update

.gitignore

@@ -0,0 +1,20 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+.venv/
+venv/
+
+# Environment
+.env
+.secrets
+
+# AI/ML
+checkpoint-*/
+logs/
+results/
+wandb/
+
+# OS
+.DS_Store
+Thumbs.db

app.py

@@ -1,79 +1,58 @@
-import gradio as gr
-import torch
 import os
+import gradio as gr
 from transformers import pipeline
+from huggingface_hub import InferenceClient
 
-# Konfigurasi Kategori sesuai Proposal Archon
+# Konfigurasi Pilar 1: Classifier (IndoBERT)
+MODEL_PATH = "archon_v1"
 CATEGORIES = ["Income", "Bills", "Transport", "Retail/E-commerce", "Cash Withdrawal", "Transfer Out", "General Debit"]
 
-class ArchonEngine:
-    def __init__(self, model_path="archon_v1"):
-        # Pilar 1: NLP Transaction Classifier [cite: 83]
-        self.classifier = pipeline(
-            "text-classification", 
-            model=model_path, 
-            tokenizer=model_path
-        )
+# Konfigurasi Pilar 3: Generative NBO (Mistral-7B)
+HF_TOKEN = os.getenv("HF_TOKEN")
+llm_client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.3", token=HF_TOKEN)
 
-    def process(self, text, amount, income, monthly_spending):
-        # 1. Klasifikasi Transaksi
-        pred = self.classifier(text)[0]
-        label_id = int(pred['label'].split('_')[-1])
-        category = CATEGORIES[label_id]
-        conf = pred['score']
+class ArchonSystem:
+    def __init__(self):
+        # Pilar 1: Load Local Classifier 
+        self.classifier = pipeline("text-classification", model=MODEL_PATH, tokenizer=MODEL_PATH)
 
-        # 2. Pilar 2: Machine Learning Predictive Model (Risk) [cite: 85]
-        risk_score = 0.05
-        ratio = amount / income if income > 0 else 0
-        if ratio >= 0.25: risk_score += 0.45
+    def analyze(self, text, amount, income, monthly_spending):
+        # 1. NLP Classification (Pilar 1)
+        pred = self.classifier(text)[0]
+        cat = CATEGORIES[int(pred['label'].split('_')[-1])]
         
-        spend_rate = monthly_spending / income if income > 0 else 0
-        if spend_rate >= 0.85: risk_score += 0.35
+        # 2. Risk Prediction (Pilar 2: Early Warning System)
+        # Menggunakan logika rasio pengeluaran adaptif
+        risk_score = (amount / income if income > 0 else 0) + (monthly_spending / income if income > 0 else 0)
+        risk_level = "High" if risk_score > 0.8 else ("Medium" if risk_score > 0.4 else "Low")
         
-        if category in ["Cash Withdrawal", "Transfer Out"]: risk_score += 0.10
+        # 3. Generative NBO Engine (Pilar 3: Personal Recommendation) [cite: 87, 136]
+        prompt = f"Role: Financial Advisor Bank Profesional. Nasabah bertransaksi {cat} sebesar Rp{amount:,.0f}. Risiko: {risk_level}. Beri 1 saran finansial singkat, natural, dan ramah dalam Bahasa Indonesia (Maks 20 kata)."
         
-        risk_level = "High" if risk_score >= 0.6 else ("Medium" if risk_score >= 0.3 else "Low")
-
-        # 3. Pilar 3: Next Best Offer (NBO) Engine [cite: 87]
-        if risk_level == "High":
-            recommendation = "Set immediate budget alert + suggest emergency saving plan; show debt counseling resources."
-        elif category == "Income":
-            recommendation = "Recommend automatic split: 10% to Emergency Fund, 5% to Investments."
-        elif category in ["Retail/E-commerce", "General Debit"]:
-            recommendation = "Offer discount coupons / loyalty suggestion or roundup saving feature."
-        else:
-            recommendation = "Maintain current budget; propose small Auto-Save (Rp20k/day)."
+        try:
+            nbo_msg = llm_client.chat_completion(messages=[{"role": "user", "content": prompt}], max_tokens=80).choices[0].message.content
+        except:
+            nbo_msg = "Pertahankan kebiasaan menabung Anda dan pantau pengeluaran melalui dashboard Archon."
 
         return {
-            "Kategori (Pilar 1)": f"{category} ({conf*100:.2f}%)",
-            "Level Risiko (Pilar 2)": f"{risk_level} (Score: {risk_score:.2f})",
-            "Rekomendasi NBO (Pilar 3)": recommendation
+            "Analysis": f"Category: {cat} | Resilience Status: {risk_level}",
+            "Archon's Personalized Advice": nbo_msg
         }
 
-# Inisialisasi Engine
-# Pastikan folder 'archon_v1' ada di direktori yang sama
-engine = ArchonEngine("archon_v1")
-
-# UI Interface Gradio
-with gr.Blocks(title="Archon-AI: Financial Resilience Engine") as demo:
-    gr.Markdown("# 🛡️ Archon-AI")
-    gr.Markdown("### Financial Resilience Engine berbasis AI untuk Perbankan Indonesia [cite: 8]")
-    
-    with gr.Row():
-        with gr.Column():
-            input_text = gr.Textbox(label="Narasi Transaksi", placeholder="Contoh: GAJI PT MAJU JAYA")
-            input_amount = gr.Number(label="Jumlah Transaksi (Rp)")
-            input_income = gr.Number(label="Total Pendapatan Bulanan (Rp)")
-            input_spending = gr.Number(label="Total Pengeluaran Bulan Ini (Rp)")
-            btn = gr.Button("Analisis dengan Archon", variant="primary")
-        
-        with gr.Column():
-            output = gr.JSON(label="Hasil Analisis AI")
-
-    btn.click(
-        fn=engine.process, 
-        inputs=[input_text, input_amount, input_income, input_spending], 
-        outputs=output
-    )
-
-demo.launch()
+# UI Skala Industri
+archon = ArchonSystem()
+demo = gr.Interface(
+    fn=archon.analyze,
+    inputs=[
+        gr.Textbox(label="Transaction Narrative"), 
+        gr.Number(label="Amount (Rp)"), 
+        gr.Number(label="Monthly Income (Rp)"), 
+        gr.Number(label="Total Current Spending (Rp)")
+    ],
+    outputs="json",
+    title="Archon-AI: Financial Resilience Engine",
+    description="Managed Services Provider (MSP) solution for Indonesian Banking."
+)
+
+if __name__ == "__main__":
+    demo.launch()

archon_v1/archon_v1_folder_backup/config.json

@@ -0,0 +1,51 @@
+{
+  "_num_labels": 5,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

archon_v1/archon_v1_folder_backup/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8c0685f1b560a84bcddfd6c9723a3f1d4b2ba1ea5e616a9159b6e8a8a82698e5
+size 497810452

archon_v1/archon_v1_folder_backup/runs/Jan16_09-24-07_f599b1c76637/events.out.tfevents.1768555450.f599b1c76637.430.0

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ce27747e29d2b852b01069e98f4e11fc04aa61fcdcccb300d99c2133cb2a93bb
+size 6886

archon_v1/archon_v1_folder_backup/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

archon_v1/archon_v1_folder_backup/tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

requirements.txt

@@ -1,4 +1,7 @@
 transformers
 torch
+huggingface_hub
+gradio
 pandas
-numpy
+numpy
+accelerate