davanstrien HF staff commited on
Commit
c11ccae
1 Parent(s): 8b691a8
Files changed (1) hide show
  1. app.py +17 -13
app.py CHANGED
@@ -19,11 +19,9 @@ sample_text = [
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  "Summer Travelling in Iceland; being the narrative of two journeys across the island ... With a chapter on Askja by E. Delmar Morgan ... Containing also a literal translation of three sagas. Maps, etc'"
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  ],
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  [
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- 'History of the Monument. With a brief account of the Great Fire of London, which it commemorates. By Charles Welch. (With illustrations and a map of Old London.)',
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- ],
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- [
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- "The history and antiquities of Newbury and its environs [By E. W. Gray.]"
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  ],
 
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  ["""A Christmas Carol"""],
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  ]
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@@ -67,19 +65,25 @@ The model is trained on a particular collection of books digitised by the Britis
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  tokenizer = AutoTokenizer.from_pretrained("TheBritishLibrary/bl-books-genre")
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- model = AutoModelForSequenceClassification.from_pretrained("TheBritishLibrary/bl-books-genre")
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- classifier = pipeline('text-classification',model=model, tokenizer=tokenizer, top_k=10)
 
 
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  def predict(text):
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  predictions = classifier(text)
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- return {pred['label']: float(pred['score']) for pred in predictions}
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-
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- gr.Interface(predict,
 
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  inputs=gr.Textbox(label="Book title"),
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- outputs='label',
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- interpretation='shap', num_shap=10.0, theme="huggingface",
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- examples=sample_text,description=description,article=article,
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- ).launch(enable_queue=True)
 
 
 
 
 
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  "Summer Travelling in Iceland; being the narrative of two journeys across the island ... With a chapter on Askja by E. Delmar Morgan ... Containing also a literal translation of three sagas. Maps, etc'"
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  ],
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  [
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+ "History of the Monument. With a brief account of the Great Fire of London, which it commemorates. By Charles Welch. (With illustrations and a map of Old London.)",
 
 
 
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  ],
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+ ["The history and antiquities of Newbury and its environs [By E. W. Gray.]"],
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  ["""A Christmas Carol"""],
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  ]
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  tokenizer = AutoTokenizer.from_pretrained("TheBritishLibrary/bl-books-genre")
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+ model = AutoModelForSequenceClassification.from_pretrained(
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+ "TheBritishLibrary/bl-books-genre"
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+ )
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+ classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=10)
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  def predict(text):
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  predictions = classifier(text)
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+ return {pred["label"]: float(pred["score"]) for pred in predictions}
 
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+ gr.Interface(
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+ predict,
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  inputs=gr.Textbox(label="Book title"),
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+ outputs="label",
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+ interpretation="shap",
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+ num_shap=10.0,
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+ theme="huggingface",
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+ examples=sample_text,
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+ description=description,
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+ article=article,
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+ ).launch(enable_queue=True)