model and streamlit app push 1

nlpproject/app.py

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+import streamlit as st
+import numpy as np 
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+#@st.cache(allow_output_mutation=True)
+@st.cache_resource
+def get_model():
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    model = BertForSequenceClassification.from_pretrained("PankajNk/toxichub")
+    return tokenizer,model
+
+tokenizer,model = get_model()
+
+user_input = st.text_area('Enter Test to be Analyze')
+button = st.button("Analyze")
+
+
+d ={
+    1:'Toxic',
+    0:'Non Toxic'
+}
+
+
+if user_input and button:
+    test_sample = tokenizer([user_input], padding=True, truncation=True, max_length=512,return_tensors='pt')
+    outputs = model(**test_sample)
+    #predication = torch.nn.functional.softmax(outputs.logits, dim = 1)
+    st.write("logits: ", outputs.logits)
+    y_predication = np.argmax(outputs.logits.detach().numpy(), axis =1)
+    st.write("Predication",d[y_predication[0]])

nlpproject/requirements.txt

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+streamlit
+torch
+transformers