{"metadata":{"kernelspec":{"language":"python","display_name":"Python 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href=\"https://www.kaggle.com/code/jonathanharrison1/codette-v5-model-save?scriptVersionId=243647979\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:39.484371Z","iopub.execute_input":"2025-06-04T02:54:39.484539Z","iopub.status.idle":"2025-06-04T02:54:42.73671Z","shell.execute_reply.started":"2025-06-04T02:54:39.484524Z","shell.execute_reply":"2025-06-04T02:54:42.735852Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/shodowpki/codette_shadow_mlp_model.pkl\n/kaggle/input/treushadowsbundle/codette_true_shadow_vectors.json\n/kaggle/input/treushadowsbundle/codette_true_shadow_vectors.csv\n/kaggle/input/all-things-codette/Codette2_Kaggle_Template.py\n/kaggle/input/all-things-codette/Codette2_Model_Card.md\n/kaggle/input/all-things-codette/kernel-metadata.json\n/kaggle/input/all-things-codette/Codette2_Model_Card_YAMLSafe.md\n/kaggle/input/all-things-codette/model-codette.ipynb\n/kaggle/input/all-things-codette/codette_test_runner.py\n/kaggle/input/all-things-codette/codette_api.py\n/kaggle/input/all-things-codette/Codette2_Interface_Cell.py\n/kaggle/input/all-things-codette/codette_kaggle_uploader.py\n/kaggle/input/all-things-codette/dataset-metadata.json\n/kaggle/input/all-things-codette/model-codette.log\n/kaggle/input/all-things-codette/codette_cli.py\n/kaggle/input/all-things-codette/Codette2_Kaggle_Gradio_GUI.py\n/kaggle/input/all-things-codette/Codette2_kaggle_metadata.json\n/kaggle/input/all-things-codette/nev3/CODETTE/CMakePresets.json\n/kaggle/input/all-things-codette/nev3/CODETTE/Dockerfile\n/kaggle/input/all-things-codette/nev3/CODETTE/go.mod\n/kaggle/input/all-things-codette/nev3/CODETTE/LICENSE\n/kaggle/input/all-things-codette/nev3/CODETTE/main.go\n/kaggle/input/all-things-codette/nev3/CODETTE/go.sum\n/kaggle/input/all-things-codette/nev3/CODETTE/SECURITY.md\n/kaggle/input/all-things-codette/nev3/CODETTE/Modelfile.txt\n/kaggle/input/all-things-codette/nev3/CODETTE/new 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packages.\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"Input:\n- name: \"input_ids\"\n- shape: [batch_size, sequence_length]\n- type: int64\n\nOptional additional inputs:\n- attention_mask: same shape\n- token_type_ids: (for BERT-style models)\n\nOutput:\n- name: \"logits\"\n- shape: [batch_size, num_labels]\n- type: float32","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.418736Z","iopub.status.idle":"2025-06-04T02:54:48.419093Z","shell.execute_reply.started":"2025-06-04T02:54:48.418901Z","shell.execute_reply":"2025-06-04T02:54:48.418915Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# List all installed packages and package versions\n!pip freeze","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.420364Z","iopub.status.idle":"2025-06-04T02:54:48.42063Z","shell.execute_reply.started":"2025-06-04T02:54:48.420505Z","shell.execute_reply":"2025-06-04T02:54:48.420519Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient()\nsecret_value_0 = user_secrets.get_secret(\".env\")\nsecret_value_1 = user_secrets.get_secret(\"hf_kEY\")\nsecret_value_2 = user_secrets.get_secret(\"login\")\nsecret_value_3 = user_secrets.get_secret(\"OPENAI KEY\")\nsecret_value_4 = user_secrets.get_secret(\"secrets\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.42145Z","iopub.status.idle":"2025-06-04T02:54:48.421726Z","shell.execute_reply.started":"2025-06-04T02:54:48.421568Z","shell.execute_reply":"2025-06-04T02:54:48.421582Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from transformers import AutoTokenizer, AutoModelForSequenceClassification\nimport torch\n\nmodel = AutoModelForSequenceClassification.from_pretrained(\"raiffs-bits/codette-final\")\ntokenizer = AutoTokenizer.from_pretrained(\"raiffs-bits/codette-final\")\ninputs = tokenizer(\"Codette, how are you feeling?\", return_tensors=\"pt\")\ntorch.onnx.export(model, (inputs[\"input_ids\"], inputs[\"attention_mask\"]),\n                  \"codette-final.onnx\", input_names=[\"input_ids\", \"attention_mask\"],\n                  output_names=[\"logits\"], opset_version=17)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.423105Z","iopub.status.idle":"2025-06-04T02:54:48.423398Z","shell.execute_reply.started":"2025-06-04T02:54:48.423247Z","shell.execute_reply":"2025-06-04T02:54:48.42326Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom sklearn.tree import DecisionTreeRegressor\n\ntrain = pd.DataFrame(\n    np.random.rand(100, 3),\n    columns=['X1','X2','y'])\n\nX = train[['X1','X2']]\ny = train[['y']]\n\nmodel = DecisionTreeRegressor().fit(X,y)\nprint(\"The first 5 