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import gradio as gr
import json
from collections import Counter, defaultdict
import os
def analyze_spotify_data(files):
# files: list of file objects
# We'll parse each JSON file and aggregate the data
all_records = []
for f in files:
try:
data = json.load(open(f))
if isinstance(data, list):
all_records.extend(data)
else:
# If the JSON file isn't a list at top-level, skip or handle differently
continue
except:
# If there's an error in loading JSON, skip that file
continue
# If no valid data found
if not all_records:
return "No valid data found in the uploaded files."
# Aggregate listening stats
artist_counter = Counter()
track_counter = Counter()
album_counter = Counter() # Note: album info not provided in the sample data, will only work if album is in the data
# We also want to consider total listening time per artist/track/album
artist_time = defaultdict(int)
track_time = defaultdict(int)
album_time = defaultdict(int)
# Attempt to detect if albumName is present
album_present = all("albumName" in record for record in all_records if isinstance(record, dict))
for record in all_records:
if not isinstance(record, dict):
continue
artist = record.get("artistName", "Unknown Artist")
track = record.get("trackName", "Unknown Track")
ms_played = record.get("msPlayed", 0)
# Album may not be present; handle gracefully
album = record.get("albumName", "Unknown Album") if album_present else None
artist_counter[artist] += 1
track_counter[track] += 1
artist_time[artist] += ms_played
track_time[track] += ms_played
if album_present and album is not None:
album_counter[album] += 1
album_time[album] += ms_played
# Determine top artists by number of tracks played (frequency) and also by time
top_artists_by_count = artist_counter.most_common(10)
top_artists_by_time = sorted(artist_time.items(), key=lambda x: x[1], reverse=True)[:10]
# Determine top tracks by frequency and by time
top_tracks_by_count = track_counter.most_common(10)
top_tracks_by_time = sorted(track_time.items(), key=lambda x: x[1], reverse=True)[:10]
# Determine top albums if available
if album_present:
top_albums_by_count = album_counter.most_common(10)
top_albums_by_time = sorted(album_time.items(), key=lambda x: x[1], reverse=True)[:10]
else:
top_albums_by_count = [("No album data found", 0)]
top_albums_by_time = [("No album data found", 0)]
# Format the results into a readable output
def format_list(title, data_list, time_data=False):
result = f"**{title}**\n"
if not time_data:
for i, (name, count) in enumerate(data_list, 1):
result += f"{i}. {name} ({count} plays)\n"
else:
for i, (name, ms) in enumerate(data_list, 1):
hours = ms / (1000*60*60)
result += f"{i}. {name} ({hours:.2f} hours)\n"
result += "\n"
return result
output = ""
output += format_list("Top Artists by Play Count", top_artists_by_count, time_data=False)
output += format_list("Top Artists by Listening Time", top_artists_by_time, time_data=True)
output += format_list("Top Tracks by Play Count", top_tracks_by_count, time_data=False)
output += format_list("Top Tracks by Listening Time", top_tracks_by_time, time_data=True)
output += format_list("Top Albums by Play Count", top_albums_by_count, time_data=False)
output += format_list("Top Albums by Listening Time", top_albums_by_time, time_data=True)
return output
with gr.Blocks() as demo:
gr.Markdown("# Spotify Listening Data Analyzer")
gr.Markdown("Upload your Spotify JSON files (e.g., 'StreamingHistory0.json', 'StreamingHistory1.json', etc.) to get an overview of your top artists, albums, and tracks.")
file_input = gr.File(file_count="multiple", type="filepath", label="Upload JSON files")
analyze_button = gr.Button("Analyze")
output_box = gr.Markdown()
analyze_button.click(fn=analyze_spotify_data, inputs=file_input, outputs=output_box)
if __name__ == "__main__":
demo.launch() |