From 575e0594261108a96324c34aeed370b39aab5595 Mon Sep 17 00:00:00 2001 From: ShanukaLakshan Date: Sun, 29 Oct 2023 18:25:54 +0530 Subject: [PATCH 1/2] You can add this model --- app.ipynb | 937 +++++++++++++++++++++++++++++++++++++++++++++ model.pkl | Bin 0 -> 2609788 bytes title_category.csv | 632 ++++++++++++++++++++++++++++++ vectorizer.pkl | Bin 0 -> 830 bytes 4 files changed, 1569 insertions(+) create mode 100644 app.ipynb create mode 100644 model.pkl create mode 100644 title_category.csv create mode 100644 vectorizer.pkl diff --git a/app.ipynb b/app.ipynb new file mode 100644 index 0000000..4768b42 --- /dev/null +++ b/app.ipynb @@ -0,0 +1,937 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd \n", + "# !pip install sentence_transformers" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IndexTitle of the videoType of Video
00.0Everything Happens For A Reason | Muniba MazariNaN
1NaNSprituality__##__General KnowledgeNaN
21.0Detachment from Overthinking & Stress | Boost ...Sprituality__##__Mental Health__##__Chill Mix
32.0I Meditated Every Day & This Is What Happened ...Sprituality__##__Mental Health
43.0Vipassanā Meditation: a daily meditation timer...Mental Health
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" + ], + "text/plain": [ + " Index Title of the video \\\n", + "0 0.0 Everything Happens For A Reason | Muniba Mazari \n", + "1 NaN Sprituality__##__General Knowledge \n", + "2 1.0 Detachment from Overthinking & Stress | Boost ... \n", + "3 2.0 I Meditated Every Day & This Is What Happened ... \n", + "4 3.0 Vipassanā Meditation: a daily meditation timer... \n", + "\n", + " Type of Video \n", + "0 NaN \n", + "1 NaN \n", + "2 Sprituality__##__Mental Health__##__Chill Mix \n", + "3 Sprituality__##__Mental Health \n", + "4 Mental Health " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('title_category.csv')\n", + "columns = df.columns.tolist()\n", + "columns[0] = 'Index'\n", + "df.columns = columns\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IndexTitle of the videoType of Video
1NaNSprituality__##__General KnowledgeNaN
17NaNGeneral KnowledgeNaN
19NaNGeneral KnowledgeNaN
25NaNSpritualityNaN
27NaNSpritualityNaN
102NaNMental Health__##__Chill MixNaN
104NaNMental HealthNaN
106NaNMental HealthNaN
108NaNMental HealthNaN
128NaNSpritualityNaN
130NaNMental HealthNaN
134NaNSpritualityNaN
136NaNFitnessNaN
138NaNMental HealthNaN
140NaNMental HealthNaN
142NaNMental HealthNaN
145NaNMental HealthNaN
147NaNMental HealthNaN
150NaNMental Health__##__SpritualityNaN
154NaNLucid DreamingNaN
156NaNFitnessNaN
158NaNScienceNaN
160NaNSpritualityNaN
165NaNMental HealthNaN
167NaNMental HealthNaN
169NaNSocializingNaN
172NaNSpritualityNaN
176NaNSprituality__##__Mental HealthNaN
178NaNMental HealthNaN
180NaNMental HealthNaN
183NaNMental HealthNaN
185NaNMental HealthNaN
199NaNMeditation__##__Chill MixNaN
202NaNMeditation__##__Chill MixNaN
206NaNGeneral KnowledgeNaN
208NaNGeneral KnowledgeNaN
210NaNGeneral KnowledgeNaN
212NaNGeneral KnowledgeNaN
214NaNGeneral KnowledgeNaN
216NaNGeneral KnowledgeNaN
218NaNSpritualityNaN
220NaNGeneral KnowledgeNaN
222NaNSpritualityNaN
224NaNMental HealthNaN
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" + ], + "text/plain": [ + " Index Title of the video Type of Video\n", + "1 NaN Sprituality__##__General Knowledge NaN\n", + "17 NaN General Knowledge NaN\n", + "19 NaN General Knowledge NaN\n", + "25 NaN Sprituality NaN\n", + "27 NaN Sprituality NaN\n", + "102 NaN Mental Health__##__Chill Mix NaN\n", + "104 NaN Mental Health NaN\n", + "106 NaN Mental Health NaN\n", + "108 NaN Mental Health NaN\n", + "128 NaN Sprituality NaN\n", + "130 NaN Mental Health NaN\n", + "134 NaN Sprituality NaN\n", + "136 NaN Fitness NaN\n", + "138 NaN Mental Health NaN\n", + "140 NaN Mental Health NaN\n", + "142 NaN Mental Health NaN\n", + "145 NaN Mental Health NaN\n", + "147 NaN Mental Health NaN\n", + "150 NaN Mental Health__##__Sprituality NaN\n", + "154 NaN Lucid Dreaming NaN\n", + "156 NaN Fitness NaN\n", + "158 NaN Science NaN\n", + "160 NaN Sprituality NaN\n", + "165 NaN Mental