app.py

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr
import matplotlib.pyplot as plt

model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

def predict_fake_news(text):
    input_ids = tokenizer.encode(text, return_tensors='pt')
    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    fake_confidence = 1 if "fake" in generated_text.lower() else 0
    real_confidence = 1 - fake_confidence
    
    fig, ax = plt.subplots()
    ax.bar(["Real", "Fake"], [real_confidence, fake_confidence], color=['blue', 'red'])
    plt.ylim(0, 1)
    plt.xticks(rotation=45)
    plt.title("Prediction Confidence")
    
    return fig

input_text = gr.Textbox(lines=7, label="Paste the news article here", placeholder="Example: Scientists have discovered a new cure for cancer.")
output_graph = gr.Image(label="Prediction Confidence")

examples = [["New study shows coffee may prevent heart disease.", "Real"],
            ["Aliens have landed in New York City!", "Fake"],
            ["Global warming effects becoming more severe.", "Real"]]

gr.Interface(predict_fake_news, inputs=input_text, outputs=output_graph, title="Real/Fake News Detector", theme="soft", examples=examples).launch()