Upload 3 files

QuestionAnswering.py

@@ -0,0 +1,75 @@
+from os import path
+import streamlit as st
+import tensorflow as tf
+from transformers import ElectraTokenizerFast, TFElectraForQuestionAnswering
+
+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="Question Answering", page_icon="📝")
+
+    # giving a title to our page
+    col1, col2 = st.columns([2, 1])
+    col1.title("Question Answering")
+
+    col2.link_button("Explore my model", "https://huggingface.co/nguyennghia0902/electra-small-discriminator_5e-05_32")
+
+    question = st.text_area(
+        "QUESTION: Please enter a question:",
+        placeholder="Enter your question here",
+        height=15,
+    )
+    text = st.text_area(
+        "CONTEXT: Please enter a context:",
+        placeholder="Enter your context here",
+        height=100,
+    )
+
+    prediction = ""
+
+    upload_file = st.file_uploader("CONTEXT: Or upload a file with some contexts", type=["txt"])
+    if upload_file is not None:
+        text = upload_file.read().decode("utf-8")
+
+        for line in text.splitlines():
+            line = line.strip()
+            if not line:
+                continue
+
+            prediction = predict(question, line)
+
+            st.success(line + "\n\n" + prediction)
+
+    
+    # Create a prediction button
+    elif 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()

app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+from st_pages import Page, show_pages
+
+st.set_page_config(page_title="Question Answering", page_icon="🏠")
+
+show_pages(
+    [
+        Page("app.py", "Home", "🏠"),
+        Page(
+            "QuestionAnswering.py", "Question Answering", "📝"
+        ),
+    ]
+)
+
+st.title("Project in Text Mining and Application")
+st.header("Question Answering use a pre-trained model - ELECTRA")
+st.markdown(
+    """
+    **Team members:**
+    | Student ID | Full Name                | Email                          |
+    | ---------- | ------------------------ | ------------------------------ |
+    | 1712603    | Lê Quang Nam             | 1712603@student.hcmus.edu.vn   |
+    | 19120582   | Lê Nhựt Minh             | 19120582@student.hcmus.edu.vn  |
+    | 19120600   | Bùi Nguyên Nghĩa         | 19120600@student.hcmus.edu.vn  |
+    | 21120198   | Nguyễn Thị Lan Anh       | 21120198@student.hcmus.edu.vn  |
+    """
+)
+
+st.header("The Need for Question Answering")
+st.markdown(
+    """
+    ...
+    """
+)
+
+st.header("Technology used")
+st.markdown(
+    """
+    The ELECTRA model, specifically the "google/electra-small-discriminator" used here, 
+    is a deep learning model in the field of natural language processing (NLP) developed 
+    by Google. This model is an intelligent variation of the supervised learning model 
+    based on the Transformer architecture, designed to understand and process natural language efficiently.
+    For this Question Answering task, we choose two related classes: ElectraTokenizerFast and 
+    TFElectraForQuestionAnswering to implement.
+    """
+)

requirements.txt

@@ -0,0 +1,7 @@
+transformers
+numpy
+pandas
+tensorflow
+streamlit
+st-pages
+tf-keras