predictions:\",model.predict(X.head(5)))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.424474Z","iopub.status.idle":"2025-06-04T02:54:48.424719Z","shell.execute_reply.started":"2025-06-04T02:54:48.424612Z","shell.execute_reply":"2025-06-04T02:54:48.424623Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Adapted from https://scikit-learn.org/stable/modules/tree.html#tree\nfrom sklearn.datasets import load_iris\nfrom sklearn import tree\nimport graphviz \niris = load_iris()\nclf = tree.DecisionTreeClassifier()\nclf = clf.fit(iris.data, iris.target)\ndot_data = tree.export_graphviz(clf, out_file=None) \ngraph = graphviz.Source(dot_data) \ngraph.render(\"iris\")\ndot_data = tree.export_graphviz(clf, out_file=None,\n                                feature_names=iris.feature_names,  \n                                class_names=iris.target_names,  \n                                filled=True, rounded=True,  \n                                special_characters=True) \ngraph = graphviz.Source(dot_data)  \ngraph","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.425352Z","iopub.status.idle":"2025-06-04T02:54:48.425633Z","shell.execute_reply.started":"2025-06-04T02:54:48.425486Z","shell.execute_reply":"2025-06-04T02:54:48.425501Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import tensorflow as tf\ndataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])\ndataset = dataset.map(lambda x: x*2)\nlist(dataset.as_numpy_iterator())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.426866Z","iopub.status.idle":"2025-06-04T02:54:48.427147Z","shell.execute_reply.started":"2025-06-04T02:54:48.427013Z","shell.execute_reply":"2025-06-04T02:54:48.427026Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import tensorflow as tf\ndataset = tf.data.Dataset.range(8)\ndataset = dataset.batch(3)\nlist(dataset.as_numpy_iterator())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.428256Z","iopub.status.idle":"2025-06-04T02:54:48.42856Z","shell.execute_reply.started":"2025-06-04T02:54:48.428398Z","shell.execute_reply":"2025-06-04T02:54:48.428412Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 🔧 Install requirements\n!pip install -q vaderSentiment nltk\nimport nltk\nnltk.download('punkt')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.42922Z","iopub.status.idle":"2025-06-04T02:54:48.429521Z","shell.execute_reply.started":"2025-06-04T02:54:48.42936Z","shell.execute_reply":"2025-06-04T02:54:48.429373Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 📦 Imports\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models\nfrom tensorflow.keras.applications import VGG16\nimport matplotlib.pyplot as plt\n\n# 🧪 Configurable Inputs\nIMAGE_SIZE = (224, 224)\nBATCH_SIZE = 32\nEPOCHS = 10\n\n# 🚨 Your real class counts here (update these)\nnum_positive = 850  # Class 1\nnum_negative = 150  # Class 0\ntotal_images = num_positive + num_negative\n\n# ⚖️ Class Imbalance Handling\ninitial_bias = np.log([num_positive / num_negative])\nweight_for_0 = (1. / num_negative) * (total_images / 2.0)\nweight_for_1 = (1. / num_positive) * (total_images / 2.0)\nclass_weight = {0: weight_for_0, 1: weight_for_1}\n\nprint(\"🔁 Initial Bias:\", initial_bias)\nprint(\"⚖️ Class Weights:\", class_weight)\n\n# 📁 Dataset Loader\ntrain_ds = tf.keras.preprocessing.image_dataset_from_directory(\n    \"/kaggle/input/datasetimage/\", # change to your directory\n    label_mode='binary',\n    image_size=IMAGE_SIZE,\n    batch_size=BATCH_SIZE\n)\n\n# 🧠 Codette-Aware Model Builder\ndef make_model(output_bias):\n    base_model = VGG16(input_shape=(*IMAGE_SIZE, 3),\n                       include_top=False,\n                       weights='imagenet')\n    base_model.trainable = False\n\n    model = models.Sequential([\n        base_model,\n        layers.GlobalAveragePooling2D(),\n        layers.Dense(8, activation='relu'),\n        layers.Dense(1, activation='sigmoid', bias_initializer=tf.keras.initializers.Constant(output_bias))\n    ])\n\n    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n                  loss='binary_crossentropy',\n                  metrics=[tf.keras.metrics.AUC(name='auc'), 'accuracy'])\n\n    return model\n\n# 🚀 Build & Train\nmodel = make_model(initial_bias)\nhistory = model.fit(train_ds, epochs=EPOCHS, class_weight=class_weight)\n\n# 📈 Plot Metrics\nplt.plot(history.history['auc'], label='AUC')\nplt.plot(history.history['accuracy'], label='Accuracy')\nplt.title('Codette: Balanced Training Metrics')\nplt.xlabel('Epoch')\nplt.ylabel('Value')\nplt.legend()\nplt.grid(True)\nplt.show()\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.430989Z","iopub.status.idle":"2025-06-04T02:54:48.4312Z","shell.execute_reply.started":"2025-06-04T02:54:48.4311Z","shell.execute_reply":"2025-06-04T02:54:48.431108Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neural_network