Spaces:
Running
on
Zero
Running
on
Zero
Lord-Raven
commited on
Commit
·
19a483c
1
Parent(s):
de9282c
Messing with configuration.
Browse files
app.py
CHANGED
@@ -37,23 +37,8 @@ tokenizer_name = "MoritzLaurer/deberta-v3-base-zeroshot-v2.0"
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# model = ORTModelForSequenceClassification.from_pretrained(model_name, export=True, provider="CUDAExecutionProvider")
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# tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, model_max_length=512)
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# session_options = onnxruntime.SessionOptions()
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# session_options.log_severity_level = 0
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# print(f"ORTModelForSequenceClassification.from_pretrained")
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# model = ORTModelForSequenceClassification.from_pretrained(
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# "distilbert-base-uncased-finetuned-sst-2-english",
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# export=True,
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# provider="CUDAExecutionProvider",
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# session_options=session_options
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# )
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# print(f"AutoTokenizer.from_pretrained")
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# tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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print(f"pipeline")
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classifier = pipeline(task="zero-shot-classification", model=model_name, tokenizer=tokenizer_name, device="cuda:0")
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print(f"Testing 1")
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def classify(data_string, request: gradio.Request):
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if request:
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if request.headers["origin"] not in ["https://statosphere-3704059fdd7e.c5v4v4jx6pq5.win", "https://crunchatize-77a78ffcc6a6.c5v4v4jx6pq5.win", "https://crunchatize-2-2b4f5b1479a6.c5v4v4jx6pq5.win", "https://tamabotchi-2dba63df3bf1.c5v4v4jx6pq5.win", "https://ravenok-statosphere-backend.hf.space", "https://lord-raven.github.io"]:
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@@ -64,7 +49,6 @@ def classify(data_string, request: gradio.Request):
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# else:
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return zero_shot_classification(data)
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print(f"Testing 2")
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@spaces.GPU
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def zero_shot_classification(data):
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results = classifier(data['sequence'], candidate_labels=data['candidate_labels'], hypothesis_template=data['hypothesis_template'], multi_label=data['multi_label'])
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@@ -75,7 +59,6 @@ def create_sequences(data):
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# return ['###Given:\n' + data['sequence'] + '\n###End Given\n###Hypothesis:\n' + data['hypothesis_template'].format(label) + "\n###End Hypothesis" for label in data['candidate_labels']]
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return [data['sequence'] + '\n' + data['hypothesis_template'].format(label) for label in data['candidate_labels']]
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print(f"Testing 3")
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# def few_shot_classification(data):
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# sequences = create_sequences(data)
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# print(sequences)
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@@ -91,13 +74,11 @@ print(f"Testing 3")
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# response_dict = {'scores': scores, 'labels': labels}
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# print(response_dict)
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# response_string = json.dumps(response_dict)
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# return
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print(f"Testing 4")
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gradio_interface = gradio.Interface(
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fn = classify,
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inputs = gradio.Textbox(label="JSON Input"),
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outputs = gradio.Textbox()
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)
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gradio_interface.launch()
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# model = ORTModelForSequenceClassification.from_pretrained(model_name, export=True, provider="CUDAExecutionProvider")
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# tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, model_max_length=512)
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classifier = pipeline(task="zero-shot-classification", model=model_name, tokenizer=tokenizer_name, device="cuda:0")
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def classify(data_string, request: gradio.Request):
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if request:
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if request.headers["origin"] not in ["https://statosphere-3704059fdd7e.c5v4v4jx6pq5.win", "https://crunchatize-77a78ffcc6a6.c5v4v4jx6pq5.win", "https://crunchatize-2-2b4f5b1479a6.c5v4v4jx6pq5.win", "https://tamabotchi-2dba63df3bf1.c5v4v4jx6pq5.win", "https://ravenok-statosphere-backend.hf.space", "https://lord-raven.github.io"]:
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# else:
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return zero_shot_classification(data)
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@spaces.GPU
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def zero_shot_classification(data):
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results = classifier(data['sequence'], candidate_labels=data['candidate_labels'], hypothesis_template=data['hypothesis_template'], multi_label=data['multi_label'])
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# return ['###Given:\n' + data['sequence'] + '\n###End Given\n###Hypothesis:\n' + data['hypothesis_template'].format(label) + "\n###End Hypothesis" for label in data['candidate_labels']]
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return [data['sequence'] + '\n' + data['hypothesis_template'].format(label) for label in data['candidate_labels']]
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# def few_shot_classification(data):
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# sequences = create_sequences(data)
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# print(sequences)
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# response_dict = {'scores': scores, 'labels': labels}
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# print(response_dict)
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# response_string = json.dumps(response_dict)
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# return response_strin
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gradio_interface = gradio.Interface(
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fn = classify,
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inputs = gradio.Textbox(label="JSON Input"),
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outputs = gradio.Textbox()
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)
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gradio_interface.launch()
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