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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3f7f7977-2d7f-45d9-a5ce-860e9f12b167",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/transformers/utils/generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+ " torch.utils._pytree._register_pytree_node(\n",
+ "/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/transformers/utils/generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+ " torch.utils._pytree._register_pytree_node(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from flask import Flask, jsonify, request, render_template\n",
+ "import json\n",
+ "import os\n",
+ "import logging\n",
+ "import pandas as pd\n",
+ "import datasets, evaluate\n",
+ "from transformers import pipeline\n",
+ "import torch\n",
+ "from datetime import datetime\n",
+ "from functools import partial\n",
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "import string\n",
+ "import nltk\n",
+ "import re\n",
+ "import time\n",
+ "\n",
+ "from transformers import AutoTokenizer, AutoModelForTokenClassification, DataCollatorForTokenClassification, TrainingArguments, Trainer, EarlyStoppingCallback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "59de7380-6213-4d2c-9c21-d2e51a242c98",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from datasets import load_dataset\n",
+ " \n",
+ "dataset = load_dataset(\"surrey-nlp/PLOD-filtered\")\n",
+ "dataset=dataset['train']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e381b9eb-06ea-4333-adea-3aae055d6a56",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1247"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "max(len(example['tokens']) for example in dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c9fccb03-9080-4d79-9323-2a7e29e31c47",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def filter_long(data):\n",
+ " return len(data['tokens']) <= 500\n",
+ "dataset = dataset.filter(filter_long)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "39c416a9-bdde-44c5-a88e-962ec39e6c51",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from datasets import load_dataset\n",
+ "import random\n",
+ "\n",
+ "def print_random_tokens(dataset):\n",
+ " # Get the total number of rows in the dataset\n",
+ " num_rows = len(dataset)\n",
+ " \n",
+ " # Generate 1000 random unique indices from the dataset\n",
+ " random_indices = random.sample(range(num_rows), 1000)\n",
+ " \n",
+ " # Retrieve the 'tokens' from these random indices\n",
+ " random_tokens = dataset.select(random_indices)['tokens']\n",
+ " \n",
+ " # Print each list of tokens\n",
+ " for tokens in random_tokens:\n",
+ " print(tokens)\n",
+ "\n",
+ "# Load the dataset\n",
+ "# dataset = load_dataset(\"surrey-nlp/PLOD-unfiltered\")\n",
+ "# train_dataset = dataset[\"train\"]\n",
+ "\n",
+ "# # Example usage of the function\n",
+ "# print_random_tokens(train_dataset)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "15fb65b5-33dd-43d4-92a7-de043b4334a9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "app = Flask(__name__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "64969fe0-4820-457a-ac94-b737ee928727",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@app.route('/use-pretrained', methods=['GET'])\n",
+ "def use_pretrained():\n",
+ " \"\"\"Endpoint to load and use a pre-trained model.\"\"\"\n",
+ " try:\n",
+ " # Load the pre-trained model and tokenizer\n",
+ " global loaded_tokenizer, loaded_model\n",
+ " loaded_tokenizer = AutoTokenizer.from_pretrained(\"SciBERT-finetuned-NER\")\n",
+ " loaded_model = AutoModelForTokenClassification.from_pretrained(\"SciBERT-finetuned-NER\")\n",
+ " return jsonify(success=\"Pre-trained model loaded successfully\")\n",
+ " except Exception as e:\n",
+ " return jsonify(error=str(e)), 500"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "3358edd7-ced6-4a2e-b838-ac354c9c0809",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@app.route('/predict', methods=['POST'])\n",
+ "## Train must be run before this\n",
+ "## run from command line with: curl -s -H \"Content-Type: application/json\" -X POST -d '{\"input\": }' localhost:8080/predict\n",
+ "## examples:\n",
+ "## curl -s -H \"Content-Type: application/json\" -X POST -d '{\"input\": \"For this purpose the Gothenburg Young Persons Empowerment Scale (GYPES) was developed.\"}' localhost:8080/predict\n",
+ "## curl -s -H \"Content-Type: application/json\" -X POST -d '{\"input\": \"Recent work by us and others suggest that the host’s heat shock protein 90 (Hsp90) chaperone can modulate the evolutionary paths traversed by viruses [18, 19].\"}' localhost:8080/predict\n",
+ "def predict():\n",
+ " inputs = request.get_json().get('input')\n",
+ " converted_inputs = split_string(inputs)\n",
+ " predictions = predict_tags(converted_inputs, loaded_tokenizer, loaded_model, label_encoding)\n",
+ "\n",
+ " ner_tags = [i[1] for i in predictions]\n",
+ " save_results(converted_inputs, ner_tags)\n",
