File size: 1,416 Bytes
64c9146 d06635d 64c9146 |
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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import gradio as gr
from huggingface_hub import from_pretrained_keras
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing import image
import numpy as np
model = from_pretrained_keras("yusyel/fishv2")
class_names = [
"Black Sea Sprat",
"Gilt-Head Bream",
"Hourse Mackerel",
"Red Sea Bream",
"Red Mullet",
"Sea Bass",
"Shrimp",
"Striped Red Mullet",
"Trout",
]
def preprocess_image(img, label):
img = load_img(img, target_size=(199, 199))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img /= 255.0
print(img.shape)
return img, label
def predict(img):
img, _ = preprocess_image(img, 1)
pred = model.predict(img)
pred = np.squeeze(pred).astype(float)
print(pred)
return dict(zip(class_names, pred))
demo = gr.Interface(
fn=predict,
inputs=[gr.inputs.Image(type="filepath")],
outputs=gr.outputs.Label(),
examples=[
["./img/Black_Sea_Sprat.png"],
["./img/Gilt_Head_Bream.JPG"],
["./img/Horse_Mackerel.png"],
["./img/Red_mullet.png"],
["./img/Red_Sea_Bream.JPG"],
["./img/Sea_Bass.JPG"],
["./img/Shrimp.png"],
["./img/Striped_Red_Mullet.png"],
["./img/Trout.png"],
],
title="fish classification",
)
demo.launch()
|