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vickeee465
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e1df718
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Parent(s):
093e523
finding bottlenecks pt1
Browse files- interfaces/manifesto.py +37 -17
interfaces/manifesto.py
CHANGED
@@ -1,3 +1,4 @@
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import gradio as gr
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import os
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@@ -9,6 +10,18 @@ from huggingface_hub import HfApi
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from label_dicts import MANIFESTO_LABEL_NAMES
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HF_TOKEN = os.environ["hf_read"]
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languages = [
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@@ -24,23 +37,30 @@ def build_huggingface_path(language: str):
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def predict(text, model_id, tokenizer_id):
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device = torch.device("cpu")
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with
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return output_pred, output_info
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def predict_cap(text, language):
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import time
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import gradio as gr
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import os
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from label_dicts import MANIFESTO_LABEL_NAMES
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class RuntimeMeasure:
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def __enter__(self):
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self.start_time = time.time()
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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end_time = time.time()
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runtime = end_time - self.start_time
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gr.Info(f"Runtime: {runtime} seconds")
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def m(msg):
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return RuntimeMeasure(msg)
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HF_TOKEN = os.environ["hf_read"]
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languages = [
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def predict(text, model_id, tokenizer_id):
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device = torch.device("cpu")
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with m("Loading model"):
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model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
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with m("Loading tokenizer"):
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
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with m("Tokenizing"):
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inputs = tokenizer(text,
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max_length=512,
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truncation=True,
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padding="do_not_pad",
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return_tensors="pt").to(device)
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with m("model.eval()"):
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model.eval()
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with m("Inference"):
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with torch.no_grad():
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logits = model(**inputs).logits
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with m("Softmax"):
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probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
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with m("Output formatting"):
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output_pred = {f"[{model.config.id2label[i]}] {MANIFESTO_LABEL_NAMES[int(model.config.id2label[i])]}": probs[i] for i in np.argsort(probs)[::-1]}
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output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
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return output_pred, output_info
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def predict_cap(text, language):
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