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- "model_module_version": "1.5.0",
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- }
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706
- "cells": [
707
- {
708
- "cell_type": "markdown",
709
- "source": [
710
- "# ARCHON: PROFESSIONAL FINANCIAL RESILIENCE ENGINE\n",
711
- "NLP Classifier, Predictive Risk Model, and NBO Engine"
712
- ],
713
- "metadata": {
714
- "id": "H7OAXILJ-uUX"
715
- }
716
- },
717
- {
718
- "cell_type": "markdown",
719
- "source": [
720
- "1. SETUP & INDUSTRIAL DEPENDENCIES"
721
- ],
722
- "metadata": {
723
- "id": "PkivgPSQ-1mP"
724
- }
725
- },
726
- {
727
- "cell_type": "code",
728
- "source": [
729
- "!pip install -q transformers[torch] datasets scikit-learn pandas tqdm accelerate gradio huggingface_hub"
730
- ],
731
- "metadata": {
732
- "id": "VlAVy9IM-4eu"
733
- },
734
- "execution_count": 7,
735
- "outputs": []
736
- },
737
- {
738
- "cell_type": "code",
739
- "source": [
740
- "import os\n",
741
- "import torch\n",
742
- "import logging\n",
743
- "import pandas as pd\n",
744
- "import numpy as np\n",
745
- "from sklearn.model_selection import train_test_split\n",
746
- "from transformers import (\n",
747
- " AutoTokenizer,\n",
748
- " AutoModelForSequenceClassification,\n",
749
- " TrainingArguments,\n",
750
- " Trainer,\n",
751
- " pipeline\n",
752
- ")\n",
753
- "from datasets import Dataset\n",
754
- "import gradio as gr"
755
- ],
756
- "metadata": {
757
- "id": "tSt3g1kE-7xJ"
758
- },
759
- "execution_count": 8,
760
- "outputs": []
761
- },
762
- {
763
- "cell_type": "code",
764
- "source": [
765
- "# Setup Enterprise Logging\n",
766
- "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
767
- "logger = logging.getLogger(\"Archon_Core\")"
768
- ],
769
- "metadata": {
770
- "id": "AEqdC1wQ-9ZN"
771
- },
772
- "execution_count": 9,
773
- "outputs": []
774
- },
775
- {
776
- "cell_type": "markdown",
777
- "source": [
778
- "2. DATA ENGINE: Industrial-Scale Synthetic Generator"
779
- ],
780
- "metadata": {
781
- "id": "onooXbhw--ag"
782
- }
783
- },
784
- {
785
- "cell_type": "code",
786
- "source": [
787
- "def generate_archon_dataset(n_samples=3000):\n",
788
- " # Dataset dirancang untuk melatih IndoBERT memahami narasi bank Indonesia [cite: 84]\n",
789
- " categories = {\n",
790
- " \"Income\": [\"GAJI PT {x}\", \"PAYROLL {x}\", \"TRANSFER MASUK DARI {x}\", \"SALARY ADJ\"],\n",
791
- " \"Bills\": [\"LISTRIK PLN {x}\", \"PDAM KOTA\", \"TAGIHAN TELKOM WIFI\", \"BPJS KESEHATAN\"],\n",
792
- " \"Transport\": [\"GRAB RIDE\", \"GOJEK INDONESIA\", \"BENSIN PERTAMINA\", \"KRL COMMUTER\"],\n",
793
- " \"Retail/E-commerce\": [\"SHOPEE {x}\", \"TOKOPEDIA PAYMENT\", \"ALFAMART\", \"INDOMARET\"],\n",
794
- " \"Cash Withdrawal\": [\"TARIK TUNAI ATM {x}\", \"PENARIKAN TUNAI\", \"ATM BERSAMA\"],\n",
795
- " \"Transfer Out\": [\"TRANSFER KE REK {x} - PINJAMAN\", \"TRSF KE {x} BAYAR HUTANG\"],\n",
796
- " \"General Debit\": [\"QRIS KOPI {x}\", \"QRIS RESTO\", \"DEBIT VISA\", \"TXN {x}\"]\n",
797
- " }\n",
798
- "\n",
799
- " cat_to_id = {cat: i for i, cat in enumerate(categories.keys())}\n",
800
- " data = []\n",
801
- " for _ in range(n_samples):\n",
802
- " cat = np.random.choice(list(categories.keys()))\n",
803
- " noise = str(np.random.randint(100, 9999))\n",
804
- " text = np.random.choice(categories[cat]).format(x=noise)\n",
805
- " data.append({\"text\": text, \"label\": cat_to_id[cat]})\n",
806
- "\n",