Health NaN\n", + "167 NaN Mental Health NaN\n", + "169 NaN Socializing NaN\n", + "172 NaN Sprituality NaN\n", + "176 NaN Sprituality__##__Mental Health NaN\n", + "178 NaN Mental Health NaN\n", + "180 NaN Mental Health NaN\n", + "183 NaN Mental Health NaN\n", + "185 NaN Mental Health NaN\n", + "199 NaN Meditation__##__Chill Mix NaN\n", + "202 NaN Meditation__##__Chill Mix NaN\n", + "206 NaN General Knowledge NaN\n", + "208 NaN General Knowledge NaN\n", + "210 NaN General Knowledge NaN\n", + "212 NaN General Knowledge NaN\n", + "214 NaN General Knowledge NaN\n", + "216 NaN General Knowledge NaN\n", + "218 NaN Sprituality NaN\n", + "220 NaN General Knowledge NaN\n", + "222 NaN Sprituality NaN\n", + "224 NaN Mental Health NaN" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[pd.isnull(df['Index'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(df.shape[0]):\n", + " if pd.isnull(df.iloc[i,2]):\n", + " df.iloc[i,2] = df.iloc[i+1,1]\n", + "df.dropna(subset=['Index'], inplace=True)\n", + "df.index = df['Index']\n", + "df.drop(['Index'], axis = 1, inplace=True)\n", + "df['Type of Video'] = df['Type of Video'].apply(lambda x: str(x).replace('__##__',','))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Title of the videoType of Video
Index
0.0Everything Happens For A Reason | Muniba MazariSprituality,General Knowledge
1.0Detachment from Overthinking & Stress | Boost ...Sprituality,Mental Health,Chill Mix
2.0I Meditated Every Day & This Is What Happened ...Sprituality,Mental Health
3.0Vipassanā Meditation: a daily meditation timer...Mental Health
4.0Why Meditate? | Change your Brain's Default ModeSprituality,Science,Mental Health
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" + ], + "text/plain": [ + " Title of the video \\\n", + "Index \n", + "0.0 Everything Happens For A Reason | Muniba Mazari \n", + "1.0 Detachment from Overthinking & Stress | Boost ... \n", + "2.0 I Meditated Every Day & This Is What Happened ... \n", + "3.0 Vipassanā Meditation: a daily meditation timer... \n", + "4.0 Why Meditate? | Change your Brain's Default Mode \n", + "\n", + " Type of Video \n", + "Index \n", + "0.0 Sprituality,General Knowledge \n", + "1.0 Sprituality,Mental Health,Chill Mix \n", + "2.0 Sprituality,Mental Health \n", + "3.0 Mental Health \n", + "4.0 Sprituality,Science,Mental Health " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:594: FutureWarning: Passing an int for a boolean parameter is deprecated in version 1.2 and won't be supported anymore in version 1.4.\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:547: UserWarning: The parameter 'stop_words' will not be used since 'analyzer' != 'word'\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:555: UserWarning: The parameter 'token_pattern' will not be used since 'analyzer' != 'word'\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "tf_idf = TfidfVectorizer(min_df=2, max_features = None, strip_accents = 'unicode', norm='l2',\n", + " analyzer = 'char', token_pattern = r'\\w{1,}',ngram_range=(1,5),\n", + " use_idf = 1, smooth_idf = 1, sublinear_tf = 1, stop_words = 'english')\n", + "features = tf_idf.fit_transform(df['Title of the video']).toarray()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(625, 14723)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from sentence_transformers import SentenceTransformer\n", + "embedder = SentenceTransformer('bert-base-nli-mean-tokens')\n", + "bert_features = embedder.encode(df['Title of the video'].tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "semantic_embedder = SentenceTransformer('bert-base-nli-stsb-mean-tokens')\n", + "semantic_bert_features = semantic_embedder.encode(df['Title of the video'].tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "bert_features = np.array(bert_features)\n", + "semantic_bert_features = np.array(semantic_bert_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(625, 16259)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_features = np.hstack((features, bert_features, semantic_bert_features))\n", + "final_features.