stream.py

import streamlit as st
from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load the model and tokenizer from Hugging Face
@st.cache_resource(allow_output_mutation=True)
def load_model():
    model = BertForSequenceClassification.from_pretrained("your-huggingface-username/your-model-repo")
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    return model, tokenizer

model, tokenizer = load_model()

# Define Streamlit app layout
st.title("AI vs Human Text Classifier")

user_input = st.text_area("Enter the text to classify:")

if st.button("Classify"):
    # Preprocess the input text
    inputs = tokenizer(user_input, return_tensors="pt", max_length=256, padding="max_length", truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get prediction
    prediction = torch.argmax(outputs.logits, dim=1).item()

    # Convert prediction to human-readable label
    label_mapping = {0: "Human-written", 1: "AI-generated"}
    st.write(f"The text is classified as: {label_mapping[prediction]}")