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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
from tensorflow.keras.applications.mobilenet_v3 import preprocess_input
import numpy as np
model = from_pretrained_keras("yusyel/fishv2")
CLASS=["Black Sea Sprat",
"Gilt-Head Bream",
"Hourse Mackerel",
"Red Mullet",
"Red Sea Bream",
"Sea Bass",
"Shrimp",
"Striped Red Mullet",
"Trout"]
def preprocess_image(img):
img = load_img(img, target_size=(224, 224, 3))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)
print(img.shape)
return img
def predict(img):
img = preprocess_image(img)
pred = model.predict(img)
pred = np.squeeze(pred).astype(float)
print(pred)
return dict(zip(CLASS, 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(server_name="0.0.0.0", server_port=7860)
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