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import streamlit as st |
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import numpy as np |
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import pandas as pd |
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import re |
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import time |
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import os |
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from transformers import AutoModelForSequenceClassification, AutoModel, AutoTokenizer |
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from sklearn.metrics.pairwise import cosine_similarity |
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from datasets import load_dataset |
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from sentence_transformers import SentenceTransformer |
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from Scraper import Scrap |
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model_checkpoint = "Rifky/indobert-hoax-classification" |
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base_model_checkpoint = "indobenchmark/indobert-base-p1" |
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data_checkpoint = "Rifky/indonesian-hoax-news" |
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label = {0: "valid", 1: "fake"} |
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@st.cache(show_spinner=False, allow_output_mutation=True) |
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def load_model(): |
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model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=2) |
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base_model = SentenceTransformer(base_model_checkpoint) |
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, fast=True) |
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data = load_dataset(data_checkpoint, split="train", download_mode='force_redownload') |
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return model, base_model, tokenizer, data |
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def sigmoid(x): |
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return 1 / (1 + np.exp(-x)) |
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with st.spinner("Loading Model..."): |
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model, base_model, tokenizer, data = load_model() |
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st.markdown("""<h1 style="text-align:center;">Fake News Detection AI</h1>""", unsafe_allow_html=True) |
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user_input = st.text_input("Article URL") |
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m = st.markdown(""" |
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<style> |
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div.stButton > button:first-child { |
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margin: auto; |
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display: block; |
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width: 100%; |
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} |
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</style>""", unsafe_allow_html=True) |
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submit = st.button("submit") |
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if submit: |
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last_time = time.time() |
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with st.spinner("Reading Article..."): |
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scrap = Scrap(user_input) |
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title, text = scrap.title, scrap.text |
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if text: |
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text = re.sub(r'\n', ' ', text) |
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with st.spinner("Computing..."): |
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token = text.split() |
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text_len = len(token) |
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sequences = [] |
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for i in range(text_len // 512): |
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sequences.append(" ".join(token[i * 512: (i + 1) * 512])) |
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sequences.append(" ".join(token[text_len - (text_len % 512) : text_len])) |
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sequences = tokenizer(sequences, max_length=512, truncation=True, padding="max_length", return_tensors='pt') |
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predictions = model(**sequences)[0].detach().numpy() |
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result = [ |
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np.sum([sigmoid(i[0]) for i in predictions]) / len(predictions), |
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np.sum([sigmoid(i[1]) for i in predictions]) / len(predictions) |
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] |
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print (f'\nresult: {result}') |
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title_embeddings = base_model.encode(title) |
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similarity_score = cosine_similarity( |
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[title_embeddings], |
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data["embeddings"] |
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).flatten() |
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sorted = np.argsort(similarity_score)[::-1].tolist() |
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prediction = np.argmax(result, axis=-1) |
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if prediction == 0: |
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st.markdown(f"""<p style="background-color: rgb(254, 242, 242); |
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color: rgb(153, 27, 27); |
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font-size: 20px; |
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border-radius: 7px; |
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padding-left: 12px; |
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padding-top: 15px; |
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padding-bottom: 15px; |
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line-height: 25px; |
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text-align: center;">This article is <b>{label[prediction]}</b>.</p>""", unsafe_allow_html=True) |
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else: |
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st.markdown(f"""<p style="background-color: rgb(236, 253, 245); |
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color: rgb(6, 95, 70); |
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font-size: 20px; |
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border-radius: 7px; |
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padding-left: 12px; |
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padding-top: 15px; |
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padding-bottom: 15px; |
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line-height: 25px; |
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text-align: center;">This article is <b>{label[prediction]}</b>.</p>""", unsafe_allow_html=True) |
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with st.expander("Related Articles"): |
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for i in sorted[:5]: |
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st.markdown(f""" |
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<small style="text-align: left;">{data["url"][i].split("/")[2]}</small><br> |
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<a href={data["url"][i]} style="text-align: left;">{data["title"][i]}</a> |
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""", unsafe_allow_html=True) |
