from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr
import torch
import json

title = "AI ChatBot"
description = "A State-of-the-Art Large-scale Pretrained Response generation model (GEMMA)"
examples = [["How are you?"]]

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Load courses data from JSON file
with open("uts_courses.json", "r") as f:
    courses_data = json.load(f)

# Define the predict function as before

def main():
    # Load courses data from JSON file
    with open("uts_courses.json", "r") as f:
        courses_data = json.load(f)
    print("Contents of uts_courses.json:")
    print(courses_data)
    print()

if __name__ == "__main__":
    main()

gr.Interface(
    fn=predict,
    title=title,
    description=description,
    examples=examples,
    inputs=["text"],
    outputs=["text", "state"],
    theme="finlaymacklon/boxy_violet"
).launch()