app.py

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)