app.py

import gradio as gr
from transformers import pipeline
from newspaper import Article

# Load the model
model = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")

def analyze(input_text, input_type):
    # Auto-detect if input is URL or text
    if input_type == "Auto Detect":
        if input_text.startswith("http://") or input_text.startswith("https://"):
            input_type = "URL"
        else:
            input_type = "Text"

    if input_type == "URL":
        try:
            article = Article(input_text)
            article.download()
            article.parse()
            text = article.text
        except Exception as e:
            return f"❌ Failed to extract article: {e}", 0
    else:
        text = input_text

    if not text:
        return "❌ No text provided", 0

    try:
        result = model(text)[0]
        label = result["label"]
        score = result["score"]
        verdict = "Authentic" if label == "REAL" else "Possibly Misinformation"
        authenticity_score = round(score * 100, 2)
        return verdict, authenticity_score
    except Exception as e:
        return f"❌ Model inference failed: {e}", 0

interface = gr.Interface(
    fn=analyze,
    inputs=[
        gr.Textbox(lines=6, label="Paste article text or URL here"),
        gr.Radio(["Auto Detect", "Text", "URL"], label="Input Type", value="Auto Detect")
    ],
    outputs=[
        gr.Textbox(label="Verdict"),
        gr.Number(label="Authenticity Score (%)")
    ],
    title="Misinformation Detection Dashboard",
    description="Enter article text or a URL to detect whether the content is authentic or possibly misinformation."
)

interface.launch()