app.py

import gradio as gr
from transformers import pipeline

# Initialize the text generation pipeline
pipe = pipeline("text-generation", model="oMarquess/trained-2k10-v4-model-merged", trust_remote_code=True)

# Define the function to generate text based on user input
def generate_text(input_text):
    generated_text = pipe(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
    return generated_text

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(label="Input Text"),
    outputs=gr.Textbox(label="Generated Text"),
    layout="vertical",
    title="Text Generation App",
    description="Generate text using a pretrained model.",
)

# Start the Gradio app
if __name__ == "__main__":
    iface.launch()