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import gradio as gr
from fastai.vision.all import *
from efficientnet_pytorch import EfficientNet 


title = "COVID_19 Infection Detectation App!"
head = (
    "<center>"
      "<img src='Gradcam.png' width=400>"
      "This Space demonstrates model based on efficientnet base model. I has been trained to classify chest xray image."
    "</center>"
    "<body>"
    
        "<h2>"
            "This Space demonstrates a model based on efficientnet base model"
        "</h2>"
        "<p>"
            "I has been trained to classify chest xray image. To test it, Use the Example Images Provided or Upload your own xray images the space provided." 
        "</p>"
        "<p>"
          "The model is trained using [anasmohammedtahir/covidqu(https://www.kaggle.com/datasets/anasmohammedtahir/covidqu) dataset"
        "</p>"
    
    "</body>"
)
description = head

examples = [
    ['covid/covid_1038.png'], ['covid/covid_1034.png'], 
    ['covid/cd.png'], ['covid/covid_1021.png'], 
    ['covid/covid_1027.png'], ['covid/covid_1042.png'], 
    ['covid/covid_1031.png']
]

#learn = load_learner('model/predictcovidfastaifinal18102023.pkl')
learn = load_learner('model/final_20102023_eb7_model.pkl')

categories = learn.dls.vocab

def predict_image(get_image):
   pred, idx, probs = learn.predict(get_image)
   return dict(zip(categories, map(float, probs)))

interpretation="default"
enable_queue=True

gr.Interface(fn=predict_image, inputs=gr.Image(shape=(224,224)),
             outputs = gr.Label(num_top_classes=3),title=title,description=description,examples=examples, interpretation=interpretation,enable_queue=enable_queue).launch(share=False)