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app.py
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app.py
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import random
import re
from os import environ
from pathlib import Path
import requests
from flask_caching import Cache
from flask import Flask, jsonify, request
from flask_cors import CORS
from gensim.models import KeyedVectors
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
app = Flask(__name__)
CORS(app)
DEBUG = not "FILTER_PRODUCTION" in environ
data_dir = "data" if DEBUG else "/data"
if DEBUG:
app.config["CACHE_TYPE"] = "null"
else:
app.config["CACHE_TYPE"] = "redis"
app.config["CACHE_REDIS_URL"] = environ["REDIS_URL"]
app.config["CACHE_DEFAULT_TIMEOUT"] = 60 * 60 * 24 * 14 # 2 weeks
cache = Cache(app)
vecs = {}
for m in Path(data_dir).glob("*.model"):
vecs[m.stem] = KeyedVectors.load(str(m), mmap="r")
@app.route("/typeahead/<vec_name>")
@cache.cached(query_string=True)
def typeahead(vec_name):
q = request.args.get("q", type=str)
if q == '':
return jsonify({"tokens": []})
v = vecs[vec_name]
q = re.sub(r"\d+", "0", q)
q = q.lower()
tokens = [t for t in v.index2entity if t.startswith(q)]
tokens = sorted(tokens, key=len)
return jsonify({"tokens": tokens[:10]})
# make sure to check wheter file exists
@app.route("/vectors/typeahead_videos/<vec_name>")
@cache.cached(query_string=True)
def typeahead_videos(vec_name):
q = request.args.get("q", type=str)
if q == '':
return jsonify({"tokens": []})
v = vecs[vec_name]
q = re.sub(r"\d+", "0", q)
q = q.lower()
tokens = [t for t in v.index2entity if t.startswith(q)]
tokens = sorted(tokens, key=len)
results = []
for t in tokens:
if len(results) >= 10:
break
r = requests.head('http://kommentare.vis.one/videos/' + t + '.mp4')
if r.ok:
results.append(t)
return jsonify({"tokens": results})
@app.route("/vectors/nearest/<vec_name>")
@cache.cached(query_string=True)
def nearest(vec_name):
q, n = request.args.get("q"), request.args.get("n", 10, type=int)
if q == '':
return jsonify({"tokens": [], "vectors": []})
v = vecs[vec_name]
results = v.most_similar(q, topn=n)
tokens, _ = list(zip(*results))
tokens = [q] + list(tokens)
vectors = [v[t] for t in tokens]
vectors = PCA(n_components=2).fit_transform(vectors)
vectors = MinMaxScaler((-1, 1)).fit_transform(vectors)
return jsonify({"tokens": tokens, "vectors": vectors.tolist()})
@app.route("/vectors/dist/<vec_name>")
@cache.cached(query_string=True)
def dist(vec_name):
tokens = request.args.getlist("q")
v = vecs[vec_name]
vectors = [v[t] for t in tokens]
vectors = PCA(n_components=2).fit_transform(vectors)
vectors = MinMaxScaler((-1, 1)).fit_transform(vectors)
return jsonify({"tokens": tokens, "vectors": vectors.tolist()})
@app.route("/vectors/sim/<vec_name>")
@cache.cached(query_string=True)
def sim(vec_name):
"""get similarities
"""
q, n = request.args.get("q"), request.args.get("n", 10, type=int)
v = vecs[vec_name]
results = v.most_similar(q, topn=n)
return jsonify({"tokens": [r[0] for r in results], "sims": [r[1] for r in results]})
@app.route("/vectors/sim_multiple/<vec_name>")
@cache.cached(query_string=True)
def sim_multiple(vec_name):
"""get similarities
"""
qs = request.args.getlist("q")
v = vecs[vec_name]
return jsonify({"tokens": qs, "sims": [v.similarity(qs[0], x) for x in qs[1:]]})
@app.route("/vectors/sim_random/<vec_name>")
@cache.cached(query_string=True)
def sim_random(vec_name):
"""get similarities, n random tokens
"""
q, n = request.args.get("q"), request.args.get("n", 10, type=int)
v = vecs[vec_name]
tokens = [q] + random.sample(v.index2entity, n)
return jsonify({"tokens": tokens, "sims": [v.similarity(q, x) for x in tokens[1:]]})
@app.route("/vectors/token_random/<vec_name>")
@cache.cached(query_string=True, timeout=10) # 10 seconds
def random_tokens(vec_name):
n = request.args.get("n", 100, type=int)
v = vecs[vec_name]
tokens = random.sample(v.index2entity, n)
return jsonify({"tokens": tokens})
if __name__ == "__main__":
app.run(debug=DEBUG)