Upload SampleQA.py

SampleQA.py

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+from os import path
+import streamlit as st
+import tensorflow as tf
+import random
+from transformers import ElectraTokenizerFast, TFElectraForQuestionAnswering
+from datasets import Dataset, DatasetDict, load_dataset
+
+model_hf = "nguyennghia0902/bestfailed_electra-small-discriminator_5e-05_16"
+tokenizer = ElectraTokenizerFast.from_pretrained(model_hf)
+reload_model = TFElectraForQuestionAnswering.from_pretrained(model_hf)
+
+@st.cache_resource
+def predict(question, context):
+    inputs = tokenizer(question, context, return_offsets_mapping=True,return_tensors="tf",max_length=512, truncation=True)
+    offset_mapping = inputs.pop("offset_mapping")
+    outputs = reload_model(**inputs)
+    answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
+    answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
+    start_char = offset_mapping[0][answer_start_index][0]
+    end_char = offset_mapping[0][answer_end_index][1]
+    predicted_answer_text = context[start_char:end_char]
+    
+    return predicted_answer_text
+
+def main():
+    st.set_page_config(page_title="Sample in Dataset", page_icon="📝")
+
+    # giving a title to our page
+    col1, col2 = st.columns([2, 1])
+    col1.title("Sample in Dataset")
+
+    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]
+
+    question = st.text_area(
+        "Sample QUESTION: ",
+        sampleQ,
+        height=15,
+    )
+    text = st.text_area(
+        "Sample CONTEXT:",
+        sampleC,
+        height=100,
+    )
+    answer = st.text_area(
+        "True ANSWER:",
+        sampleA,
+        height=20,
+    )
+
+    # Create a prediction button
+    if st.button("Predict"):
+        prediction = ""
+        stripped_text = text.strip()
+        if not stripped_text:
+            st.error("Please enter a context.")
+            return
+        stripped_question = question.strip()
+        if not stripped_question:
+            st.error("Please enter a question.")
+            return
+
+        prediction = predict(stripped_question, stripped_text)
+        st.success(prediction)
+
+if __name__ == "__main__":
+    main()