app.py

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

# Load the tokenizer and model
model_name = "roberta-large"  # Replace with your trained model if uploaded
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Define the prediction function
def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=-1).tolist()[0]
    labels = ["Speculating War Outcomes", "Discrediting Ukraine", "Praise of Russia"]  # Replace with your actual labels
    predictions = {label: prob for label, prob in zip(labels, probabilities)}
    return predictions

# Create the Gradio interface
demo = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(lines=3, placeholder="Enter text to classify..."),
    outputs=gr.Label(num_top_classes=3),
    title="Narrative Classification",
    description="Classify text into predefined narrative categories."
)

# Launch the app
demo.launch()