Delete app.py

app.py

@@ -1,474 +0,0 @@
-import os
-import time
-import requests
-import json
-import pandas as pd
-import gradio as gr
-import plotly.express as px
-from datetime import date, timedelta
-
-# =====================================================
-# CONFIG / SECRETS
-# =====================================================
-API_TOKEN = os.getenv("LEADFEEDER_API_TOKEN")
-APP_PASSWORD = os.getenv("APP_PASSWORD")
-
-if not API_TOKEN or not APP_PASSWORD:
-    print("⚠️ WARNING: Secrets missing. App will launch but API calls will fail.")
-    API_TOKEN = "PLACEHOLDER"
-    APP_PASSWORD = "password"
-
-ACCOUNT_ID = "255333" 
-BASE_URL = "https://api.leadfeeder.com"
-PAGE_SIZE = 100 
-HEADERS = {
-    "Authorization": f"Token token={API_TOKEN}",
-    "Accept": "application/json"
-}
-CAMPAIGN_CONFIG_FILE = "campaign_rules.json"
-
-# =====================================================
-# 1. CAMPAIGN MANAGER & LOGIC
-# =====================================================
-DEFAULT_CAMPAIGNS = {
-    "MS Tech": [
-        "microsoft", "power", "dynamics", "crm", "365", "xamarin", "sharepoint", 
-        "dot", ".net", "asp.net", "azure", "copilot", "real-estate-website-development", 
-        "manufacturing-production", "legacy-hrms", "healthcare-appointment", "eld-fleet",
-        "legacy-erp", "clinical-trial", "digital-e-learning", "bi-analytics", 
-        "application-modernization", "data-warehouse", "workforce", "contingent"
-    ],
-    "Fintech": [
-        "fintech", "finance", "money", "credit", "debit", "card", "invest", "bank", 
-        "wallet", "account", "bookkeeping", "payment", "loan", "nfc", "lend", "debt", 
-        "tax", "invoice", "quot", "borrow", "bill", "coin", "currency", "aml", "kyc", 
-        "blockchain", "cryptocurrency", "budget", "expense", "wealth", "sox", "sarbanes"
-    ],
-    "Adtech": [
-        "programmatic", "publisher", "media", "entertainment", "trading", "ott", 
-        "email", "news", "social", "marketing", "video", "audio", "radio", "livestream", 
-        "bid", "anime", "dooh", "vod", "rtb", "supply-side-platform", "demand-side-platform", 
-        "data-management-platform"
-    ]
-}
-
-def load_campaign_rules():
-    if os.path.exists(CAMPAIGN_CONFIG_FILE):
-        try:
-            with open(CAMPAIGN_CONFIG_FILE, "r") as f:
-                return json.load(f)
-        except:
-            return DEFAULT_CAMPAIGNS
-    return DEFAULT_CAMPAIGNS
-
-def categorize_quality(score):
-    if pd.isna(score): return "Unknown"
-    try:
-        s = int(score)
-        if 8 <= s <= 10: return "High Quality (8-10)"
-        if 5 <= s <= 7:  return "Mid Quality (5-7)"
-        if 1 <= s <= 4:  return "Low Quality (1-4)"
-        return "Low Quality (0)"
-    except:
-        return "Unknown"
-
-def get_campaign_match(text, rules):
-    if not text or not isinstance(text, str):
-        return None
-    text_lower = text.lower()
-    for campaign_name, keywords in rules.items():
-        for kw in keywords:
-            if kw.lower() in text_lower:
-                return campaign_name
-    return None
-
-def apply_business_logic(df):
-    if df is None or df.empty: return df
-    
-    if "lead_quality_score" in df.columns:
-        df["Quality_Group"] = df["lead_quality_score"].apply(categorize_quality)
-    
-    rules = load_campaign_rules()
-    
-    def resolve_campaign(row):
-        path = row.get("landing_page_path")
-        # Strictly check landing page to avoid generic categorizations
-        if path and path not in ["", "/", "/home"]:
-            match = get_campaign_match(path, rules)
-            if match: 
-                return match
-        return "Uncategorized"
-
-    df["Campaign"] = df.apply(resolve_campaign, axis=1)
-    return df
-
-# =====================================================
-# 2. PRESETS (Original structure preserved)
-# =====================================================
-DASHBOARD_PRESETS = {
-    # --- Added New Presets ---
-    "Visits by Campaign": ("Campaign", "total_visits", "sum"),
-    "Leads by Campaign": ("Campaign", "company_name", "count"),
-    "Leads by Quality Group": ("Quality_Group", "company_name", "count"),
-    "Visits by City": ("city", "total_visits", "sum"),
-    "Leads by City": ("city", "company_name", "count"),
-    "Leads by Country": ("country", "company_name", "count"),
-    "Leads by Industry": ("primary_industry", "company_name", "count"),
-    
-    # --- Original 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"),
