feat: Implement mobile application and lead processing utilities.

leads/identify_missing_leads.py

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+import pandas as pd
+import json
+import os
+
+def normalize(text):
+    if not isinstance(text, str):
+        return ""
+    return text.strip().lower()
+
+def main():
+    # 1. Load the "Done" list
+    leads_csv_path = 'leads/leads.csv'
+    if os.path.exists(leads_csv_path):
+        leads_df = pd.read_csv(leads_csv_path)
+        done_names = set(leads_df['Firm/Innung'].apply(normalize))
+        done_emails = set(leads_df['Email'].apply(normalize))
+    else:
+        done_names = set()
+        done_emails = set()
+
+    missing_duesseldorf = []
+    missing_cologne = []
+    missing_unterfranken = []
+
+    # 2. Check Düsseldorf Targets
+    duesseldorf_path = 'leads/cologne_duesseldorf_data/duesseldorf_targets.json'
+    if os.path.exists(duesseldorf_path):
+        with open(duesseldorf_path, 'r', encoding='utf-8') as f:
+            targets = json.load(f)
+            seen_d = set()
+            for t in targets:
+                name = t.get('innung', '')
+                if normalize(name) not in done_names and normalize(name) not in seen_d:
+                    missing_duesseldorf.append(t)
+                    seen_d.add(normalize(name))
+
+    # 3. Check Cologne/Düsseldorf Raw CSV
+    cologne_raw_path = 'leads/raw/innungen_leads_koeln_duesseldorf.csv'
+    if os.path.exists(cologne_raw_path):
+        cologne_df = pd.read_csv(cologne_raw_path)
+        seen_c = set()
+        for _, row in cologne_df.iterrows():
+            name = row.get('organisation', '')
+            region = row.get('region', '')
+            email = row.get('email', '')
+            
+            if str(region).lower() == 'koeln':
+                if normalize(name) in seen_c:
+                    continue
+                seen_c.add(normalize(name))
+
+                if pd.isna(email) or email.strip() == '':
+                     missing_cologne.append({'name': name, 'reason': 'No Email'})
+                else:
+                    if normalize(email) not in done_emails and normalize(name) not in done_names:
+                        missing_cologne.append({'name': name, 'email': email, 'reason': 'Not in final list'})
+
+    # 4. Check Unterfranken Raw CSV
+    unterfranken_raw_path = 'leads/raw/leads_unterfranken.csv'
+    if os.path.exists(unterfranken_raw_path):
+        uf_df = pd.read_csv(unterfranken_raw_path)
+        name_col = None
+        for col in uf_df.columns:
+            if 'innung' in col.lower() or 'firm' in col.lower() or 'name' in col.lower():
+                name_col = col
+                break
+        
+        seen_u = set()
+        if name_col:
+            for _, row in uf_df.iterrows():
+                name = str(row[name_col]).strip()
+                # Filter garbage
+                if len(name) < 5: continue
+                if "regierungsbezirk" in name.lower() and "sitz" in name.lower(): continue # Garbage header line
+                
+                if normalize(name) not in done_names and normalize(name) not in seen_u:
+                    missing_unterfranken.append(name)
+                    seen_u.add(normalize(name))
+
+    # 5. Generate Markdown
+    with open('missing_leads.md', 'w', encoding='utf-8') as f:
+        f.write('# Missing Leads Report\n\n')
+        
+        f.write(f'## Düsseldorf (Missing: {len(missing_duesseldorf)})\n')
+        if not missing_duesseldorf:
+             f.write("No missing leads identified (or source file empty).\n")
+        for item in missing_duesseldorf:
+            f.write(f"- {item.get('innung')} (Contact: {item.get('person', 'N/A')})\n")
+        
+        f.write(f'\n## Cologne (Missing: {len(missing_cologne)})\n')
+        if not missing_cologne:
+             f.write("No missing leads identified from raw source.\n")
+        for item in missing_cologne:
+            reason = item.get('reason', '')
+            email_part = f" (Email: {item['email']})" if 'email' in item else ""
+            f.write(f"- {item.get('name')}{email_part} [{reason}]\n")
+
+        f.write(f'\n## Unterfranken (Missing: {len(missing_unterfranken)})\n')
+        if not missing_unterfranken:
+             f.write("All raw Unterfranken leads seem to be in the final list.\n")
+        for name in missing_unterfranken:
+            f.write(f"- {name}\n")
+
+if __name__ == "__main__":
+    main()