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import gradio as gr
import numpy as np
import pandas as pd
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
# Load the model and tokenizer from the folder
model_path = "bert_model"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Create the pipeline
clf = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Define function for fake news detection
def classify_fake_news(text):
prediction = clf.predict(text)[0]["score"]
# Convert prediction to label
label = "Fake" if prediction < 0.7 else "Real"
return label
# Define Gradio interface
iface = gr.Interface(
fn=classify_fake_news,
inputs="text",
outputs="label",
title="BERT & CatBoost Fake News Detection",
description="Paste a news or tweet to check if it's fake or real."
)
# Launch the Gradio interface
iface.launch(share=True)
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