Create streamlit_app.py / pages/Information_Retrieval.py

streamlit_app.py / pages/Information_Retrieval.py

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+from os import path
+import streamlit as st
+
+import nltk, subprocess, sys
+
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+install("pyvi")
+
+stwfilename = "Vstopword_new.txt"
+punfilename = "punctuation.txt"
+STW_PATH = path.join(path.dirname(__file__), stwfilename)
+PUNCT_PATH = path.join(path.dirname(__file__), punfilename)
+
+
+from pyvi import ViTokenizer
+@st.cache_resource
+def open2list_vn(path):
+        if path:
+            with open(path) as f:
+                line = list(f.read().splitlines())
+            return line
+def pre_progress(input):
+    stw = open2list_vn(STW_PATH)
+    punctuations = open2list_vn(PUNCT_PATH)
+    textU = ViTokenizer.tokenize(input)
+    text = textU.lower()
+    tokens = []
+    all_tokens = []
+    raw = nltk.wordpunct_tokenize(text)
+    for token in raw:
+        if token not in punctuations:
+          tokens.append(token)
+    for i in range(len(tokens)):
+      if tokens[i] not in stw:
+        all_tokens.append(tokens[i])
+    return " ".join(all_tokens)
+
+
+# from tensorflow import keras
+import tensorflow as tf
+from transformers import ElectraTokenizer, TFElectraForSequenceClassification
+
+MODEL_NAME = "google/electra-small-discriminator"
+MODEL_PATH = 'nguyennghia0902/textming_proj01_electra'
+
+tokenizer = ElectraTokenizer.from_pretrained(MODEL_NAME)
+
+id2label = {0: "FALSE", 1: "TRUE"}
+label2id = {"FALSE": 0, "TRUE": 1}
+loaded_model = TFElectraForSequenceClassification.from_pretrained(MODEL_PATH, id2label=id2label, label2id=label2id)
+
+def predict(question, text):
+    combined = pre_progress(question + ' ' + text)
+
+    inputs = tokenizer(combined, truncation=True, padding=True, return_tensors='tf')
+    logits = loaded_model(**inputs).logits
+    predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
+    
+    return loaded_model.config.id2label[predicted_class_id]
+
+
+def main():
+    st.set_page_config(page_title="Information Retrieval", page_icon="📝")
+
+    # giving a title to our page
+    st.title("Information Retrieval")
+    text = st.text_area(
+        "Please enter a text:",
+        placeholder="Enter your text here",
+        height=200,
+    )
+    question = st.text_area(
+        "Please enter a question:",
+        placeholder="Enter your question here",
+        height=200,
+    )
+
+    prediction = ""
+
+    # Create a prediction button
+    if st.button("Predict"):
+        stripped = text.strip()
+        if not stripped:
+            st.error("Please enter some text.")
+            return
+        stripped = question.strip()
+        if not stripped:
+            st.error("Please enter a question.")
+            return
+        text = text.replace("\n", "")
+        prediction = predict(question, text)
+        if prediction == "TRUE":
+            st.success("TRUE 😄")
+        else:
+            st.warning("FALSE 😟")
+
+if __name__ == "__main__":
+    main()