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Tumor_Detection
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from PIL import Image, ImageOps
import numpy as np
from collections import OrderedDict
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from keras.models import load_model
import gradio as gr


def create_plot(data):
  sns.set_theme(style="whitegrid")

  f, ax = plt.subplots(figsize=(5, 5))

  sns.set_color_codes("pastel")
  sns.barplot(x="Total", y="Labels", data=data,label="Total", color="b")

  sns.set_color_codes("muted")
  sns.barplot(x="Confidence Score", y="Labels", data=data,label="Conficence Score", color="b")

  ax.legend(ncol=2, loc="lower right", frameon=True)
  sns.despine(left=True, bottom=True)
  return f


def predict_tumor(img):
  np.set_printoptions(suppress=True)
  model = load_model('keras_model.h5', compile=False)
  class_names = open('labels.txt', 'r').readlines()
  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
  
  # image = Image.open(img).convert('RGB')
  image = img
  size = (224, 224)
  image_PIL = Image.fromarray(image)
  image = ImageOps.fit(image_PIL, size, Image.LANCZOS)
  image_array = np.asarray(image)
  normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
  data[0] = normalized_image_array
  prediction = model.predict(data)
  index = np.argmax(prediction)
  class_name = class_names[index]
  confidence_score = prediction[0][index]
  
  c_name = (class_name[2:])[:-1]
  if c_name == "Yes":
    tumor_prediction = "Model detected Tumor"
    other_class = "No"
  else:
    other_class = "Yes"
    tumor_prediction = "Model did not detect Tumor"
 
  res = {"Labels":[c_name,other_class], "Confidence Score":[(confidence_score*100),(1-confidence_score)*100],"Total":100}
  data_for_plot = pd.DataFrame.from_dict(res)

  tumor_conf_plt = create_plot(data_for_plot)
  return tumor_prediction,tumor_conf_plt

css = """
footer {display:none !important}
.output-markdown{display:none !important}
footer {visibility: hidden}
.hover\:bg-orange-50:hover {
    --tw-bg-opacity: 1 !important;
    background-color: rgb(229,225,255) !important;
}

img.gr-sample-image:hover, video.gr-sample-video:hover {
    --tw-border-opacity: 1;
    border-color: rgb(37, 56, 133) !important;
}

.gr-button-lg {
    z-index: 14;
    width: 113px;
    height: 30px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(17, 20, 45) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: none !important;
}
.gr-button-lg:hover{
    z-index: 14;
    width: 113px;
    height: 30px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(66, 133, 244) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}

"""
  
with gr.Blocks(title="Brain Tumor Detection | Data Science Dojo", css = css) as demo:
  with gr.Row():
    with gr.Column(scale=4):
      with gr.Row():
          imgInput = gr.Image()
    with gr.Column(scale=1):
      tumor = gr.Textbox(label='Presence of Tumor')
      plot = gr.Plot(label="Plot")
      
  submit_button = gr.Button(value="Submit")
  submit_button.click(fn=predict_tumor, inputs=[imgInput], outputs=[tumor,plot])

  gr.Examples(
        examples=["pred2.jpg","pred3.jpg"],
        inputs=imgInput,
        outputs=[tumor,plot],
        fn=predict_tumor,
        cache_examples=True,
    )

demo.launch()