Update app.py

app.py

@@ -26,7 +26,7 @@ st.write("Try it yourself here 👇🏻")
 generator = pipeline("text2text-generation", model = "mrm8488/t5-small-finetuned-quora-for-paraphrasing")
 
 default_value_gen = "How can I put out grease fire?"
-sent = st.text_area("Input", placeholder = default_value_gen, height = 10)
+sent = st.text_area(label = "Input", value = default_value_gen, height = 10)
 outputs = generator(sent)
 st.write("Paraphrased Example:")
 st.write(outputs[0]["generated_text"])
@@ -41,7 +41,7 @@ st.write("Your English intent classification model will be between these two mod
 model_id = "Helsinki-NLP/opus-mt-en-fr"
 
 default_value_tr = "How are you?"
-tr_input = st.text_input("Input in English", placeholder = default_value_tr, key = "translation")
+tr_input = st.text_area(label = "Input in English", value = default_value_tr, height = 5)
 outputs = query(tr_input, model_id, api_token)
 st.write("Translated Example:")
 st.write(outputs[0]["translation_text"])
@@ -52,8 +52,8 @@ st.subheader("Easy Information Retrieval")
 st.write("If you're making a chatbot that needs to provide information to user, you can take user's query and search for the answer in the documents you have, using question answering models. Look at the example and try it yourself here 👇🏻")
 
 qa_model = pipeline("question-answering")
-question = st.text_area("Question", placeholder = "What does transformers do?", height = 5)
-context = st.text_area("Context", placeholder = "🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.")
+question = st.text_area(label = "Question", value = "What does transformers do?", height = 5)
+context = st.text_area(label = "Context", value = "🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.")
 output_answer = qa_model(question = question, context = context)
 st.write("Answer:")
 st.write(output_answer["answer"])