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{
   "dev": {
      "app_function": "app.app",
      "profile_name": null,
      "project_name": "moviebot",
      "runtime": "python3.6",
      "s3_bucket": "sugges"
   },
   "production": {
      "app_function": "app.app",

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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 = []

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bucket = "sugges"
s3 = boto3.client('s3', config=Config(connect_timeout=600, read_timeout=600, retries={'max_attempts': 10}))
metadata = pd.read_csv(s3.get_object(Bucket= bucket, Key="metadata.csv")['Body'])
credits_ = pd.read_csv(s3.get_object(Bucket= bucket, Key="credits.csv")['Body'])
keywords = pd.read_csv(s3.get_object(Bucket= bucket, Key="keywords.csv")['Body'])

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from flask import Flask, jsonify, render_template, request
from flask_cors import CORS, cross_origin
import pandas as pd
from ast import literal_eval
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import boto3
from botocore.config import Config

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