Update SampleQA.py

SampleQA.py

@@ -5,7 +5,7 @@ import random
 from transformers import ElectraTokenizerFast, TFElectraForQuestionAnswering
 from datasets import Dataset, DatasetDict, load_dataset
 
-model_hf = "nguyennghia0902/bestfailed_electra-small-discriminator_5e-05_16"
+model_hf = "nguyennghia0902/electra-small-discriminator_0.0005_32"
 tokenizer = ElectraTokenizerFast.from_pretrained(model_hf)
 reload_model = TFElectraForQuestionAnswering.from_pretrained(model_hf)
 
@@ -31,33 +31,30 @@ def main():
 
     new_data = load_dataset("nguyennghia0902/project02_textming_dataset", data_files={'train': 'raw_newformat_data/traindata-00000-of-00001.arrow', 'test': 'raw_newformat_data/testdata-00000-of-00001.arrow'})
     
-    sampleQ = ""
-    sampleC = ""
-    sampleA = ""
-    if st.button("Sample"):
-        sample = random.choice(new_data['test'])
-        sampleQ = sample['question']
-        sampleC = sample['context']
-        sampleA = sample['answers']["text"][0]
+    sample = random.choice(new_data['test'])
+    sampleQ = sample['question']
+    sampleC = sample['context']
+    sampleA = sample['answers']["text"][0]
 
-    question = st.text_area(
-        "Sample QUESTION: ",
-        sampleQ,
-        height=15,
-    )
     text = st.text_area(
         "Sample CONTEXT:",
         sampleC,
-        height=100,
+        height=200,
     )
+    question = st.text_area(
+        "Sample QUESTION: ",
+        sampleQ,
+        height=5,
+    )
+    
     answer = st.text_area(
         "True ANSWER:",
         sampleA,
-        height=20,
+        height=5,
     )
 
     # Create a prediction button
-    if st.button("Predict"):
+    if st.button("Sample & Predict"):
         prediction = ""
         prediction = predict(question, text)
         st.success(prediction)