import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the uncensoredgpt model and tokenizer
model_name = "gpt2"  # Replace with the actual uncensoredgpt model name if available
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def generate_text(prompt, max_length=100):
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
    outputs="text",
    title="UncensoredGPT",
    description="A simple interface for the uncensoredgpt model."
)

# Launch the interface
iface.launch()