def analyze_article(articles: List[Article]): ents = [] for article in articles: doc = nlp(article.content) for ent in doc.ents: ents.append({"text": ent.text, "label": ent.label_}) return {"ents": ents}
def analyze_article4(article: Article): """example to run ML model of input given by body """ ents =[] doc = nlp(article.content) for ent in doc.ents: ents.append({"text":ent.text,"label":ent.label_}) return {"message": article.content,"comments": article.comments, "ents": ents}
def analyze_article (article_id:int,q:str=None): """Eample of path variable as input, article_id is required and q is optional """ # also convert string to integer as it expects an interger cnt =0 if q: doc = nlp(q) cnt = len(doc.ents) return {"article_id":article_id, "q":q, "count": cnt }
def display_main(articles: List[Article]): ents = [] comments = [] for article in articles: for comment in article.comment: comments.append(comment.upper()) doc = nlp(article.content) for ent in doc.ents: ents.append({"text": ent.text, "label": ent.label_}) return {"comments": comments, "entities": ents}
def analyze_article5(articles: List[Article]): """example to run *ML model* of **List of inputs** * comment1 * comment2 """ ents =[] for article in articles: doc = nlp(article.content) for ent in doc.ents: ents.append({"text":ent.text,"label":ent.label_}) return {"message": article.content,"comments": article.comments, "ents": ents}
def analyze_article(articles: List[Article]): """ Analyze an article and extract entities with ⚡spaCy⚡ Statistical models *will* have **errors**. * Extract entities * Display comments """ ents = [] for article in articles: doc = nlp(article.content) for ent in doc.ents: ents.append({"entity": ent.text, "label": ent.label_}) return {"entities": ents}
def analyze_article(articles: List[Article]): """ Analyze an article and extract entities with 🌟 spaCy 🌟 Statistical Models *will* have **errors** * Extract Entities * Scream Comments """ ents=[] comments=[] for article in articles: for comment in article.comments: comments.append(comment.upper()) doc = nlp(article.content) for ent in doc.ents: ents.append({"text":ent.text, "label":ent.label_}) return{"ents":ents,"comments":comments} # return {"message":article.content,"comments":article.comments,"ents":ents}