Update app.py

app.py

@@ -1,13 +1,25 @@
 import streamlit as st
-st.title("Text Summarizer")
+st.title("TextPressoMachine")
 
 #from transformers import AutoModelForSeq2SeqLM
 from t5_model import T5
 from transformers import AutoTokenizer
 from transformers import pipeline
-model_name = "ZinebSN/T5_Summarier"
-model = T5().from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained('t5-small')
+models = {
+    "T5 Small": "ZinebSN/T5_Summarier",
+    "GPT2": "ZinebSN/GPT2_Summarier"
+}
+
+selected_model = st.radio("Select Model", list(models.keys()))
+model_name = models[selected_model]
+
+if model_name == "T5 Small":
+    model = T5().from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained('t5-small')
+else:
+    model = GPT2().from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
 input_text=st.text_area("Input the text to summarize","")
 if st.button("Summarize"):
     st.text("It may take a minute or two.")