+ " return jsonify(predictions = str(predictions))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "92347951-af52-4c1f-b81f-f89cd3272c3a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@app.route('/test-model', methods=['GET'])\n",
+ "def test_model():\n",
+ " start_time = time.time()\n",
+ " \"\"\"Endpoint to test the pre-trained model on 1000 random dataset samples.\"\"\"\n",
+ " dataset = load_dataset(\"surrey-nlp/PLOD-unfiltered\", split='train')\n",
+ "\n",
+ " def filter_long(data):\n",
+ " return len(data['tokens']) <= 400\n",
+ " dataset = dataset.filter(filter_long)\n",
+ " \n",
+ " sample_indices = random.sample(range(len(dataset)), 20000)\n",
+ " sampled_data = dataset.select(sample_indices)\n",
+ "\n",
+ " results = []\n",
+ " print(\"in test_model\")\n",
+ " for item in sampled_data:\n",
+ " # Join tokens to form a single string as the model expects a sequence\n",
+ " input_text = \" \".join(item['tokens'])\n",
+ " # Tokenize the text\n",
+ " inputs = loaded_tokenizer(input_text, return_tensors=\"pt\")\n",
+ " # Get model predictions\n",
+ " with torch.no_grad():\n",
+ " outputs = loaded_model(**inputs)\n",
+ " predictions = torch.argmax(outputs.logits, dim=-1)\n",
+ " tokens = loaded_tokenizer.convert_ids_to_tokens(inputs['input_ids'].squeeze())\n",
+ " predicted_tags = [dataset.features['ner_tags'].feature.int2str(p) for p in predictions.squeeze().tolist()]\n",
+ "\n",
+ " # Combine tokens and their predicted tags\n",
+ " token_predictions = list(zip(tokens, predicted_tags))\n",
+ " results.append({'text': input_text, 'predictions': token_predictions})\n",
+ " total_time = time.time() - start_time\n",
+ " print(\"Total time taken: \" + str(total_time))\n",
+ "\n",
+ " return jsonify(results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "e00a3561-f701-4482-866e-68d26cc8d3d8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * Serving Flask app '__main__'\n",
+ " * Debug mode: off\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\n",
+ " * Running on http://127.0.0.1:8080\n",
+ "Press CTRL+C to quit\n",
+ "/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/transformers/utils/generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+ " torch.utils._pytree._register_pytree_node(\n",
+ "127.0.0.1 - - [24/May/2024 11:21:35] \"GET /use-pretrained HTTP/1.1\" 200 -\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in test_model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[2024-05-24 11:28:18,175] ERROR in app: Exception on /test-model [GET]\n",
+ "Traceback (most recent call last):\n",
+ " File \"/Users/meenusathyanarayanan/.local/lib/python3.11/site-packages/flask/app.py\", line 1455, in wsgi_app\n",
+ " response = self.full_dispatch_request()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/.local/lib/python3.11/site-packages/flask/app.py\", line 869, in full_dispatch_request\n",
+ " rv = self.handle_user_exception(e)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/.local/lib/python3.11/site-packages/flask/app.py\", line 867, in full_dispatch_request\n",
+ " rv = self.dispatch_request()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/.local/lib/python3.11/site-packages/flask/app.py\", line 852, in dispatch_request\n",
+ " return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/var/folders/3c/_rc81dfd35755sd95j1m_6zc0000gn/T/ipykernel_50926/1002882848.py\", line 23, in test_model\n",
+ " outputs = loaded_model(**inputs)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+ " return self._call_impl(*args, **kwargs)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+ " return forward_call(*args, **kwargs)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py\", line 1756, in forward\n",
+ " outputs = self.bert(\n",
+ " ^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+ " return self._call_impl(*args, **kwargs)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+ " return forward_call(*args, **kwargs)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py\", line 1015, in forward\n",
+ " embedding_output = self.embeddings(\n",
+ " ^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+ " return self._call_impl(*args, **kwargs)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+ " return forward_call(*args, **kwargs)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/Users/meenusathyanarayanan/anaconda3/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py\", line 238, in forward\n",
+ " embeddings += position_embeddings\n",
+ "RuntimeError: The size of tensor a (534) must match the size of tensor b (512) at non-singleton dimension 1\n",
+ "127.0.0.1 - - [24/May/2024 11:28:18] \"GET /test-model HTTP/1.1\" 500 -\n"
+ ]
+ }
+ ],
+ "source": [
+ "if __name__ == '__main__':\n",
+ "\t# Entry point for running on the local machine\n",
+ "\t# host is localhost; port is 8080; this file is index (.py)\n",
+ "\tapp.run(host='127.0.0.1', port=8080, debug=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c24f86ed-2e75-4cb2-a66a-0db6a18aeb05",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "05340c7e-9ba7-464e-9853-e6011581cee7",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}