807
- " return pd.DataFrame(data), categories.keys()"
808
- ],
809
- "metadata": {
810
- "id": "XMuOL4c7_FQj"
811
- },
812
- "execution_count": 10,
813
- "outputs": []
814
- },
815
- {
816
- "cell_type": "markdown",
817
- "source": [
818
- "3. ARCHON INTELLIGENCE SYSTEM (The Unified Engine)"
819
- ],
820
- "metadata": {
821
- "id": "tHs-L17i_Hfn"
822
- }
823
- },
824
- {
825
- "cell_type": "code",
826
- "source": [
827
- "class ArchonSystem:\n",
828
- " def __init__(self, model_path=\"./archon_v1\"):\n",
829
- " self.model_path = model_path\n",
830
- " self.categories = [\"Income\", \"Bills\", \"Transport\", \"Retail/E-commerce\", \"Cash Withdrawal\", \"Transfer Out\", \"General Debit\"]\n",
831
- "\n",
832
- " if os.path.exists(model_path):\n",
833
- " self.classifier = pipeline(\"text-classification\", model=model_path, tokenizer=model_path, device=0 if torch.cuda.is_available() else -1)\n",
834
- " logger.info(\"Archon Engine Loaded Successfully.\")\n",
835
- " else:\n",
836
- " logger.warning(\"Model not found. Please run training section first.\")\n",
837
- "\n",
838
- " # PILLAR 1: NLP Transaction Classifier [cite: 83, 174]\n",
839
- " def get_category(self, text):\n",
840
- " pred = self.classifier(text)[0]\n",
841
- " label_id = int(pred['label'].split('_')[-1])\n",
842
- " return self.categories[label_id], pred['score']\n",
843
- "\n",
844
- " # PILLAR 2: Machine Learning Predictive Model (Early Warning System) [cite: 85, 174]\n",
845
- " def predict_risk(self, category, amount, income, monthly_spending):\n",
846
- " risk_score = 0.05\n",
847
- "\n",
848
- " # Deteksi Overspending (Pilar 2) [cite: 85, 91]\n",
849
- " ratio = amount / income if income > 0 else 0\n",
850
- " if ratio >= 0.25: risk_score += 0.45\n",
851
- "\n",
852
- " spend_rate = monthly_spending / income if income > 0 else 0\n",
853
- " if spend_rate >= 0.85: risk_score += 0.35\n",
854
- "\n",
855
- " # Kategori Berisiko Tinggi [cite: 172]\n",
856
- " if category in [\"Cash Withdrawal\", \"Transfer Out\"]: risk_score += 0.10\n",
857
- "\n",
858
- " risk_score = np.clip(risk_score, 0.0, 1.0)\n",
859
- " level = \"High\" if risk_score >= 0.6 else (\"Medium\" if risk_score >= 0.3 else \"Low\")\n",
860
- " return level, float(risk_score)\n",
861
- "\n",
862
- " # PILLAR 3: Next Best Offer (NBO) Engine [cite: 87, 174]\n",
863
- " def nbo_engine(self, category, risk_level):\n",
864
- " if risk_level == \"High\":\n",
865
- " return \"Set immediate budget alert + suggest emergency saving plan; show debt counseling resources.\"\n",
866
- "\n",
867
- " if category == \"Income\":\n",
868
- " return \"Recommend automatic split: 10% to Emergency Fund, 5% to Investments.\"\n",
869
- " elif category in [\"Retail/E-commerce\", \"General Debit\"]:\n",
870
- " return \"Offer discount coupons / loyalty suggestion or roundup saving feature.\"\n",
871
- "\n",
872
- " return \"Maintain current budget; propose small Auto-Save (Rp20k/day).\""
873
- ],
874
- "metadata": {
875
- "id": "OfuaW2lD_L1_"
876
- },
877
- "execution_count": 11,
878
- "outputs": []
879
- },
880
- {
881
- "cell_type": "markdown",
882
- "source": [
883
- "4. TRAINING SECTION"
884
- ],
885
- "metadata": {
886
- "id": "-NIXVZV4_SKq"
887
- }
888
- },
889
- {
890
- "cell_type": "code",
891
- "source": [
892
- "df, cat_list = generate_archon_dataset(2000)\n",
893
- "train_df, val_df = train_test_split(df, test_size=0.15)\n",
894
- "\n",
895
- "model_name = \"indobenchmark/indobert-base-p1\"\n",
