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Update app.py
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app.py
CHANGED
@@ -1,69 +1,41 @@
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import gradio as gr
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import numpy as np
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import
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import json
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"mobilenet_v2"
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]
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colname = "mobilenet_v2"
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radio = gr.inputs.Radio(models_name, default="mobilenet_v2", type="value", label=colname)
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print(radio.label)
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def predict_image(image, model_name):
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image = Image.fromarray(np.uint8(image)).convert('RGB')
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print(type(image))
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print(type(model_name))
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print("==========")
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print(image)
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print(model_name)
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print("======================")
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# image = np.array(image) / 255
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# image = np.expand_dims(image, axis=0)
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)
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pred =
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print(pred)
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acc = dict((labels[i], 0
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acc[pred] = 100.0
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print(acc)
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return acc
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# return pred
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# open categories.txt in read mode
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categories = open("categories.txt", "r")
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labels = categories.readline().split(";")
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image = gr.inputs.Image(shape=(
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print(image)
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label = gr.outputs.Label(num_top_classes=len(labels))
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samples = ['
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# './samples/tigre.jpg', './samples/whale.jpg', './samples/white.jpg', './samples/whitetip.jpg']
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interface = gr.Interface(
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fn=predict_image,
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inputs=
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outputs=label,
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capture_session=True,
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allow_flagging=False,
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interface.launch()
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import gradio as gr
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import numpy as np
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import pandas as pd
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from tensorflow.keras import models
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import tensorflow as tf
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# open categories.txt in read mode
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categories = open("categories.txt", "r")
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labels = categories.readline().split(";")
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model = models.load_model('models/modelnet/best_model.h5')
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def predict_image(image):
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image = np.array(image) / 255
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image = np.expand_dims(image, axis=0)
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pred = model.predict(image)
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acc = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
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print(acc)
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return acc
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image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
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label = gr.outputs.Label(num_top_classes=len(labels))
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samples = ['samples/basking.jpg', 'samples/blacktip.jpg', 'samples/blue.jpg', 'samples/bull.jpg', 'samples/hammerhead.jpg',
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'samples/lemon.jpg', 'samples/mako.jpg', 'samples/nurse.jpg', 'samples/sand tiger.jpg', 'samples/thresher.jpg',
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'samples/tigre.jpg', 'samples/whale.jpg', 'samples/white.jpg', 'samples/whitetip.jpg']
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interface = gr.Interface(
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fn=predict_image,
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inputs=image,
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outputs=label,
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capture_session=True,
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allow_flagging=False,
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examples=samples
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)
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interface.launch()
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