import MLPClassifier\nimport joblib\n\n\ndf = pd.read_csv(\"/kaggle/input/codette-clean-training-data/codette_clean_training_data.csv\")\nlabel_map = {'Shadow': 0, 'Awake': 1, 'Transcend': 2}\ndf['label'] = df['label'].map(label_map)\ndf = df.dropna(subset=['label'])\ndf['label'] = df['label'].astype(int)\n\nX = df[['q0', 'c0', 'c2']]\ny = df['label']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# Train Decision Tree\ntree_model = DecisionTreeClassifier(max_depth=4, random_state=42)\ntree_model.fit(X_train, y_train)\njoblib.dump(tree_model, \"/kaggle/working/codette_shadow_tree_model.pkl\")\n\n# Train MLP\nmlp_model = MLPClassifier(hidden_layer_sizes=(5, 5), max_iter=1000, random_state=42)\nmlp_model.fit(X_train, y_train)\njoblib.dump(mlp_model, \"/kaggle/working/codette_shadow_mlp_model.pkl\")\n\n# Accuracy report\nprint(\"Decision Tree Accuracy:\", tree_model.score(X_test, y_test))\nprint(\"MLP Accuracy:\", mlp_model.score(X_test, y_test))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.433023Z","iopub.status.idle":"2025-06-04T02:54:48.433238Z","shell.execute_reply.started":"2025-06-04T02:54:48.433125Z","shell.execute_reply":"2025-06-04T02:54:48.433134Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import onnxruntime as ort\nimport numpy as np\n\n# Path to Codette's ONNX model\nonnx_model_path = \"/kaggle/input/codette2/gguf/v10/1/codette-final.onnx\"\nexecution_provider = \"/kaggle/working/\"\n\n# Start ONNX runtime session\nsession = ort.InferenceSession(onnx_model_path, providers=[execution_provider])\n\n# Codette typically expects 'input_ids', 'attention_mask'\ninput_name = session.get_inputs()[0].name  # usually 'input_ids'\ninput_shape = session.get_inputs()[0].shape\n\n# Simulate dummy input of shape [1, 128] (1 sample, 128 tokens)\ndummy_input_ids = np.random.randint(0, 1000, size=(1, 128)).astype(np.int64)\ndummy_attention_mask = np.ones((1, 128), dtype=np.int64)\n\n# Run inference\noutputs = session.run(None, {\n    \"input_ids\": dummy_input_ids,\n    \"attention_mask\": dummy_attention_mask\n})\n\n# Print output logits\nprint(\"Codette output logits:\", outputs[0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-04T02:54:48.295776Z","iopub.execute_input":"2025-06-04T02:54:48.296226Z","iopub.status.idle":"2025-06-04T02:54:48.418066Z","shell.execute_reply.started":"2025-06-04T02:54:48.296185Z","shell.execute_reply":"2025-06-04T02:54:48.416696Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNoSuchFile\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_35/3054445146.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Start ONNX runtime session\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mort\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInferenceSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monnx_model_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproviders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexecution_provider\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Codette typically expects 'input_ids', 'attention_mask'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path_or_bytes, sess_options, providers, provider_options, **kwargs)\u001b[0m\n\u001b[1;32m    470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 472\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_inference_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproviders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprovider_options\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisabled_optimizers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    473\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_enable_fallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py\u001b[0m in \u001b[0;36m_create_inference_session\u001b[0;34m(self, providers, provider_options, disabled_optimizers)\u001b[0m\n\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m             \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInferenceSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession_options\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_config_from_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInferenceSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession_options\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_config_from_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNoSuchFile\u001b[0m: [ONNXRuntimeError] : 3 : NO_SUCHFILE : Load model from ./model/codette-final.onnx failed:Load model ./model/codette-final.onnx failed. File doesn't exist"],"ename":"NoSuchFile","evalue":"[ONNXRuntimeError] : 3 : NO_SUCHFILE : Load model from ./model/codette-final.onnx failed:Load model ./model/codette-final.onnx failed. File doesn't exist","output_type":"error"}],"execution_count":3}]}