some code

app.py

@@ -1,4 +1,7 @@
 import streamlit as st  #Web App
+from transformers import pipeline
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+
 
 #title
 st.title("Sentiment Analysis")
@@ -8,11 +11,17 @@ def analyze(input, model):
     return "This is a sample output"
 
 #text insert
-input = st.text_area("insert text to be analyzed", value="lorem ipsum...", height=None, max_chars=None, key=None, help=None, on_change=None, args=None, kwargs=None, placeholder=None, disabled=False, label_visibility="visible")
-model = st.selectbox("choose a transformer model", ['a', 'b', 'c'], index=0, key=None, help=None, on_change=None, args=None, kwargs=None, disabled=False, label_visibility="visible")
+input = st.text_area("insert text to be analyzed", value="Nice to see you today.", height=None, max_chars=None, key=None, help=None, on_change=None, args=None, kwargs=None, placeholder=None, disabled=False, label_visibility="visible")
+model_name = st.text_input("choose a transformer model", value="")
+
+model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
 
 if st.button('Analyze'):
-    st.write(analyze(input, model))
+    st.write(classifier(input))
 else:
     st.write('Goodbye')
 

requirements.txt

@@ -1 +1,2 @@
-streamlit
+streamlit
+transformers