Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+import numpy as np
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import torch
+
+@st.cache(allow_output_mutation=True)
+def get_model():
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    model = TFBertForSequenceClassification.from_pretrained("lfernandopg/Proyecto-Transformers")
+    return tokenizer,model
+
+
+tokenizer,model = get_model()
+
+user_input = st.text_area('Enter Text to Analyze')
+button = st.button("Analyze")
+
+d = {
+  0 : 'Accountant',
+  1 : 'Actuary',                    
+  2 : 'Biologist',  
+  3 : 'Chemist',
+  4 : 'Civil engineer',
+  5 : 'Computer programmer',
+  6 : 'Data scientist',
+  7 : 'Database administrator',
+  8 : 'Dentist',
+  9 : 'Economist',
+  10 : 'Environmental engineer',
+  11 : 'Financial analyst',
+  12 : 'IT manager',
+  13 : 'Mathematician',
+  14 : 'Mechanical engineer',
+  15 : 'Physician assistant',
+  16 : 'Psychologist',
+  17 : 'Statistician',
+  18 : 'Systems analyst',
+  19 : 'Technical writer ',
+  20 : 'Web developer '
+}
+
+
+if user_input and button :
+    test_sample = tokenizer([user_input], padding=True, truncation=True, max_length=512,return_tensors='pt')
+    # test_sample
+    output = model(**test_sample)
+    st.write("Logits: ",output.logits)
+    y_pred = np.argmax(output.logits.detach().numpy(),axis=1)
+    st.write("Prediction: ",d[y_pred[0]])