-    "Accounts by Assignee": ("assignee", "company_name", "count"),
-    "Visits by Assignee": ("assignee", "total_visits", "sum"),
-}
-
-TREND_PRESETS = {
-    # --- Added New Presets ---
-    "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"),
-    "Visits Trend by City": ("total_visits", "sum", "city"),
-    "Leads Trend by City": ("company_name", "count", "city"),
-    "Leads Trend by Country": ("company_name", "count", "country"),
-    "Leads Trend by Industry": ("company_name", "count", "primary_industry"),
-    
-    # --- Original 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 (Original structure preserved)
-# =====================================================
-def make_request(url, params=None):
-    retries = 3
-    while retries > 0:
-        r = requests.get(url, headers=HEADERS, params=params, timeout=45)
-        if r.status_code == 429:
-            time.sleep(61)
-            retries -= 1
-            continue
-        r.raise_for_status()
-        return r.json()
-    raise Exception("Max retries exceeded")
-
-def fetch_basic_leads(start_date, end_date):
-    page = 1
-    rows = []
-    print(f"🚀 Fetching full company list for {start_date} to {end_date}...")
-    while True:
-        try:
-            js = make_request(
-                f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads",
-                params={"start_date": start_date, "end_date": end_date, "page[number]": page, "page[size]": PAGE_SIZE, "include": "location"}
-            )
-            data = js.get("data", [])
-            if not data: break
-            
-            included = js.get("included", [])
-            loc_map = {str(i["id"]): i["attributes"] for i in included if i["type"] == "locations"}
-            
-            for lead in data:
-                a = lead["attributes"]
-                loc_id = lead.get("relationships", {}).get("location", {}).get("data", {}).get("id")
-                loc = loc_map.get(str(loc_id), {})
-                
-                rows.append({
-                    "lead_id": lead.get("id"),
-                    "company_name": a.get("name"),
-                    "website_url": a.get("website_url"),
-                    "phone": a.get("phone"),
-                    "business_id": a.get("business_id"),
-                    "primary_industry": a.get("industry"),
-                    "all_industries": ", ".join([i.get("name") for i in a.get("industries", [])]) if a.get("industries") else None,
-                    "first_visit_date": a.get("first_visit_date"),
-                    "last_visit_date": a.get("last_visit_date"),
-                    "total_visits": a.get("visits"),
-                    "lead_quality_score": a.get("quality"),
-                    "revenue": a.get("revenue"),
-                    "employee_count": a.get("employee_count"),
-                    "employees_min": a.get("employees_range", {}).get("min") if a.get("employees_range") else None,
-                    "employees_max": a.get("employees_range", {}).get("max") if a.get("employees_range") else None,
-                    "assignee": a.get("assignee"),
-                    "emailed_to": a.get("emailed_to"),
-                    "crm_lead_id": a.get("crm_lead_id"),
-                    "crm_organization_id": a.get("crm_organization_id"),
-                    "tags": ", ".join(a.get("tags", [])) if a.get("tags") else None,
-                    "linkedin_url": a.get("linkedin_url"),
-                    "twitter_handle": a.get("twitter_handle"),
-                    "facebook_url": a.get("facebook_url"),
-                    "country": loc.get("country"),
-                    "region": loc.get("region"),
-                    "city": loc.get("city"),
-                    "leadfeeder_url": a.get("view_in_leadfeeder"),
-                    "landing_page_path": None,
-                    "exit_page_path": None
-                })
-            print(f"✅ Page {page} loaded. Rows: {len(rows)}")
-            page += 1
-        except Exception as e:
-            print(f"Error on page {page}: {e}")
-            break
-            
-    df = pd.DataFrame(rows)
-    if not df.empty:
-        df["last_visit_date"] = pd.to_datetime(df["last_visit_date"], errors="coerce")
-    return df
-
-def enrich_leads_with_visits(df, start_date, end_date, max_rows=None, progress=gr.Progress()):
-    if df.empty: return df
-    target_df = df.head(max_rows) if max_rows else df
-    total = len(target_df)
-    
-    print(f"🕵️ Deep enriching {total} rows ({start_date} to {end_date})...")
