app.py

import gradio as gr
import torch
import json
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# -------------------------
# Load model + tokenizer
# -------------------------
model_dir = "./argument-scheme-classifier-json"

tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(model_dir)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# -------------------------
# Load Label Mapping
# -------------------------
with open("./label2scheme.json", "r", encoding="utf-8") as f:
    label2scheme = json.load(f)

# -------------------------
# Prediction Function
# -------------------------
def classify_argument(argument_text):
    if not argument_text.strip():
        return "Please enter an argument."

    inputs = tokenizer(argument_text, return_tensors="pt", truncation=True, max_length=128).to(device)

    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=-1).cpu().numpy()[0]

    # Sort from highest to lowest confidence
    results = [
        (label2scheme[str(i)], float(probs[i]))
        for i in range(len(probs))
    ]
    results.sort(key=lambda x: x[1], reverse=True)

    # Format nicely
    top_scheme, top_conf = results[0]
    output_text = f"🎯 Predicted Scheme: **{top_scheme}** (confidence {top_conf:.4f})\n\n"
    output_text += "---\n📊 Full Probability Distribution:\n"

    for scheme, p in results:
        output_text += f"- {scheme}: **{p:.4f}**\n"

    return output_text

# -------------------------
# Gradio interface
# -------------------------
title = "🧩 Argument Scheme Classifier"
description = "Enter an argument and see confidence scores for all possible argument schemes."

iface = gr.Interface(
    fn=classify_argument,
    inputs=gr.Textbox(lines=4, placeholder="Type your argument here..."),
    outputs=gr.Markdown(label="Model Prediction"),
    title=title,
    description=description,
)

iface.launch()