app.py

import gradio as gr
import torch
import re
from transformers import BertTokenizer, BertForSequenceClassification
# import nltk
# from nltk.tokenize import word_tokenize
# from nltk.corpus import stopwords
# from nltk.stem import WordNetLemmatizer

# Download required NLTK data
# nltk.download("stopwords")
# nltk.download("punkt")
# nltk.download("wordnet")

# Load model and tokenizer
model_name = "./model"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# NLP tools
# stop_words = set(stopwords.words("english"))
# lemmatizer = WordNetLemmatizer()

# MBTI Labels
MBTI_CLASSES = [
    "ISTJ", "ISFJ", "INFJ", "INTJ",
    "ISTP", "ISFP", "INFP", "INTP",
    "ESTP", "ESFP", "ENFP", "ENTP",
    "ESTJ", "ESFJ", "ENFJ", "ENTJ"
]

# Preprocess text
def preprocess_text(text):
    text = text.lower()
    text = re.sub(r"http\S+|www.\S+", "", text)
    text = re.sub(r"[^a-zA-Z\s]", "", text)
    # tokens = word_tokenize(text)
    # tokens = [lemmatizer.lemmatize(word) for word in tokens]
    # return " ".join(tokens)

# Inference function
def predict_mbti(text):
    cleaned = preprocess_text(text)
    inputs = tokenizer(
        cleaned,
        max_length=512,
        padding="max_length",
        truncation=True,
        return_tensors="pt"
    ).to(device)

    with torch.no_grad():
        outputs = model(**inputs)
        pred_idx = torch.argmax(outputs.logits, dim=1).item()
        return MBTI_CLASSES[pred_idx]

# Gradio interface
interface = gr.Interface(
    fn=predict_mbti,
    inputs=gr.Textbox(lines=12, label="Enter Combined Answers (Q1 A1 Q2 A2 ...)"),
    outputs=gr.Textbox(label="Predicted MBTI Type"),
    title="MBTI Personality Predictor (BERT)",
    description="Paste your combined answers to get your MBTI personality type. Powered by Sid26Roy/mbti"
)

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