from diffusers import DiffusionPipeline
import gradio as gr
import sys 

generator = DiffusionPipeline.from_pretrained("kaveh/wsi_generator")

class Logger:
    def __init__(self, filename):
        self.terminal = sys.stdout
        self.log = open(filename, "w")

    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)
        
    def flush(self):
        self.terminal.flush()
        self.log.flush()
        
    def isatty(self):
        return False  
        
sys.stdout = Logger("output.log")

def read_logs():
    sys.stdout.flush()
    with open("output.log", "r") as f:
        return f.read()

  

def generate(n_samples=1, progress=gr.Progress()):
    images = []
    for i in range(n_samples):
        image = generator().images[0]
        images.append(image)
    return images
    
with gr.Blocks() as demo:
    with gr.Column(variant="panel"):
        with gr.Row(variant="compact"):
            n_s = gr.Slider(1, 4, label='Number of Samples', value=1, step=1.0, show_label=True).style(container=False)
            btn = gr.Button("Generate image").style(full_width=False)

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery").style(columns=[2], rows=[2], object_fit="contain", height="auto", preview=True)

    btn.click(generate, n_s, gallery)
    logs = gr.Textbox()
    demo.load(read_logs, None, logs, every=1)
    
demo.launch()