_for_status()
-        return r
-
-    raise RuntimeError("Leadfeeder API rate limit exceeded. Try smaller date range.")
-
 # =====================================================
 # DASHBOARD PRESETS
 # =====================================================
@@ -83,57 +64,26 @@ def authenticate(pwd):
     return True
 
 # =====================================================
-# VISITS → LANDING & EXIT PAGE (THROTTLED)
-# =====================================================
-def fetch_landing_exit_pages(lead_id, start_date, end_date):
-    try:
-        r = safe_get(
-            f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads/{lead_id}/visits",
-            params={
-                "start_date": start_date,
-                "end_date": end_date,
-                "page[size]": 50
-            }
-        )
-
-        visits = r.json().get("data", [])
-        if not visits:
-            return None, None
-
-        visits = sorted(visits, key=lambda v: v["attributes"].get("started_at", ""))
-
-        first_route = visits[0]["attributes"].get("visit_route", [])
-        last_route = visits[-1]["attributes"].get("visit_route", [])
-
-        landing = first_route[0]["page_path"] if first_route else None
-        exit_page = last_route[-1]["page_path"] if last_route else None
-
-        # IMPORTANT: throttle visit-level calls
-        time.sleep(0.25)
-
-        return landing, exit_page
-
-    except Exception:
-        return None, None
-
-# =====================================================
-# DATA FETCH (COMPANY LEVEL – RATE SAFE)
+# DATA FETCH (WORKING)
 # =====================================================
 def fetch_company_data(start_date, end_date):
     page, rows = 1, []
 
     while True:
-        r = safe_get(
+        r = requests.get(
             f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads",
+            headers=HEADERS,
             params={
                 "start_date": start_date,
                 "end_date": end_date,
                 "page[number]": page,
                 "page[size]": PAGE_SIZE
-            }
+            },
+            timeout=30
         )
-
+        r.raise_for_status()
         js = r.json()
+
         data = js.get("data", [])
         if not data:
             break
@@ -149,10 +99,6 @@ def fetch_company_data(start_date, end_date):
             loc_id = lead.get("relationships", {}).get("location", {}).get("data", {}).get("id")
             loc = loc_map.get(loc_id, {})
 
-            landing, exit_page = fetch_landing_exit_pages(
-                lead.get("id"), start_date, end_date
-            )
-
             rows.append({
                 "lead_id": lead.get("id"),
                 "company_name": a.get("name"),
@@ -187,29 +133,25 @@ def fetch_company_data(start_date, end_date):
                 "region": loc.get("region"),
                 "city": loc.get("city"),
                 "leadfeeder_url": a.get("view_in_leadfeeder"),
-                "landing_page_path": landing,
-                "exit_page_path": exit_page,
             })
 
         page += 1
-        time.sleep(0.3)  # IMPORTANT pagination throttle
 
     df = pd.DataFrame(rows)
     df["last_visit_date"] = pd.to_datetime(df["last_visit_date"], errors="coerce")
     return df
 
 # =====================================================
-# LOAD WRAPPER (NO CRASH)
+# LOAD WRAPPER (STATE SAFE)
 # =====================================================
 def load_data_with_preview(start_date, end_date):
-    try:
-        df = fetch_company_data(start_date, end_date)
-        return df, df.head(500), f"✅ Loaded {len(df):,} companies"
-    except Exception as e:
-        return None, None, f"❌ {str(e)}"
+    df = fetch_company_data(start_date, end_date)
+    preview = df.head(500)
+    status = f"✅ Loaded **{len(df):,} companies** (showing first 500 rows)"
+    return df, preview, status
 
 # =====================================================
-# EXPORT
+# EXCEL EXPORT
 # =====================================================
 def export_excel(df):
     path = "/tmp/leadfeeder_company_data.xlsx"
@@ -234,7 +176,6 @@ def build_kpis(df):
 # =====================================================
 def build_dashboard(df, preset, top_n):
     dim, metric, agg = DASHBOARD_PRESETS[preset]
-
     if agg == "count":
         grouped = df.groupby(dim, dropna=False).size().reset_index(name="value")
     else:
@@ -244,7 +185,7 @@ def build_dashboard(df, preset, top_n):
     return px.bar(grouped, x=dim, y="value", template="plotly_white", title=preset)
 
 # =====================================================
-# TREND ENGINE (CHART + TABLE)
+# TREND ENGINE
 # =====================================================
 def build_trend(df, preset, grain):
     metric, agg, segment = TREND_PRESETS[preset]
@@ -265,12 +206,10 @@ def build_trend(df, preset, grain):
 
     if segment:
         ts = df.groupby(["period", segment])[val].agg(agg).reset_index()
-        fig = px.bar(ts, x="period", y=val, color=segment, template="plotly_white")
-        return fig, ts
+        return px.line(ts, x="period", y=val, color=segment, template="plotly_white", title=preset)
 
     ts = df.groupby("period")[val].agg(agg).reset_index()
-    fig = px.bar(ts, x="period", y=val, template="plotly_white")
-    return fig, ts
+    return px.line(ts, x="period", y=val, markers=True, template="plotly_white", title=preset)
 
 # =====================================================
 # UI
@@ -285,17 +224,25 @@ with gr.Blocks(title="Leadfeeder Company-Level Analytics") as demo:
 
     with gr.Tabs():
 
+        # ================= DATA =================
         with gr.Tab("📋 Data"):
             start = gr.Textbox(value=(date.today() - timedelta(days=30)).isoformat())
             end = gr.Textbox(value=date.today().isoformat())
+
             load_btn = gr.Button("Load Data")
-            preview = gr.Dataframe(interactive=True)
+            preview_table = gr.Dataframe(interactive=True)
             download_btn = gr.Button("⬇️ Download Excel")
             download_file = gr.File()
 
-            load_btn.click(load_data_with_preview, [start, end], [df_state, preview, status])
+            load_btn.click(
+                load_data_with_preview,
+                [start, end],
+                [df_state, preview_table, status]
+            )
+
             download_btn.click(export_excel, df_state, download_file)
 
+        # ================= DASHBOARD =================
         with gr.Tab("📊 Dashboard"):
             with gr.Row():
                 k1 = gr.Number(label="Total Companies")
@@ -313,12 +260,12 @@ with gr.Blocks(title="Leadfeeder Company-Level Analytics") as demo:
 
             gr.Button("Build View").click(build_dashboard, [df_state, preset, top_n], chart)
 
+        # ================= TRENDS =================
         with gr.Tab("📈 Trends"):
             trend_view = gr.Dropdown(choices=list(TREND_PRESETS.keys()))
             grain = gr.Radio(["Daily", "Weekly", "Monthly"], value="Daily")
             plot = gr.Plot()
-            table = gr.Dataframe()
 
-            gr.Button("Show Trend").click(build_trend, [df_state, trend_view, grain], [plot, table])
+            gr.Button("Show Trend").click(build_trend, [df_state, trend_view, grain], plot)
 
 demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)