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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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st.set_page_config(layout="wide") |
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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") |
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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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input_column, reference_column = st.columns(2) |
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input_column.write('# Fake News Detection AI') |
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with st.spinner("Loading Model..."): |
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model, base_model, tokenizer, data = load_model() |
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user_input = input_column.text_input("Article url") |
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submit = input_column.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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title, text = Scrap(user_input) |
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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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input_column.markdown(f"<small>Compute Finished in {int(time.time() - last_time)} seconds</small>", unsafe_allow_html=True) |
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prediction = np.argmax(result, axis=-1) |
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input_column.success(f"This news is {label[prediction]}.") |
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input_column.text(f"{int(result[prediction]*100)}% confidence") |
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input_column.progress(result[prediction]) |
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for i in sorted[:5]: |
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reference_column.write(f""" |
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<small>turnbackhoax.id</small> |
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<a href={data["url"][i]}><h5>{data["title"][i]}</h5></a> |
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""", unsafe_allow_html=True) |
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with reference_column.expander("read content"): |
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st.write(data["text"][i]) |