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "vectorizer = CountVectorizer(tokenizer=lambda x: x.split(','))\n", + "y = vectorizer.fit_transform(df['Type of Video'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(625, 16259)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_features.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(625, 20)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.multioutput import MultiOutputClassifier\n", + "from sklearn.linear_model import SGDClassifier\n", + "clf = MultiOutputClassifier(SGDClassifier(max_iter=4000)).fit(final_features, y.toarray())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_embedding(text):\n", + " word_transform = tf_idf.transform([text]).toarray()[0]\n", + " bert_transform = embedder.encode([text], show_progress_bar=False)[0]\n", + " semantic_bert_transform = semantic_embedder.encode([text], show_progress_bar=False)[0]\n", + " embedding = np.hstack((word_transform, bert_transform, semantic_bert_transform))\n", + " return embedding\n", + "\n", + "def get_terms(pred_list):\n", + " return [w.title() for w in vectorizer.inverse_transform([pred_list])[0]]\n", + "\n", + "def get_topics(text):\n", + " text_embedding = generate_embedding(text)\n", + " pred_list = clf.predict([text_embedding])[0]\n", + " return get_terms(pred_list)\n", + "\n", + "def increment_learn(text, topics):\n", + " available_topics = vectorizer.get_feature_names()\n", + " for topic in topics.split(','):\n", + " if topic.lower() not in available_topics:\n", + " return -1\n", + " text_embedding = generate_embedding(text)\n", + " topics = vectorizer.transform([topics]).toarray()[0]\n", + " clf.partial_fit([text_embedding], [topics])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['General Knowledge']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "example = 'Eric Weinstein: Revolutionary Ideas in Science, Math, and Society | Artificial Intelligence Podcast'\n", + "get_topics(example)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Sprituality']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "title = 'Consciousness -- the final frontier | Dada Gunamuktananda | TEDxNoosa 2014'\n", + "get_topics(title)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "# Define a custom tokenizer function\n", + "def custom_tokenizer(text):\n", + " return text.split(',')\n", + "\n", + "# Create the CountVectorizer with the custom tokenizer\n", + "vectorizer = CountVectorizer(tokenizer=custom_tokenizer)\n", + "\n", + "# Fit and transform the data\n", + "y = vectorizer.fit_transform(df['Type of Video'])\n", + "\n", + "# Save the model\n", + "import pickle\n", + "pickle.dump(clf, open('model.pkl', 'wb'))\n", + "pickle.dump(vectorizer, open('vectorizer.pkl', 'wb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['General Knowledge']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pickle\n", + "\n", + "# Load model\n", + "model = pickle.load(open('model.pkl','rb'))\n", + "vectorizer = pickle.load(open('vectorizer.pkl','rb'))\n", + "\n", + "# Use model\n", + "example = 'Eric Weinstein: Revolutionary Ideas in Science, Math, and Society | Artificial Intelligence Podcast'\n", + "get_topics(example)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Sprituality']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "title = 'Machine Learning for Humans, Part 1: Introduction'\n", + "get_topics(title)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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2.0I Meditated Every Day & This Is What Happened ...Sprituality,Mental Health
