app.py

import gradio as gr
from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline

# Load the model and tokenizer
model_name = "JakeTurner616/Adonalsium-gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Create a pipeline for text generation
text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer)

# Define a function that uses the model to generate text based on the given prompt and parameters
def generate_text(prompt, max_length, temperature, top_p, repetition_penalty):
    return text_generator(
        prompt, 
        max_length=max_length, 
        temperature=temperature, 
        top_p=top_p, 
        repetition_penalty=repetition_penalty,
        num_return_sequences=1
    )[0]['generated_text']

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(lines=2, label="Input Prompt"),
        gr.Slider(minimum=10, maximum=300, step=10, value=100, label="Max Length"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Temperature"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.9, label="Top P"),
        gr.Slider(minimum=1.0, maximum=2.0, step=0.1, value=1.1, label="Repetition Penalty"),
    ],
    outputs="text",
    title="Cosmere Series Text Generator",
    description="Adjust the sliders to control text generation parameters."
)

# Launch the interface
iface.launch()