app.py

import gradio as gr
import torch
from transformers import pipeline

# Load your model pipeline (e.g., text generation or similar tasks)
model = pipeline("text-generation", model="black-forest-labs/FLUX.1-schnell")

# Define a function that includes parameters
def generate_text(prompt, seed, num_inference_steps):
    # Set the random seed for reproducibility
    generator = torch.manual_seed(seed)
    
    # Generate text with additional parameters
    output = model(prompt, num_return_sequences=1, max_length=256, 
                   do_sample=True, generator=generator)
    return output[0]['generated_text']

# Create a Gradio interface
interface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Input Prompt"),
        gr.Number(label="Seed", default=0),
        gr.Number(label="Inference Steps", default=50)
    ],
    outputs=gr.Textbox(label="Generated Text")
)

# Launch the interface
interface.launch()