app.py

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

# ✅ Load model & tokenizer
model_name = "microsoft/deberta-v3-base"  # Change if needed
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=16)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)


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

# ✅ Define prediction function
def predict_mbti(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=256)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    predictions = torch.argmax(outputs.logits, dim=1).cpu().item()
    
    # Mapping predicted labels back to MBTI types
    mbti_types = [
        "INFJ", "ENTP", "INTP", "INTJ", "ENTJ", "ENFJ", "INFP", "ENFP",
        "ISFP", "ISTP", "ISFJ", "ISTJ", "ESTP", "ESFP", "ESTJ", "ESFJ"
    ]
    
    return mbti_types[predictions]

# ✅ Create Gradio UI
interface = gr.Interface(
    fn=predict_mbti,
    inputs=gr.Textbox(lines=3, placeholder="Enter a text to predict MBTI type"),
    outputs="text",
    title="MBTI Personality Predictor",
    description="Enter a text and get the predicted MBTI personality type."
)

# ✅ Launch app
if __name__ == "__main__":
    interface.launch()