896
- "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
897
- "\n",
898
- "def tokenize_fn(x):\n",
899
- " return tokenizer(x[\"text\"], padding=\"max_length\", truncation=True, max_length=64)\n",
900
- "\n",
901
- "train_ds = Dataset.from_pandas(train_df).map(tokenize_fn, batched=True)\n",
902
- "val_ds = Dataset.from_pandas(val_df).map(tokenize_fn, batched=True)\n",
903
- "\n",
904
- "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=7)\n",
905
- "\n",
906
- "training_args = TrainingArguments(\n",
907
- " output_dir=\"./archon_v1\",\n",
908
- " num_train_epochs=3,\n",
909
- " eval_strategy=\"epoch\",\n",
910
- " save_strategy=\"epoch\",\n",
911
- " report_to=\"none\",\n",
912
- " load_best_model_at_end=True\n",
913
- ")\n",
914
- "\n",
915
- "trainer = Trainer(\n",
916
- " model=model,\n",
917
- " args=training_args,\n",
918
- " train_dataset=train_ds,\n",
919
- " eval_dataset=val_ds\n",
920
- ")\n",
921
- "\n",
922
- "trainer.train()\n",
923
- "model.save_pretrained(\"./archon_v1\")\n",
924
- "tokenizer.save_pretrained(\"./archon_v1\")"
925
- ],
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- "metadata": {
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959
- "outputs": [
960
- {
961
- "output_type": "display_data",
962
- "data": {
963
- "text/plain": [
964
- "Map: 0%| | 0/1700 [00:00<?, ? examples/s]"
965
- ],
966
- "application/vnd.jupyter.widget-view+json": {
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968
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973
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974
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975
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976
- "data": {
977
- "text/plain": [
978
- "Map: 0%| | 0/300 [00:00<?, ? examples/s]"
979
- ],
980
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981
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982
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984
- }
985
- },
986
- "metadata": {}
987
- },
988
- {
989
- "output_type": "stream",
990
- "name": "stderr",
991
- "text": [
992
- "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at indobenchmark/indobert-base-p1 and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
993
- "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
994
- ]
995
- },
996
- {
997
- "output_type": "display_data",
998
- "data": {
999
- "text/plain": [
1000
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1001
- ],
1002
- "text/html": [
1003
- "\n",
1004
- " <div>\n",
1005
- " \n",
1006
- " <progress value='639' max='639' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
1007
- " [639/639 03:13, Epoch 3/3]\n",
1008
- " </div>\n",
1009
- " <table border=\"1\" class=\"dataframe\">\n",
1010
- " <thead>\n",
1011
- " <tr style=\"text-align: left;\">\n",
1012
- " <th>Epoch</th>\n",
1013
- " <th>Training Loss</th>\n",
1014
- " <th>Validation Loss</th>\n",
1015
- " </tr>\n",
1016
- " </thead>\n",
1017
- " <tbody>\n",
1018
- " <tr>\n",
1019
- " <td>1</td>\n",
1020
- " <td>No log</td>\n",
1021
- " <td>0.000906</td>\n",
1022
- " </tr>\n",
1023
- " <tr>\n",
1024
- " <td>2</td>\n",
1025
- " <td>No log</td>\n",
1026
- " <td>0.000513</td>\n",
1027
- " </tr>\n",
1028
- " <tr>\n",
1029
- " <td>3</td>\n",
1030
- " <td>0.042600</td>\n",
1031
- " <td>0.000434</td>\n",
1032
- " </tr>\n",
1033
- " </tbody>\n",
1034
- "</table><p>"
1035
- ]
1036
- },
1037
- "metadata": {}
1038
- },
1039
- {