-    for index, row in target_df.iterrows():
-        if row.get("landing_page_path"): continue
-        lead_id = row["lead_id"]
-        try:
-            visit_data = make_request(
-                f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads/{lead_id}/visits",
-                params={"start_date": start_date, "end_date": end_date, "page[size]": 1, "include": "page_views"}
-            )
-            visits = visit_data.get("data", [])
-            included = visit_data.get("included", [])
-            landing = None
-            exit_p = None
-            
-            if visits:
-                visit = visits[0]
-                v_attrs = visit.get("attributes", {})
-                landing = v_attrs.get("landing_page_path") or v_attrs.get("landing_page_url")
-                
-                visit_route = v_attrs.get("visit_route", [])
-                if visit_route and isinstance(visit_route, list):
-                    last_step = visit_route[-1]
-                    if not exit_p: exit_p = last_step.get("page_path") or last_step.get("page_url")
-                    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", [])]
-                    if pv_ids:
-                        if not landing:
-                            first_pv = pv_map.get(pv_ids[0])
-                            if first_pv: landing = first_pv.get("url") or first_pv.get("path")
-                        if not exit_p:
-                            last_pv = pv_map.get(pv_ids[-1])
-                            if last_pv: exit_p = last_pv.get("url") or last_pv.get("path")
-                
-                df.at[index, "landing_page_path"] = landing
-                df.at[index, "exit_page_path"] = exit_p
-        except Exception as e:
-            print(f"Failed to enrich lead {lead_id}: {e}")
-        
-        if max_rows and index % 5 == 0:
-            progress(index / total, desc="Enriching...")
-    return df
-
-# =====================================================
-# 4. WRAPPERS
-# =====================================================
-def load_preview(start, end):
-    df = fetch_basic_leads(start, end)
-    if df.empty: return df, pd.DataFrame(), "⚠️ No data found."
-    df_preview = df.copy()
-    df_preview = enrich_leads_with_visits(df_preview, start, end, max_rows=50)
-    df_preview = apply_business_logic(df_preview)
-    return df, df_preview.head(50), f"✅ Loaded {len(df):,} companies. Preview top 50."
-
-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)
-    final_df = apply_business_logic(enriched_df)
-    path = "/tmp/leadfeeder_campaign_data.xlsx"
-    final_df.to_excel(path, index=False)
-    return path
-
-def inspect_raw_json(lead_id, start_date, end_date):
-    if not lead_id: return "Please enter a Lead ID"
-    try:
-        url = f"{BASE_URL}/accounts/{ACCOUNT_ID}/leads/{lead_id}/visits"
-        params = {"start_date": start_date, "end_date": end_date, "page[size]": 1, "include": "page_views"}
-        r = requests.get(url, headers=HEADERS, params=params)
-        return json.dumps(r.json(), indent=2)
-    except Exception as e:
-        return str(e)
-
-# =====================================================
-# 5. CHART ENGINES (Restored to exact original logic)
-# =====================================================
-def build_kpis(df):
-    if df is None or df.empty: return 0, 0, 0, 0, 0, 0
-    return (
-        len(df),
-        df["total_visits"].gt(0).sum(),
-        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),
-    )
-
-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)
-    
-    dim, metric, agg = DASHBOARD_PRESETS[preset]
-    if agg == "count":
-        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)
-
-def build_trend(df, preset, grain, filter_values):
-    if df is None or df.empty: return px.line(title="No Data Loaded")
-    if "Campaign" not in df.columns: df = apply_business_logic(df)
-    
-    metric, agg, segment = TREND_PRESETS[preset]
-    df = df.dropna(subset=["last_visit_date"]).copy()
-        
-    if segment and filter_values:
-        df = df[df[segment].isin(filter_values)]
-        if df.empty: return px.line(title="No data for selected filters")
-
-    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
-            
-    val = metric
-    if agg == "count":
-        df["_v"] = df[metric].notna().astype(int)
-        val = "_v"
-            
-    if segment:
-        ts = df.groupby(["period", segment])[val].agg(agg).reset_index()
-        title = f"{preset} (Filtered)" if filter_values else preset
-        return px.line(ts, x="period", y=val, color=segment, template="plotly_white", title=title)
-        
-    ts = df.groupby("period")[val].agg(agg).reset_index()
-    return px.line(ts, x="period", y=val, markers=True, template="plotly_white", title=preset)
