app.py

import streamlit as st
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the model and tokenizer from Hugging Face
model_name = "KevSun/Personality_LM"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Streamlit app
st.title("Personality Prediction App")
st.write("Enter your text below to predict personality traits:")

# Input text from user
user_input = st.text_area("Your text here:")

if st.button("Predict"):
    if user_input:
        # Tokenize input text
        inputs = tokenizer(user_input, return_tensors="pt")
        
        # Get predictions from the model
        with torch.no_grad():
            outputs = model(**inputs)
        
        # Extract the predictions
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predictions = predictions[0].tolist()
        
        # Display the predictions
        labels = ["Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism", "Openness"]
        for label, score in zip(labels, predictions):
            st.write(f"{label}: {score:.4f}")
    else:
        st.write("Please enter some text to get predictions.")