Updated app

app.py

@@ -54,44 +54,38 @@ if page == 'Text Generation' and data == 'Oscar':
     st.header('Tamil text generation with GPT2')
     st.markdown('A simple demo using gpt-2-tamil model trained on Oscar dataset!')
     model, tokenizer = load_model(config[data])
-    # Set default options
-    prompt = st.selectbox('Examples', prompts, index=len(prompts) - 1)
-    if prompt == "Custom":
-        prompt_box = ""
-    else:
-        prompt_box = prompt
-    text = st.text_input(
-        'Add your custom text in Tamil',
-        "",
-        max_chars=1000)
-    max_len = st.slider('Length of the sentence to generate', 25, 300, 100)
-    gen_bt = st.button('Generate')
 elif page == 'Text Generation' and data == "Oscar + Indic Corpus":
     st.header('Tamil text generation with GPT2')
     st.markdown('A simple demo using gpt-2-tamil model trained on Oscar + IndicNLP dataset')
     model, tokenizer = load_model(config[data])
+else:
+    st.title('Tamil News classification with Finetuned GPT2')
+    st.markdown('In progress')
+
+if page == "Text Generation":
     # Set default options
-    prompt = st.selectbox('Examples', prompts, index=len(prompts) - 1)
+    prompt = st.selectbox('Examples', prompts, index=0)
     if prompt == "Custom":
-        prompt_box = ""
-    else:
-        prompt_box = prompt
-    text = st.text_input(
+        prompt_box = "",
+        text = st.text_input(
         'Add your custom text in Tamil',
         "",
         max_chars=1000)
-    max_len = st.slider('Length of the sentence', 5, 300, 100)
+    else:
+        prompt_box = prompt
+        text = st.text_input(
+        'Selected example in Tamil',
+        prompt,
+        max_chars=1000)
+    max_len = st.slider('Select length of the sentence to generate', 25, 300, 100)
     gen_bt = st.button('Generate')
-else:
-    st.title('Tamil News classification with Finetuned GPT2')
-    st.markdown('In progress')
 
-# Generate text
-if gen_bt:
-        try:
-            with st.spinner('Generating...'):
-                generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
-                seqs = generator(prompt_box, max_length=max_len)[0]['generated_text']
-            st.write(seqs)
-        except Exception as e:
-            st.exception(f'Exception: {e}')
+    # Generate text
+    if gen_bt:
+            try:
+                with st.spinner('Generating...'):
+                    generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+                    seqs = generator(prompt_box, max_length=max_len)[0]['generated_text']
+                st.write(seqs)
+            except Exception as e:
+                st.exception(f'Exception: {e}')

config.json

@@ -1,9 +1,9 @@
 {
     "models": ["Text Generation", "Text Classification"],
-    "Text Generation": ["Oscar", "Oscar + Indic Corpus"],
+    "Text Generation": ["Oscar", "Oscar + IndicNLP"],
     "Text Classification": ["News Data"],
     "Oscar": "flax-community/gpt-2-tamil",
-    "Oscar + Indic Corpus": "abinayam/gpt-2-tamil",
+    "Oscar + IndicNLP": "abinayam/gpt-2-tamil",
     "examples": ["ஒரு ஊரிலே ஒரு காக்கைக்கு",
                 "அன்பிர்க்கும் உன்டோ அடைக்கும்",
                 "தெனாலி ராமன், ஒரு பெரிய விகடகவி"]