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import os
import gradio as gr
from transformers import pipeline
# from diffusers import StableDiffusionPipeline
# import torch
sd_description = "ζεηζεΎη"
sd_examples = [["ε°η«"], ["cat"], ["dog"]]
sd_demo = gr.Interface.load("models/runwayml/stable-diffusion-v1-5", title='ζεηζεΎη', examples=sd_examples)
pipe = pipeline("image-classification")
examples = [[os.path.join(os.path.dirname(__file__), "lion.jpg")], [os.path.join(os.path.dirname(__file__), "cat.jpeg")]]
app = gr.Interface.from_pipeline(pipe, examples=examples, title='εΎηθ―ε«')
# model_id = "dreamlike-art/dreamlike-photoreal-2.0"
# pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
# pipe_v1 = pipe.to("cpu")
# def generate_image_v1(prompt):
# return pipe_v1(prompt).images[0]
# examples = [["θ½ζ₯"], ["ζ²ζ»©"]]
# app_v1 = gr.Interface(fn=generate_image_v1, inputs="text", outputs="image", examples=examples)
demo = gr.TabbedInterface([sd_demo, app], ["ζεηζεΎη", "εΎηθ―ε«"])
demo.queue(concurrency_count=2)
demo.launch()
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