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import tensorflow as tf | |
from tensorflow.keras.preprocessing import image | |
import matplotlib.pyplot as plt | |
from keras.models import load_model | |
def load_image(img_path, show=False): | |
img = image.load_img(img_path, target_size=(224, 224)) | |
img_tensor = image.img_to_array(img) | |
img_tensor = np.expand_dims(img_tensor, axis=0) | |
img_tensor /= 255 | |
if show: | |
plt.imshow(img_tensor[0]) | |
plt.axis('off') | |
plt.show() | |
return img_tensor | |
def run_model(img): | |
model = load_model("res.h5") | |
#img_path = '/content/Indian/9/1020.jpg' | |
#new_image = load_image(img_path) | |
#result = model.predict(new_image) | |
result = model.predict(img) | |
results = dict(zip(classes, result[0])) | |
return max(results, key = results.get) | |
title = "Indian Sign Language Classifier" | |
description = "<p style='text-align: center'>Classifies images from 0-9, A-Z made using Indian Sign Language" | |
examples = ['ex1.jpg','ex2.jpg','ex3.jpg','ex4.jpg','ex5.jpg','ex6.jpg','ex7.jpg','ex8.jpg','ex9.jpg','ex10.jpg'] | |
import gradio as gr | |
gr.Interface(fn=run_model, inputs=gr.inputs.Image(shape=(224, 224)), outputs='text', title=title, description=description, examples=examples).launch() |