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import cv2
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import gradio as gr
def recog(img):
model = load_model('hack36_2.h5')
img_array = np.asarray(img)
clone = img_array.copy()
clone_resized = cv2.resize(clone, (64,64))
img_array=clone_resized/255
img_final = np.expand_dims(img_array, axis=0)
prediction = model.predict(img_final).tolist()[0]
alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'space', 'space', 'space']
return {alphabet[i]: prediction[i] for i in range(29)}
title = "ASL Fingerspelling Recognition"
desc = ""
input = gr.inputs.Image(type="pil", source="webcam")
# output = gr.outputs.HTML(label="")
output = gr.outputs.Label(num_top_classes=5)
# output = "text"
examples = [
["B_test.jpg"],
["C_test.jpg"],
["Y_test.jpg"]
]
iface = gr.Interface(
fn=recog,
title=title,
description=desc,
examples=examples,
inputs=input,
outputs=output
)
iface.launch() |