File size: 1,298 Bytes
e8746a9 80af36d e781192 80af36d b18e629 e781192 80af36d e8746a9 5353b15 e8746a9 2a867e9 e8746a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
from fastai.vision.all import *
import gradio as gr
#import torch
#from diffusers import FluxPipeline
#from huggingface_hub import login
#login()
#pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
#pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
#prompt = "A cat holding a sign that says hello world"
#image = pipe(
# prompt,
# height=1024,
# width=1024,
# guidance_scale=3.5,
# num_inference_steps=50,
#max_sequence_length=512,
# generator=torch.Generator("cpu").manual_seed(0)
#).images[0]
#image.save("flux-dev.png")
learn = load_learner('export.pkl')
categories = ('balsamroot', 'bladderpod', 'blazing star', 'bristlecone pine flowers', 'brittlebrush')
def classify_image(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
image=gr.Image(height = 192, width = 192)
label = gr.Label()
examples = ['https://www.deserthorizonnursery.com/wp-content/uploads/2024/03/Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg','https://cdn.mos.cms.futurecdn.net/VJE7gSuQ9KWbkqEsWgX5zS.jpg']
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)
|