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1 Parent(s): 7b960f1

add app and requirements

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Files changed (3) hide show
  1. app.py +79 -0
  2. notebook.ipynb +1152 -0
  3. requirements.txt +4 -0
app.py ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import gradio as gr
2
+ import torch
3
+ import os
4
+ from transformers import pipeline
5
+
6
+ # Konfigurasi Kategori sesuai Proposal Archon
7
+ CATEGORIES = ["Income", "Bills", "Transport", "Retail/E-commerce", "Cash Withdrawal", "Transfer Out", "General Debit"]
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+
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+ class ArchonEngine:
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+ def __init__(self, model_path="archon_v1"):
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+ # Pilar 1: NLP Transaction Classifier [cite: 83]
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+ self.classifier = pipeline(
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+ "text-classification",
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+ model=model_path,
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+ tokenizer=model_path
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+ )
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+
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+ def process(self, text, amount, income, monthly_spending):
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+ # 1. Klasifikasi Transaksi
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+ pred = self.classifier(text)[0]
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+ label_id = int(pred['label'].split('_')[-1])
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+ category = CATEGORIES[label_id]
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+ conf = pred['score']
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+
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+ # 2. Pilar 2: Machine Learning Predictive Model (Risk) [cite: 85]
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+ risk_score = 0.05
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+ ratio = amount / income if income > 0 else 0
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+ if ratio >= 0.25: risk_score += 0.45
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+
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+ spend_rate = monthly_spending / income if income > 0 else 0
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+ if spend_rate >= 0.85: risk_score += 0.35
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+
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+ if category in ["Cash Withdrawal", "Transfer Out"]: risk_score += 0.10
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+
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+ risk_level = "High" if risk_score >= 0.6 else ("Medium" if risk_score >= 0.3 else "Low")
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+
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+ # 3. Pilar 3: Next Best Offer (NBO) Engine [cite: 87]
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+ if risk_level == "High":
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+ recommendation = "Set immediate budget alert + suggest emergency saving plan; show debt counseling resources."
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+ elif category == "Income":
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+ recommendation = "Recommend automatic split: 10% to Emergency Fund, 5% to Investments."
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+ elif category in ["Retail/E-commerce", "General Debit"]:
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+ recommendation = "Offer discount coupons / loyalty suggestion or roundup saving feature."
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+ else:
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+ recommendation = "Maintain current budget; propose small Auto-Save (Rp20k/day)."
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+
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+ return {
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+ "Kategori (Pilar 1)": f"{category} ({conf*100:.2f}%)",
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+ "Level Risiko (Pilar 2)": f"{risk_level} (Score: {risk_score:.2f})",
50
+ "Rekomendasi NBO (Pilar 3)": recommendation
51
+ }
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+
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+ # Inisialisasi Engine
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+ # Pastikan folder 'archon_v1' ada di direktori yang sama
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+ engine = ArchonEngine("archon_v1")
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+
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+ # UI Interface Gradio
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+ with gr.Blocks(title="Archon-AI: Financial Resilience Engine") as demo:
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+ gr.Markdown("# 🛡️ Archon-AI")
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+ gr.Markdown("### Financial Resilience Engine berbasis AI untuk Perbankan Indonesia [cite: 8]")
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+
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+ with gr.Row():
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+ with gr.Column():
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+ input_text = gr.Textbox(label="Narasi Transaksi", placeholder="Contoh: GAJI PT MAJU JAYA")
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+ input_amount = gr.Number(label="Jumlah Transaksi (Rp)")
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+ input_income = gr.Number(label="Total Pendapatan Bulanan (Rp)")
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+ input_spending = gr.Number(label="Total Pengeluaran Bulan Ini (Rp)")
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+ btn = gr.Button("Analisis dengan Archon", variant="primary")
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+
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+ with gr.Column():
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+ output = gr.JSON(label="Hasil Analisis AI")
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+
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+ btn.click(
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+ fn=engine.process,
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+ inputs=[input_text, input_amount, input_income, input_spending],
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+ outputs=output
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+ )
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+
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+ demo.launch()
notebook.ipynb ADDED
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+ "model_name": "DescriptionStyleModel",
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+ "model_module_version": "1.5.0",
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+ "state": {
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+ "_model_module": "@jupyter-widgets/controls",
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+ "_model_module_version": "1.5.0",
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+ "_model_name": "DescriptionStyleModel",
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+ }
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+ }
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+ },
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+ "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
+ ],
926
+ "metadata": {
927
+ "colab": {
928
+ "base_uri": "https://localhost:8080/",
929
+ "height": 375,
930
+ "referenced_widgets": [
931
+ "293f2d1bd51649f498dce24d428e6cba",
932
+ "872414d8fd3a4c41af5710e7dda3b83d",
933
+ "b7a67689c45e41c5bd204206cd39ae44",
934
+ "aa266ef6570f4cbead12481662718cb6",
935
+ "e4807d727df14568806874d8207c270b",
936
+ "2fe7fec5c72645eaa20255aaa3a00139",
937
+ "9157018bce84419da17ebc8cbc0ed0f8",
938
+ "2e5eb516aec746418e599cd55810b0e5",
939
+ "dd17cebd28174846a343865e236ca0a0",
940
+ "67f65a87b5594a71b5247d5b71e426ee",
941
+ "14a7dda6889241dc9b0f468dfcef5ef2",
942
+ "81a83bcd3334462f84baf7fcbb9c2641",
943
+ "0c8287f51f17432d98613c5cfab8bb74",
944
+ "e9230b54e5a649919d175a8e19f0e84a",
945
+ "5b29d6cd6dff46359576c2d62f2cfe40",
946
+ "25019bd15d0d42519fe64176c56194d7",
947
+ "4f0d22b228b443ceba4e030fa87aa062",
948
+ "051bc22d52354ce7b2526a9625b0dbf8",
949
+ "ea057ae4402b4f49af81e8cbf81ef7d6",
950
+ "bd16e0909f5b499ab6651c3ca6a74d29",
951
+ "fa30a14194964f73a89fabd2dc700366",
952
+ "293eeb45c9cd40a9965165eacf601766"
953
+ ]
954
+ },
955
+ "id": "m-NbiKSY_Vgv",
956
+ "outputId": "92907783-ce2b-43ba-c85a-1abf59248c2e"
957
+ },
958
+ "execution_count": 12,
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": {
967
+ "version_major": 2,
968
+ "version_minor": 0,
969
+ "model_id": "293f2d1bd51649f498dce24d428e6cba"
970
+ }
971
+ },
972
+ "metadata": {}
973
+ },
974
+ {
975
+ "output_type": "display_data",
976
+ "data": {
977
+ "text/plain": [
978
+ "Map: 0%| | 0/300 [00:00<?, ? examples/s]"
979
+ ],
980
+ "application/vnd.jupyter.widget-view+json": {
981
+ "version_major": 2,
982
+ "version_minor": 0,
983
+ "model_id": "81a83bcd3334462f84baf7fcbb9c2641"
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
+ "<IPython.core.display.HTML object>"
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
+ }
requirements.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ transformers
2
+ torch
3
+ pandas
4
+ numpy