NimaBoscarino commited on
Commit
21c3f3d
1 Parent(s): 85292f8

Update app.py

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Files changed (1) hide show
  1. app.py +4 -7
app.py CHANGED
@@ -8,16 +8,13 @@ import gradio as gr
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  pd.options.mode.chained_assignment = None # Turn off SettingWithCopyWarning
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  auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
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- pickled = pickle.load(open(hf_hub_download("NimaBoscarino/playlist-generator", repo_type="dataset", filename="clean-large_embeddings_msmarco-MiniLM-L-6-v3.pkl"), "rb"))
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  songs = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator", repo_type="dataset", filename="songs_new.csv"))
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- verses = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator-private", repo_type="dataset", filename="verses.csv", use_auth_token=auth_token))
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- lyrics = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator-private", repo_type="dataset", filename="lyrics_new.csv", use_auth_token=auth_token))
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  embedder = SentenceTransformer('msmarco-MiniLM-L-6-v3')
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- song_ids = pickled["song_ids"]
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- corpus_embeddings = pickled["embeddings"]
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-
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  def generate_playlist(prompt):
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  prompt_embedding = embedder.encode(prompt, convert_to_tensor=True)
@@ -63,7 +60,7 @@ with demo:
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  gr.Markdown(
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  """
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  Enter a prompt and generate a playlist based on ✨semantic similarity✨
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- This was built using Sentence Transformers and Gradio – blog post coming soon!
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  """)
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  song_prompt = gr.TextArea(
 
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  pd.options.mode.chained_assignment = None # Turn off SettingWithCopyWarning
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  auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
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+ corpus_embeddings = pickle.load(open(hf_hub_download("NimaBoscarino/playlist-generator", repo_type="dataset", filename="verse-embeddings.pkl"), "rb"))
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  songs = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator", repo_type="dataset", filename="songs_new.csv"))
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+ verses = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator", repo_type="dataset", filename="verses.csv"))
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+ lyrics = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator-private", repo_type="dataset", filename="lyrics_new.csv", use_auth_token=True))
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  embedder = SentenceTransformer('msmarco-MiniLM-L-6-v3')
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  def generate_playlist(prompt):
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  prompt_embedding = embedder.encode(prompt, convert_to_tensor=True)
 
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  gr.Markdown(
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  """
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  Enter a prompt and generate a playlist based on ✨semantic similarity✨
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+ This was built using Sentence Transformers and Gradio – [see the blog](https://huggingface.co/blog/your-first-ml-project)!
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  """)
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  song_prompt = gr.TextArea(