app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load models
model_1_path = "model1"
model_2_path = "model2"

tokenizer_1 = AutoTokenizer.from_pretrained(model_1_path)
model_1 = AutoModelForSequenceClassification.from_pretrained(model_1_path)

tokenizer_2 = AutoTokenizer.from_pretrained(model_2_path)
model_2 = AutoModelForSequenceClassification.from_pretrained(model_2_path)

def predict(text, model_choice):
    if model_choice == "Model 1":
        tokenizer = tokenizer_1
        model = model_1
    else:
        tokenizer = tokenizer_2
        model = model_2

    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1)[0]
        return {
            "Real": float(probs[0]),
            "Fake": float(probs[1])
        }

iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(lines=4, label="Enter News Text"),
        gr.Radio(["Model 1", "Model 2"], label="Choose Model")
    ],
    outputs=gr.Label(num_top_classes=2),
    title="Fake News Detector - Multi Model"
)

iface.launch()