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}