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from keras.models import load_model |
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import cv2 |
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import json |
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import gradio as gr |
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model_result=load_model("fyp.h5",compile=True) |
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f=open("fyp file.json") |
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data=json.load(f) |
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Tumor_Classes=list(data) |
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def Tumor_Prediction(image): |
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image=cv2.resize(image,(32,32))/255.0 |
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result=model_result.predict(image.reshape(1,32,32,3)).argmax() |
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return Tumor_Classes[result],data[Tumor_Classes[result]]['Description'],data[Tumor_Classes[result]]['Causes'],data[Tumor_Classes[result]]['Symptoms'],data[Tumor_Classes[result]]['Treatment'] |
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interface=gr.Interface(fn=Tumor_Prediction, |
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inputs="image", |
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outputs=[gr.components.Textbox(label="Tumor Name"),gr.components.Textbox(label="Description"),gr.components.Textbox(label="Causes"),gr.components.Textbox(label="Symptoms"),gr.components.Textbox(label="Treatment")], |
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enable_queu=True) |
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interface.launch(debug=True) |
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