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" - ], - "text/plain": [ - " Title of the video \\\n", - "Index \n", - "0.0 Everything Happens For A Reason | Muniba Mazari \n", - "1.0 Detachment from Overthinking & Stress | Boost ... \n", - "2.0 I Meditated Every Day & This Is What Happened ... \n", - "3.0 Vipassanā Meditation: a daily meditation timer... \n", - "4.0 Why Meditate? | Change your Brain's Default Mode \n", - "\n", - " Type of Video \n", - "Index \n", - "0.0 Sprituality,General Knowledge \n", - "1.0 Sprituality,Mental Health,Chill Mix \n", - "2.0 Sprituality,Mental Health \n", - "3.0 Mental Health \n", - "4.0 Sprituality,Science,Mental Health " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:594: FutureWarning: Passing an int for a boolean parameter is deprecated in version 1.2 and won't be supported anymore in version 1.4.\n", - " warnings.warn(\n", - "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:547: UserWarning: The parameter 'stop_words' will not be used since 'analyzer' != 'word'\n", - " warnings.warn(\n", - "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:555: UserWarning: The parameter 'token_pattern' will not be used since 'analyzer' != 'word'\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "tf_idf = TfidfVectorizer(min_df=2, max_features = None, strip_accents = 'unicode', norm='l2',\n", @@ -598,38 +72,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(625, 14723)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "features.shape" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "from sentence_transformers import SentenceTransformer\n", "embedder = SentenceTransformer('bert-base-nli-mean-tokens')\n", @@ -638,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -648,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -658,20 +112,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(625, 16259)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "final_features = np.hstack((features, bert_features, semantic_bert_features))\n", "final_features.shape" @@ -679,18 +122,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "vectorizer = CountVectorizer(tokenizer=lambda x: x.split(','))\n", @@ -699,47 +133,25 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(625, 16259)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "final_features.shape" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(625, 20)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "y.shape" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -750,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -781,20 +193,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['General Knowledge']" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\n", "example = 'Eric Weinstein: Revolutionary Ideas in Science, Math, and Society | Artificial Intelligence Podcast'\n", @@ -803,20 +204,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Sprituality']" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "title = 'Consciousness -- the final frontier | Dada Gunamuktananda | TEDxNoosa 2014'\n", "get_topics(title)" @@ -824,18 +214,84 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# Save the model to a pickle file\n", + "with open('multioutput_classifier_model.pkl', 'wb') as model_file:\n", + " pickle.dump(clf, model_file)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# Load the model from the pickle file\n", + "with open('multioutput_classifier_model.pkl', 'rb') as model_file:\n", + " clf = pickle.load(model_file)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "\n", @@ -857,54 +313,309 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# Load model\n", + "model = pickle.load(open('model.pkl','rb'))\n", + "vectorizer = pickle.load(open('vectorizer.pkl','rb'))\n", + "\n", + "# Use model\n", + "example = 'Eric Weinstein: Revolutionary Ideas in Science, Math, and Society | Artificial Intelligence Podcast'\n", + "get_topics(example)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "title = 'Machine Learning for Humans, Part 1: Introduction'\n", + "get_topics(title)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import numpy as np\n", + "# from flask import Flask, request, jsonify\n", + "# import pickle\n", + "\n", + "# app = Flask(__name__)\n", + "# model = pickle.load(open('model.pkl', 'rb'))\n", + "# vectorizer = pickle.load(open('vectorizer.pkl', 'rb'))\n", + "\n", + "# def get_topics(text):\n", + "# # Your model prediction logic here\n", + "# # You can use 'model' and 'vectorizer' to make predictions\n", + "# # Replace this with your actual prediction code\n", + "# prediction = model.predict(vectorizer.transform([text]))\n", + "# return prediction\n", + "\n", + "# @app.route('/predict', methods=['POST'])\n", + "# def predict():\n", + "# # For rendering results on HTML GUI\n", + "# text = request.form['text']\n", + "# topics = get_topics(text)\n", + "# return render_template('index.html', prediction_text='Topics: {}'.format(topics))\n", + "\n", + "# @app.route('/predict_api', methods=['POST'])\n", + "# def predict_api():\n", + "# # For direct API calls through requests\n", + "# data = request.get_json()\n", + "# text = data['text']\n", + "# topics = get_topics(text)\n", + "# return jsonify({\"topics\": topics})\n", + "\n", + "# if __name__ == \"__main__\":\n", + "# app.run(debug=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:594: FutureWarning: Passing an int for a boolean parameter is deprecated in version 1.2 and won't be supported anymore in version 1.4.\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:547: UserWarning: The parameter 'stop_words' will not be used since 'analyzer' != 'word'\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:555: UserWarning: The parameter 'token_pattern' will not be used since 'analyzer' != 'word'\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n", + " warnings.warn(\n" + ] + }, { "data": { "text/plain": [ - "['General Knowledge']" + "[]" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pickle\n", + "import numpy as np \n", + "import pandas as pd \n", + "# !pip install sentence_transformers\n", + "\n", + "df = pd.read_csv('title_category.csv')\n", + "columns = df.columns.tolist()\n", + "columns[0] = 'Index'\n", + "df.columns = columns\n", + "\n", + "df[pd.isnull(df['Index'])]\n", + "\n", + "for i in range(df.shape[0]):\n", + " if pd.isnull(df.iloc[i,2]):\n", + " df.iloc[i,2] = df.iloc[i+1,1]\n", + "df.dropna(subset=['Index'], inplace=True)\n", + "df.index = df['Index']\n", + "df.drop(['Index'], axis = 1, inplace=True)\n", + "df['Type of Video'] = df['Type of Video'].apply(lambda x: str(x).replace('__##__',','))\n", + "\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "tf_idf = TfidfVectorizer(min_df=2, max_features = None, strip_accents = 'unicode', norm='l2',\n", + " analyzer = 'char', token_pattern = r'\\w{1,}',ngram_range=(1,5),\n", + " use_idf = 1, smooth_idf = 1, sublinear_tf = 1, stop_words = 'english')\n", + "features = tf_idf.fit_transform(df['Title of the video']).toarray()\n", + "\n", + "\n", + "from sentence_transformers import SentenceTransformer\n", + "embedder = SentenceTransformer('bert-base-nli-mean-tokens')\n", + "bert_features = embedder.encode(df['Title of the video'].tolist())\n", + "\n", + "\n", + "semantic_embedder = SentenceTransformer('bert-base-nli-stsb-mean-tokens')\n", + "semantic_bert_features = semantic_embedder.encode(df['Title of the video'].tolist())\n", + "\n", + "\n", + "bert_features = np.array(bert_features)\n", + "semantic_bert_features = np.array(semantic_bert_features)\n", + "\n", + "final_features = np.hstack((features, bert_features, semantic_bert_features))\n", + "final_features.shape\n", + "\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "vectorizer = CountVectorizer(tokenizer=lambda x: x.split(','))\n", + "y = vectorizer.fit_transform(df['Type of Video'])\n", + "\n", + "\n", + "from sklearn.multioutput import MultiOutputClassifier\n", + "from sklearn.linear_model import SGDClassifier\n", + "clf = MultiOutputClassifier(SGDClassifier(max_iter=4000)).fit(final_features, y.toarray())\n", + "\n", + "\n", + "\n", + "def generate_embedding(text):\n", + " word_transform = tf_idf.transform([text]).toarray()[0]\n", + " bert_transform = embedder.encode([text], show_progress_bar=False)[0]\n", + " semantic_bert_transform = semantic_embedder.encode([text], show_progress_bar=False)[0]\n", + " embedding = np.hstack((word_transform, bert_transform, semantic_bert_transform))\n", + " return embedding\n", + "\n", + "def get_terms(pred_list):\n", + " return [w.title() for w in vectorizer.inverse_transform([pred_list])[0]]\n", + "\n", + "def get_topics(text):\n", + " text_embedding = generate_embedding(text)\n", + " pred_list = clf.predict([text_embedding])[0]\n", + " return get_terms(pred_list)\n", + "\n", + "def increment_learn(text, topics):\n", + " available_topics = vectorizer.get_feature_names()\n", + " for topic in topics.split(','):\n", + " if topic.lower() not in available_topics:\n", + " return -1\n", + " text_embedding = generate_embedding(text)\n", + " topics = vectorizer.transform([topics]).toarray()[0]\n", + " clf.partial_fit([text_embedding], [topics])\n", "\n", - "# Load model\n", - "model = pickle.load(open('model.pkl','rb'))\n", - "vectorizer = pickle.load(open('vectorizer.pkl','rb'))\n", "\n", - "# Use model\n", "example = 'Eric Weinstein: Revolutionary Ideas in Science, Math, and Society | Artificial Intelligence Podcast'\n", - "get_topics(example)" + "get_topics(example)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:594: FutureWarning: Passing an int for a boolean parameter is deprecated in version 1.2 and won't be supported anymore in version 1.4.\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:547: UserWarning: The parameter 'stop_words' will not be used since 'analyzer' != 'word'\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:555: UserWarning: The parameter 'token_pattern' will not be used since 'analyzer' != 'word'\n", + " warnings.warn(\n", + "/mnt/7866F86B66F82B8E/git/task-manager/Depresio/myenv/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n", + " warnings.warn(\n" + ] + }, { "data": { "text/plain": [ - "['Sprituality']" + "['Mathematics']" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sentence_transformers import SentenceTransformer\n", + "from sklearn.multioutput import MultiOutputClassifier\n", + "from sklearn.linear_model import SGDClassifier\n", "\n", - "title = 'Machine Learning for Humans, Part 1: Introduction'\n", - "get_topics(title)" + "# Read the CSV file\n", + "df = pd.read_csv('title_category.csv')\n", + "\n", + "# Rename the 'Index' column\n", + "df = df.rename(columns={'Unnamed: 0': 'Index'})\n", + "\n", + "# Drop rows with NaN values in the 'Index' column\n", + "df = df.dropna(subset=['Index'])\n", + "\n", + "# Replace '__##__' in 'Type of Video' with commas\n", + "df['Type of Video'] = df['Type of Video'].str.replace('__##__', ',')\n", + "\n", + "# Create a TF-IDF vectorizer\n", + "tf_idf = TfidfVectorizer(min_df=2, max_features=None, strip_accents='unicode',\n", + " norm='l2', analyzer='char', token_pattern=r'\\w{1,}', ngram_range=(1, 5),\n", + " use_idf=1, smooth_idf=1, stop_words='english')\n", + "\n", + "# Fit and transform the 'Title of the video' to TF-IDF features\n", + "features = tf_idf.fit_transform(df['Title of the video']).toarray()\n", + "\n", + "# Initialize BERT-based sentence transformers\n", + "embedder = SentenceTransformer('bert-base-nli-mean-tokens')\n", + "semantic_embedder = SentenceTransformer('bert-base-nli-stsb-mean-tokens')\n", + "\n", + "# Encode 'Title of the video' using BERT models\n", + "bert_features = embedder.encode(df['Title of the video'].tolist())\n", + "semantic_bert_features = semantic_embedder.encode(df['Title of the video'].tolist())\n", + "\n", + "# Combine the features\n", + "final_features = np.hstack((features, bert_features, semantic_bert_features))\n", + "\n", + "# Replace NaN values in 'Type of Video' with an empty string\n", + "df['Type of Video'].fillna('', inplace=True)\n", + "\n", + "# Create a CountVectorizer for 'Type of Video'\n", + "vectorizer = CountVectorizer(tokenizer=lambda x: x.split(','))\n", + "\n", + "# Transform 'Type of Video' into binary vectors\n", + "y = vectorizer.fit_transform(df['Type of Video'])\n", + "\n", + "# Create a MultiOutputClassifier with an SGDClassifier\n", + "clf = MultiOutputClassifier(SGDClassifier(max_iter=4000)).fit(final_features, y.toarray())\n", + "\n", + "def generate_embedding(text):\n", + " word_transform = tf_idf.transform([text]).toarray()[0]\n", + " bert_transform = embedder.encode([text], show_progress_bar=False)[0]\n", + " semantic_bert_transform = semantic_embedder.encode([text], show_progress_bar=False)[0]\n", + " embedding = np.hstack((word_transform, bert_transform, semantic_bert_transform))\n", + " return embedding\n", + "\n", + "def get_terms(pred_list):\n", + " return [w.title() for w in vectorizer.inverse_transform([pred_list])[0]]\n", + "\n", + "def get_topics(text):\n", + " text_embedding = generate_embedding(text)\n", + " pred_list = clf.predict([text_embedding])[0]\n", + " return get_terms(pred_list)\n", + "\n", + "def increment_learn(text, topics):\n", + " available_topics = vectorizer.get_feature_names()\n", + " for topic in topics.split(','):\n", + " if topic.lower() not in available_topics:\n", + " return -1\n", + " text_embedding = generate_embedding(text)\n", + " topics = vectorizer.transform([topics]).toarray()[0]\n", + " clf.partial_fit([text_embedding], [topics])\n", + "\n", + "example = 'Eric Weinstein: Revolutionary Ideas in Science, Math, and Society | Artificial Intelligence Podcast'\n", + "get_topics(example)\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/app.py b/app.py new file mode 100644 index 0000000..71c87e2 --- /dev/null +++ b/app.py @@ -0,0 +1,63 @@ +from flask import Flask, request, jsonify +import numpy as np +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.feature_extraction.text import CountVectorizer +from sentence_transformers import SentenceTransformer +from sklearn.multioutput import MultiOutputClassifier +from sklearn.linear_model import SGDClassifier + +app = Flask(__name__) + +# Load the model and data (you can load them as globals) +df = pd.read_csv('title_category.csv') +df = df.rename(columns={'Unnamed: 0': 'Index'}) +df = df.dropna(subset=['Index']) +df['Type of Video'] = df['Type of Video'].str.replace('__##__', ',') + +tf_idf = TfidfVectorizer(min_df=2, max_features=None, strip_accents='unicode', + norm='l2', analyzer='char', token_pattern=r'\w{1,}', ngram_range=(1, 5), + use_idf=1, smooth_idf=1, stop_words='english') +features = tf_idf.fit_transform(df['Title of the video']).toarray() +embedder = SentenceTransformer('bert-base-nli-mean-tokens') +semantic_embedder = SentenceTransformer('bert-base-nli-stsb-mean-tokens') +bert_features = embedder.encode(df['Title of the video'].tolist()) +semantic_bert_features = semantic_embedder.encode(df['Title of the video'].tolist()) +final_features = np.hstack((features, bert_features, semantic_bert_features)) + +df['Type of Video'].fillna('', inplace=True) +vectorizer = CountVectorizer(tokenizer=lambda x: x.split(',')) +y = vectorizer.fit_transform(df['Type of Video']) +clf = MultiOutputClassifier(SGDClassifier(max_iter=4000)).fit(final_features, y.toarray()) + +def generate_embedding(text): + word_transform = tf_idf.transform([text]).toarray()[0] + bert_transform = embedder.encode([text], show_progress_bar=False)[0] + semantic_bert_transform = semantic_embedder.encode([text], show_progress_bar=False)[0] + embedding = np.hstack((word_transform, bert_transform, semantic_bert_transform)) + return embedding + +def get_terms(pred_list): + return [w.title() for w in vectorizer.inverse_transform([pred_list])[0]] + +def get_topics(text): + text_embedding = generate_embedding(text) + pred_list = clf.predict([text_embedding])[0] + return get_terms(pred_list) + +@app.route('/classify', methods=['POST']) +def classify_video(): + + print("Model called") + data = request.get_json() + video_title = 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+1,21 @@ +import requests + +# URL of your API +url = 'http://127.0.0.1:5000/classify' + +# Video title you want to classify +video_title = "Eric Weinstein: Revolutionary Ideas in Science, Math, and Society | Artificial Intelligence Podcast" + +# Prepare the data as a JSON payload +data = {'video_title': video_title} + +# Send the POST request +response = requests.post(url, json=data) + +# Check the response +if response.status_code == 200: + # The response should contain the categories + categories = response.json()['categories'] + print("Categories:", categories) +else: + print("Error:", response.text)

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