Main recommender driver function for model.py

model_get_recommendations.py

def make_recommendation(query, metadata=metadata):
  new_row = metadata.iloc[-1,:].copy()
  new_row.iloc[-1] = query
  metadata = metadata.append(new_row)
  count = CountVectorizer(stop_words='english')
  count_matrix = count.fit_transform(metadata['soup'])
  cosine_sim2 = cosine_similarity(count_matrix, count_matrix)
  sim_scores = list(enumerate(cosine_sim2[-1,:]))
  sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
  ranked_titles = []
  for i in range(1, 11):
    indx = sim_scores[i][0]   ranked_titles.append([metadata['title'].iloc[indx], metadata['imdb_id'].iloc[indx], metadata['runtime'].iloc[indx], metadata['release_date'].iloc[indx],    metadata['vote_average'].iloc[indx]])
  return ranked_titles
  
@app.route("/get")
@cross_origin()
def get_recommendations():
  userText = request.args.get('msg')
  response =  make_recommendation(str(userText))
  return jsonify(response)