Create app.py

app.py

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