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| import os | |
| import json | |
| import requests | |
| import spacy | |
| import concurrent.futures | |
| gpt3_url = "https://api.openai.com/v1/completions" | |
| gpt3_api_key = os.environ["OPENAI_KEY"] | |
| gpt3_headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": f"Bearer {gpt3_api_key}", | |
| } | |
| def paraphrase(sent): | |
| gpt3_payload = { | |
| "model": "text-davinci-003", | |
| "prompt": "Following is an original sentence followed by paraphrased version " | |
| + "of it with diverse choice of words:\n\noriginal: Once upon a time a group of " | |
| + "rats in forest were searching for water\n\nparaphrase: long ago in a jungle " | |
| + "a pack of rats were thirsty looking for drinking water\n###\noriginal: " | |
| + "{0}\nparaphrase:".format(sent), | |
| "max_tokens": 256, | |
| "temperature": 0.85, | |
| "top_p": 1, | |
| "frequency_penalty": 0.72, | |
| "presence_penalty": 0.72, | |
| "stop": ["###"], | |
| } | |
| response = requests.request( | |
| method="POST", url=gpt3_url, headers=gpt3_headers, json=gpt3_payload | |
| ) | |
| response = response.json() | |
| paraphrase_sent = response["choices"][0]["text"] | |
| return paraphrase_sent.strip() | |
| def lambda_handler(event, context): | |
| """Sample pure Lambda function | |
| Parameters | |
| ---------- | |
| event: dict, required | |
| API Gateway Lambda Proxy Input Format | |
| Event doc: https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html#api-gateway-simple-proxy-for-lambda-input-format | |
| context: object, required | |
| Lambda Context runtime methods and attributes | |
| Context doc: https://docs.aws.amazon.com/lambda/latest/dg/python-context-object.html | |
| Returns | |
| ------ | |
| API Gateway Lambda Proxy Output Format: dict | |
| Return doc: https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html | |
| """ | |
| raw_text = r"{0}".format(event["body"]) | |
| json_payload = json.loads(raw_text) | |
| input_text = json_payload["text"] | |
| ### break text into sentences | |
| nlp = spacy.load( | |
| "en_core_web_sm", disable=["ner", "tagger", "lemmatizer", "textcat"] | |
| ) | |
| sentences = nlp(input_text).sents | |
| ### get each sentence paraphrased, use concurrency for speed | |
| with concurrent.futures.ThreadPoolExecutor() as executor: | |
| futures = [ | |
| executor.submit(paraphrase, sent.text.strip()) | |
| for sent in sentences | |
| if sent.text.strip() != "" | |
| ] | |
| paraphrased_sentences = [future.result() for future in futures] | |
| output = {"paraphrased_text": " ".join(paraphrased_sentences)} | |
| ### join the paraphrased sentences and return as full paraphrased text | |
| return { | |
| "statusCode": 200, | |
| "body": json.dumps(output), | |
| } | |