File size: 4,151 Bytes
f4c8685 dad89e4 f4c8685 7c4e98c f4c8685 6868ece f4c8685 0d5194e f4c8685 dad89e4 f4c8685 7c4e98c 0670634 3020ad1 506e32d 5c5363c 0670634 3020ad1 0670634 dad89e4 6868ece 0670634 c66809e 0670634 dad89e4 0670634 c66809e 0670634 c66809e 801318e c66809e 0670634 dad89e4 0670634 f4c8685 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
import json
from urllib import request
from fastapi import FastAPI
from starlette.middleware.sessions import SessionMiddleware
from starlette.responses import HTMLResponse, RedirectResponse
from starlette.requests import Request
import gradio as gr
import uvicorn
from fastapi.responses import HTMLResponse
from fastapi.responses import RedirectResponse
import pandas as pd
import spotipy
from spotipy import oauth2
import heatmap
PORT_NUMBER = 8080
SPOTIPY_CLIENT_ID = 'c087fa97cebb4f67b6f08ba841ed8378'
SPOTIPY_CLIENT_SECRET = 'ae27d6916d114ac4bb948bb6c58a72d9'
SPOTIPY_REDIRECT_URI = 'https://hf-hackathon-2023-01-spotify.hf.space'
SCOPE = 'user-library-read'
sp_oauth = oauth2.SpotifyOAuth(SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET, SPOTIPY_REDIRECT_URI, scope=SCOPE)
app = FastAPI()
app.add_middleware(SessionMiddleware, secret_key="w.o.w")
@app.get('/', response_class=HTMLResponse)
async def homepage(request: Request):
token = request.session.get('token')
if token:
return RedirectResponse("/gradio")
url = str(request.url)
code = sp_oauth.parse_response_code(url)
if code != url:
token_info = sp_oauth.get_access_token(code)
request.session['token'] = token_info['access_token']
return RedirectResponse("/gradio")
auth_url = sp_oauth.get_authorize_url()
return "<a href='" + auth_url + "'>Login to Spotify</a>"
from vega_datasets import data
iris = data.iris()
def scatter_plot_fn(request: gr.Request):
token = request.request.session.get('token')
if token:
sp = spotipy.Spotify(token)
results = sp.current_user()
print(results)
return gr.ScatterPlot(
value=iris,
)
def heatmap_plot_fn(request: gr.Request):
token = request.request.session.get('token')
if token:
sp = spotipy.Spotify(token)
data = heatmap.build_heatmap(heatmap.fetch_recent_songs(sp))
fig, _ = heatmap.plot(data)
return fig
def get_features(spotify):
features = []
for index in range(0, 10):
results = spotify.current_user_saved_tracks(offset=index*50, limit=50)
track_ids = [item['track']['id'] for item in results['items']]
features.extend(spotify.audio_features(track_ids))
df = pd.DataFrame(data=features)
names = [
'danceability',
'energy',
'loudness',
'speechiness',
'acousticness',
'instrumentalness',
'liveness',
'valence',
'tempo',
]
features_means = df[names].mean()
# print (features_means.to_json())
return features_means
##########
def get_started():
# redirects to spotify and comes back
# then generates plots
return
with gr.Blocks() as demo:
gr.Markdown(" ## Spotify Analyzer 🥳🎉")
gr.Markdown("This app analyzes how cool your music taste is. We dare you to take this challenge!")
with gr.Row():
get_started_btn = gr.Button("Get Started")
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Column():
hm_plot = gr.Plot().style(container=True)
with gr.Column():
plot = gr.ScatterPlot(show_label=False).style(container=True)
with gr.Row():
with gr.Column():
plot = gr.ScatterPlot(show_label=False).style(container=True)
with gr.Column():
plot = gr.ScatterPlot(show_label=False).style(container=True)
with gr.Row():
gr.Markdown(" ### We have recommendations for you!")
with gr.Row():
gr.Dataframe(
headers=["Song", "Album", "Artist"],
datatype=["str", "str", "str"],
label="Reccomended Songs",
value=[["something", "something", "something"], ["something", "something", "something"]] # TODO: replace with actual reccomendations once get_started() is implemeted.
)
demo.load(fn=scatter_plot_fn, outputs=plot)
demo.load(fn=heatmap_plot_fn, output=hm_plot)
gradio_app = gr.mount_gradio_app(app, demo, "/gradio")
uvicorn.run(app, host="0.0.0.0", port=7860)
|