app.py

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

model_name = "riskyhomo/Ayn_Rand_BB"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Function to generate responses using the model
def generate_response(user_input):
    # Tokenize the user input
    inputs = tokenizer(user_input, return_tensors="pt")
    # Generate response from the model
    with torch.no_grad():
        outputs = model.generate(inputs.input_ids, max_length=500, pad_token_id=tokenizer.eos_token_id)
    # Decode the response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Set up the Gradio interface
iface = gr.Interface(
    fn=generate_response,  # Function to generate responses
    inputs="text",         # Input type is text
    outputs="text",        # Output type is text
    title="Chatbot",       # Title of the interface
    description="A chatbot trained with a language model.",  # Description
    theme="default"        # Gradio theme, can be "default", "dark", or "light"
)

# Launch the app
if __name__ == "__main__":
    iface.launch()