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Update app.py
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app.py
CHANGED
@@ -1,18 +1,13 @@
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import gradio as gr
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import numpy as np
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from tensorflow.keras.models import load_model
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import cv2
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def image_predict (
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model_path = 'resnet_ct.h5'
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h5_model = load_model(model_path)
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#OLD IMAGE
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image = cv2.imread(image_pt)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image = cv2.resize(image, (224, 224))
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image = np.array(image) / 255
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image = np.expand_dims(image, axis=0)
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h5_prediction = h5_model.predict(image)
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@@ -20,20 +15,16 @@ def image_predict (image_pt):
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print(h5_prediction)
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probability = h5_prediction[0]
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print("H5 Predictions:")
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probability = 0
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if probability[0] > 0.5:
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covid_chest_pred = str('%.2f' % (probability[0] * 100) + '% COVID-Positive')
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status = 'Covid-Positive'
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probability = (probability[0] * 100)
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else:
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covid_chest_pred = str('%.2f' % ((1 - probability[0]) * 100) + '% COVID-Negative')
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status = 'Covid-Negative'
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probability = ((1 - probability[0]) * 100)
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return {'status': str(status), 'probability': str(probability) }
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myApp = gr.Interface(fn=image_predict, inputs="image", outputs="
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myApp.launch()#share=True
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import os
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import gradio as gr
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import numpy as np
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from tensorflow.keras.models import load_model
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import cv2
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def image_predict (image):
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model_path = 'resnet_ct.h5'
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h5_model = load_model(model_path)
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image = np.array(image) / 255
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image = np.expand_dims(image, axis=0)
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h5_prediction = h5_model.predict(image)
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print(h5_prediction)
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probability = h5_prediction[0]
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print("H5 Predictions:")
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print (probability)
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if probability[0] > 0.5:
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covid_chest_pred = str('%.2f' % (probability[0] * 100) + '% COVID-Positive')
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probability = (probability[0] * 100)
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else:
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covid_chest_pred = str('%.2f' % ((1 - probability[0]) * 100) + '% COVID-Negative')
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probability = ((1 - probability[0]) * 100)
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return covid_chest_pred
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myApp = gr.Interface(fn=image_predict, inputs="image", outputs="text")
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myApp.launch()#share=True
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