app.py

import gradio as gr
import os
os.system('pip install transformers')
from transformers import AutoTokenizer, AutoModelWithLMHead

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("gpt-model-article")

def greet(prompt,temperature,top_k,top_p,max_len):
    
    # Generate text using the fine-tuned model
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    # Generate the output
    output = model.generate(
        input_ids,
        max_length=max_len,
        do_sample=True,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p
    )

    return str(tokenizer.decode(output[0], skip_special_tokens=True))

iface = gr.Interface(fn=greet, inputs=["text",gr.Slider(0, 1, 0.8),gr.Slider(0, 1, 0),gr.Slider(0, 1, 0.9),gr.Slider(0, 250, 100)], outputs="text")
iface.launch()