app.py

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import threading
import gradio as gr

model_name = "programordie2/trumpgpt"

model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def stream_generate(prompt):
    if not prompt:
        return
    
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)

    generation_kwargs = dict(input_ids=input_ids, max_new_tokens=50, streamer=streamer)
    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    generated_text = prompt

    for text in streamer:
        generated_text += text
        yield generated_text

# Preset prompts
example_prompts = [
    "The Fake News Media",
    "Sleepy Joe Biden",
    "MAKE AMERICA",
]

# Update input when a prompt is selected
def set_prompt(prompt):
    return gr.update(elem_id="prompt-box", value=prompt)

# Interface with custom layout
with gr.Blocks(css="""
body { font-family: 'Segoe UI', sans-serif; }
.gradio-container { max-width: 700px; margin: auto; padding: 2em; }
textarea { font-size: 1rem !important; }
#output-box { white-space: pre-wrap; border-radius: 12px; padding: 1em; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
""") as demo:
    gr.Markdown("## ✨ TrumpGPT Playground")
    gr.Markdown("TrumpGPT is a LLM based on GPT-2, trained on Donald Trump's tweets.")
    gr.Markdown("Please note this is a next word predictor, not a chatbot.")

    with gr.Column():
        prompt_box = gr.Textbox(label="Prompt", lines=1, placeholder="Type the start of a sentence", elem_id="prompt-box")

        gr.Markdown("No inspiration? Try one of these:")

        for prompt in example_prompts:
            btn = gr.Button(prompt, elem_id=f"prompt-{prompt}")
            btn.click(set_prompt, btn, prompt_box, show_progress="hidden")
    
    gr.Markdown("---")

    generate_btn = gr.Button("Generate", variant="primary", elem_id="generate-btn")
    output_box = gr.Textbox(label="Generated Text", lines=8, interactive=False, elem_id="output-box")

    generate_btn.click(stream_generate, prompt_box, output_box)

demo.launch()