-
-def get_trend_filter_options(df, preset):
-    if df is None or df.empty: return gr.update(choices=[], value=None, visible=False)
-    if "Campaign" not in df.columns: df = apply_business_logic(df)
-    
-    metric, agg, segment = TREND_PRESETS[preset]
-    if not segment: return gr.update(choices=[], value=None, visible=False)
-    
-    options = sorted(df[segment].astype(str).unique().tolist())
-    top_5 = df[segment].value_counts().head(5).index.tolist()
-    return gr.update(choices=options, value=top_5, visible=True, label=f"Filter {segment}")
-
-# =====================================================
-# 6. UI LAYOUT
-# =====================================================
-with gr.Blocks(title="Leadfeeder Campaign Pro") as demo:
-    gr.Markdown("## 🚀 Leadfeeder Analytics & Campaign Manager")
-    
-    with gr.Row():
-        pwd = gr.Textbox(type="password", label="App Password")
-        gr.Button("Auth").click(lambda p: gr.Info("Success") if p==APP_PASSWORD else gr.Error("Invalid"), pwd, None)
-        df_state = gr.State()
-        status = gr.Markdown()
-        
-    with gr.Tabs():
-        # --- TAB 1: DATA ---
-        with gr.Tab("📋 Data & Report"):
-            with gr.Row():
-                start = gr.Textbox(label="Start Date", value=(date.today()-timedelta(days=30)).isoformat())
-                end = gr.Textbox(label="End Date", value=date.today().isoformat())
-            with gr.Row():
-                btn_load = gr.Button("1. Load Data (Preview)", variant="primary")
-                btn_dl = gr.Button("2. Enrich & Download Full Excel")
-                file_dl = gr.File(label="Download Excel")
-                        
-            table = gr.Dataframe(label="Preview (Top 50 Enriched)", interactive=True)
-            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 %"]]
-            gr.Button("Refresh KPIs").click(build_kpis, df_state, kpis)
-            
-            with gr.Row():
-                preset = gr.Dropdown(choices=list(DASHBOARD_PRESETS.keys()), label="Chart View", value="Visits by Campaign")
-                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)
-
-        # --- TAB 3: TRENDS ---
-        with gr.Tab("📈 Trends"):
-            with gr.Row():
-                trend_view = gr.Dropdown(choices=list(TREND_PRESETS.keys()), label="Select Trend", value="Visits Trend by Campaign")
-                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)
-
-        # --- 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.")
-            
-            init_rules = load_campaign_rules()
-            camp_choices = list(init_rules.keys()) + ["+ Create New Campaign"]
-            
-            with gr.Row():
-                camp_dropdown = gr.Dropdown(choices=camp_choices, label="Select Campaign to Edit", value=camp_choices[0])
-                new_camp_name = gr.Textbox(label="New Campaign Name", visible=False)
-                
-            kw_input = gr.Textbox(label="Keywords (comma separated)", lines=5, value=", ".join(init_rules.get(camp_choices[0], [])))
-            
-            def update_ui_on_select(selected_camp):
-                rules = load_campaign_rules()
-                if selected_camp == "+ Create New Campaign":
-                    return gr.update(visible=True, value=""), gr.update(value="")
-                else:
-                    kws = rules.get(selected_camp, [])
-                    return gr.update(visible=False), gr.update(value=", ".join(kws))
-                    
-            camp_dropdown.change(update_ui_on_select, inputs=[camp_dropdown], outputs=[new_camp_name, kw_input])
-            
-            save_config_btn = gr.Button("💾 Save Configuration", variant="primary")
-            config_status = gr.Markdown()
-            
-            def save_easy_config(selected_camp, new_name, kw_string):
-                rules = load_campaign_rules()
-                raw_kws = kw_string.split(",")
-                clean_kws = [k.strip().lower() for k in raw_kws if k.strip()]
-                target_camp = new_name.strip() if selected_camp == "+ Create New Campaign" else selected_camp
-                if not target_camp:
-                    return "❌ Error: Campaign name cannot be empty.", gr.update()
-                
-                rules[target_camp] = clean_kws
-                with open(CAMPAIGN_CONFIG_FILE, "w") as f:
-                    json.dump(rules, f, indent=4)
-                    
-                updated_choices = list(rules.keys()) + ["+ Create New Campaign"]
-                return f"✅ Saved successfully! Updated keywords for '{target_camp}'.", 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 ---
-        with gr.Tab("🛠️ Debugger"):
-            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)