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import gradio as gr | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import PIL | |
import tensorflow as tf | |
from tensorflow import keras | |
from tensorflow.keras import layers | |
from tensorflow.keras.models import Sequential | |
from keras.models import load_model | |
model1 = load_model('model1.h5') | |
class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] | |
def predict_image(img): | |
img_4d=img.reshape(-1,180,180,3) | |
prediction=model1.predict(img_4d)[0] | |
return {class_names[i]: float(prediction[i]) for i in range(5)} | |
image = gr.inputs.Image(shape=(180,180)) | |
label = gr.outputs.Label(num_top_classes=3) | |
enable_queue=True | |
description="This is a Flower Classification Model made using a CNN.Deployed to Hugging Faces using Gradio." | |
examples = ['dandelion.jpg','sunflower.jpeg','tulip.jpg'] | |
article="<p style='text-align: center'>Made by Aditya Narendra with π€</p>" | |
gr.Interface(fn=predict_image, inputs=image, outputs=label,title="Flower Classifier",description=description,article=article,examples=examples,enable_queue=enable_queue,interpretation='default').launch(debug='True') |