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More fixes to HF HOME cache
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main.py
CHANGED
@@ -6,22 +6,26 @@ from pydantic import BaseModel
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import os
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app = FastAPI()
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os.
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model_path = hf_hub_download(repo_id="MienOlle/sentiment_analysis_api",
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filename="sentimentAnalysis.pth",
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cache_dir="
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)
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modelToken = token.from_pretrained("mdhugol/indonesia-bert-sentiment-classification")
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model = modelSC.from_pretrained("mdhugol/indonesia-bert-sentiment-classification", num_labels=3)
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model.eval()
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class TextInput(BaseModel):
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text: str
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def predict(input):
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inputs = modelToken(input, return_tensors="pt", padding=True, truncation=True, max_length=512)
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with torch.no_grad():
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outputs = model(**inputs)
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import os
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app = FastAPI()
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os.environ["HF_HOME"] = "/tmp/huggingface"
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os.makedirs(os.environ["HF_HOME"], exist_ok=True)
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model_path = hf_hub_download(repo_id="MienOlle/sentiment_analysis_api",
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filename="sentimentAnalysis.pth",
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cache_dir=os.environ["HF_HOME"]
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)
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modelToken = token.from_pretrained("mdhugol/indonesia-bert-sentiment-classification")
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model = modelSC.from_pretrained("mdhugol/indonesia-bert-sentiment-classification", num_labels=3)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model.load_state_dict(torch.load(model_path, map_location=torch.device(device)))
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model.to(device)
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model.eval()
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class TextInput(BaseModel):
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text: str
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def predict(input):
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inputs = modelToken(input, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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