updating sidebar

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 # from huggingface_hub import snapshot_download
 
-page = st.sidebar.selectbox("Model ", ["Pretrained GPT2", "Finetuned on News data"])
+page = st.sidebar.selectbox("Model ", ["Finetuned on News data", "Pretrained GPT2"])
 
 def load_model(model_name):
     with st.spinner('Waiting for the model to load.....'):
@@ -15,21 +15,16 @@ def load_model(model_name):
 seed = st.sidebar.text_input('Starting text', 'ආයුබෝවන්')
 seq_num = st.sidebar.number_input('Number of sentences to generate ', 1, 20, 5)
 max_len = st.sidebar.number_input('Length of the sentence ', 5, 300, 100)
+gen_bt = st.sidebar.button('Generate')
 
-if page == "Finetuned on News data":
-    
-    st.title('Sinhala Text generation with Finetuned GPT2')
-    st.markdown('This model has been finetuned Sinhala-gpt2 model with 6000 news articles(~12MB)')
-    
-    # seed = st.text_input('Starting text', 'ආයුබෝවන්')
-    # seq_num = st.number_input('Number of sentences to generate ', 1, 20, 5)
-    # max_len = st.number_input('Length of the sentence ', 5, 300, 100)
+if page == 'Pretrained GPT2':
+    st.title('Sinhala Text generation with GPT2')
+    st.markdown('A simple demo using Sinhala-gpt2 model trained during hf-flax week')
 
-    gen_news = st.button('Generate')
-    model, tokenizer = load_model('keshan/sinhala-gpt2-newswire')
+    model, tokenizer = load_model('flax-community/Sinhala-gpt2')
 
 
-    if gen_news:
+    if gen_bt:
         try:
             with st.spinner('Generating...'):
                 generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
@@ -38,14 +33,14 @@ if page == "Finetuned on News data":
         except Exception as e:
             st.exception(f'Exception: {e}')
 else:
-    st.title('Sinhala Text generation with GPT2')
-    st.markdown('A simple demo using Sinhala-gpt2 model trained during hf-flax week')
-
-    gen_gpt2 = st.button('Generate')
-    model, tokenizer = load_model('flax-community/Sinhala-gpt2')
+    
+    st.title('Sinhala Text generation with Finetuned GPT2')
+    st.markdown('This model has been finetuned Sinhala-gpt2 model with 6000 news articles(~12MB)')
+    
+    model, tokenizer = load_model('keshan/sinhala-gpt2-newswire')
 
 
-    if gen_gpt2:
+    if gen_bt:
         try:
             with st.spinner('Generating...'):
                 generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
@@ -53,7 +48,7 @@ else:
             st.write(seqs)
         except Exception as e:
             st.exception(f'Exception: {e}')
-            
+           
 
 st.markdown('____________')
 st.markdown('by Keshan with Flax Community')