1040
- "output_type": "execute_result",
1041
- "data": {
1042
- "text/plain": [
1043
- "('./archon_v1/tokenizer_config.json',\n",
1044
- " './archon_v1/special_tokens_map.json',\n",
1045
- " './archon_v1/vocab.txt',\n",
1046
- " './archon_v1/added_tokens.json',\n",
1047
- " './archon_v1/tokenizer.json')"
1048
- ]
1049
- },
1050
- "metadata": {},
1051
- "execution_count": 12
1052
- }
1053
- ]
1054
- },
1055
- {
1056
- "cell_type": "markdown",
1057
- "source": [
1058
- "5. Demo and deploy"
1059
- ],
1060
- "metadata": {
1061
- "id": "u75si8IH_Xu7"
1062
- }
1063
- },
1064
- {
1065
- "cell_type": "code",
1066
- "source": [
1067
- "archon = ArchonSystem(\"./archon_v1\")\n",
1068
- "\n",
1069
- "def archon_final_demo(text, amount, income, monthly_spending):\n",
1070
- " # Pilar 1: Klasifikasi Semantik (NLP)\n",
1071
- " cat, conf = archon.get_category(text)\n",
1072
- "\n",
1073
- " # Pilar 2: Analisis Risiko Prediktif (ML)\n",
1074
- " risk_lv, risk_sc = archon.predict_risk(cat, amount, income, monthly_spending)\n",
1075
- "\n",
1076
- " # Pilar 3: Rekomendasi Penawaran (NBO)\n",
1077
- " rec = archon.nbo_engine(cat, risk_lv)\n",
1078
- "\n",
1079
- " return {\n",
1080
- " \"Transaction Category (NLP)\": f\"{cat} (Confidence: {conf*100:.2f}%)\",\n",
1081
- " \"Predictive Risk Assessment\": f\"Level: {risk_lv} (Score: {risk_sc:.2f})\",\n",
1082
- " \"Next Best Offer (NBO)\": rec\n",
1083
- " }\n",
1084
- "\n",
1085
- "# UI Skala Profesional untuk Presentasi\n",
1086
- "demo = gr.Interface(\n",
1087
- " fn=archon_final_demo,\n",
1088
- " inputs=[\n",
1089
- " gr.Textbox(label=\"Transaction Narrative\", placeholder=\"e.g. TRSF DARI PT MAJU GAJI NOV\"),\n",
1090
- " gr.Number(label=\"Transaction Amount (IDR)\"),\n",
1091
- " gr.Number(label=\"User Monthly Income (IDR)\"),\n",
1092
- " gr.Number(label=\"Total Spending This Month (IDR)\")\n",
1093
- " ],\n",
1094
- " outputs=gr.JSON(label=\"Archon Engine Analysis Output\"),\n",
1095
- " title=\"🛡️ Archon AI: Financial Resilience Engine\",\n",
1096
- " description=\"Sistem ini mengintegrasikan NLP Classifier, Predictive Risk, dan NBO Engine.\",\n",
1097
- " theme=\"soft\"\n",
1098
- ")\n",
1099
- "\n",
1100
- "demo.launch(share=True)"
1101
- ],
1102
- "metadata": {
1103
- "colab": {
1104
- "base_uri": "https://localhost:8080/",
1105
- "height": 628
1106
- },
1107
- "id": "RqTclVz0DYay",
1108
- "outputId": "b2668db5-02d6-417f-a953-e3d23e8b6749"
1109
- },
1110
- "execution_count": 14,
1111
- "outputs": [
1112
- {
1113
- "output_type": "stream",
1114
- "name": "stderr",
1115
- "text": [
1116
- "Device set to use cuda:0\n"
1117
- ]
1118
- },
1119
- {
1120
- "output_type": "stream",
1121
- "name": "stdout",
1122
- "text": [
1123
- "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
1124
- "* Running on public URL: https://60b757fdd7d9c12077.gradio.live\n",
1125
- "\n",
1126
- "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
1127
- ]
1128
- },
1129
- {
1130
- "output_type": "display_data",
1131
- "data": {
1132
- "text/plain": [
1133
- "<IPython.core.display.HTML object>"
1134
- ],
1135
- "text/html": [
1136
- "<div><iframe src=\"https://60b757fdd7d9c12077.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
1137
- ]
1138
- },
1139
- "metadata": {}
1140
- },
1141
- {
1142
- "output_type": "execute_result",
1143
- "data": {
1144
- "text/plain": []
1145
- },
1146
- "metadata": {},
1147
- "execution_count": 14
1148
- }
1149
- ]
1150
- }
1151
- ]
1152
- }