app.py

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

# Load model and tokenizer
model_path = "mjpsm/confidence-classifier-updated"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
id2label = model.config.id2label

# Prediction function
def classify_confidence(statement):
    inputs = tokenizer(statement, return_tensors="pt", truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    probs = F.softmax(logits, dim=1)
    pred_id = torch.argmax(probs, dim=1).item()
    label = id2label[pred_id]
    confidence_score = probs[0][pred_id].item()
    return f"🧠 Prediction: {label} ({confidence_score:.2%} confidence)"

# Create Gradio interface
iface = gr.Interface(
    fn=classify_confidence,
    inputs=gr.Textbox(lines=4, placeholder="Enter a statement..."),
    outputs="text",
    title="🧠 Confidence Statement Classifier",
    description="Enter a statement to classify its level of confidence using a fine-tuned AI model."
)

if __name__ == "__main__":
    iface.launch()