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Create app.py
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app.py
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import streamlit as st
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import torch
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from transformers import BertConfig, BertForSequenceClassification, BertTokenizer
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import numpy as np
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import requests
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from io import BytesIO
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# Load the model and tokenizer
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def load_model():
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=7)
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model_state_dict = torch.load('sentiment7_model_acc8878.pth', map_location=torch.device('cpu') # cpu ์ฌ์ฉ
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model.load_state_dict(model_state_dict)
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model.eval()
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return model, tokenizer
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model, tokenizer = load_model()
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# Define the inference function
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def inference(input_doc):
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inputs = tokenizer(input_doc, return_tensors='pt')
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outputs = model(**inputs)
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probs = torch.softmax(outputs.logits, dim=1).squeeze().tolist()
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class_idx = {'๊ณตํฌ': 0, '๋๋': 1, '๋ถ๋
ธ': 2, '์ฌํ': 3, '์ค๋ฆฝ': 4, 'ํ๋ณต': 5, 'ํ์ค': 6}
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return {class_name: prob for class_name, prob in zip(class_idx, probs)}
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# Set up the Streamlit interface
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st.title('Sentiment Analysis with BERT')
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user_input = st.text_area("Enter text here:")
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if st.button('Analyze'):
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result = inference(user_input)
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st.write(result)
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