app.py

import gradio as gr
import torch

# Load your trained model + tokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "theguywhosucks/Instruct-MochaCofeeeee"  # replace with your uploaded model repo
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_length=200,
        temperature=0.7,
        do_sample=True,
        top_k=50
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## ✨ Custom Text Generator")
    with gr.Row():
        starter_prompt = gr.Textbox(label="Starter Prompt", placeholder="Type your prompt here...")
    with gr.Row():
        generate_btn = gr.Button("Generate Text")
    with gr.Row():
        output_box = gr.Textbox(label="Generated Result")

    generate_btn.click(fn=generate_text, inputs=starter_prompt, outputs=output_box)

demo.launch()