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# app.py
# Install necessary libraries (This line is for local setup; Gradio Spaces should have dependencies set in the config)
# !pip install transformers datasets scikit-learn accelerate gradio
# Importing necessary libraries
import gradio as gr
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
# Load the dataset
ds = load_dataset("GonzaloA/fake_news")
# Load pre-trained tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('TeamQuad-fine-tuned-bert')
# Create a classification pipeline
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
# Define label mapping for fake news detection
label_mapping = {0: 'fake', 1: 'true'}
# Function to classify input text
def classify_news(text):
result = classifier(text)
label = result[0]['label'].split('_')[1] # Extract label from the model's output
score = result[0]['score'] # Confidence score
mapped_result = {'label': label_mapping[int(label)], 'score': score}
return f"Label: {mapped_result['label']}, Score: {mapped_result['score']:.4f}"
# Create a Gradio interface
iface = gr.Interface(
fn=classify_news, # The function to process the input
inputs=gr.Textbox(lines=10, placeholder="Enter a news headline or article to classify..."),
outputs="text", # Output will be displayed as text
title="Fake News Detection",
description="Enter a news headline or article and see whether the model classifies it as 'Fake News' or 'True News'."
)
# Launch the interface
if __name__ == "__main__":
iface.launch(share=True)
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