Create app.py

app.py

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+import streamlit as st
+import transformers
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+st.title("Text Classification with BERT")
+
+text = st.text_input("Enter some text")
+if text:
+    encoded_text = tokenizer.encode_plus(text, max_length=128, padding="max_length", truncation=True, return_tensors="pt")
+    logits = model(encoded_text["input_ids"], attention_mask=encoded_text["attention_mask"]).logits
+    pred = logits.argmax().item()
+    st.write("Prediction:", pred)
+    
+#st.text_input('First name')
+
+#classifier = pipeline("text-classification", model=model)
+#classifier('st.text_input')
+
+#st.text('Fixed width text')
+
+#x = st.slider('Select a value')
+#st.write(x, 'squared is', x * x)