app.py

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_pneumonia(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 == "Normal":
    pneumonia_prediction = "Chest XRay is normal no signs of pneumonia"
    other_class = "Pneumonia"
  else:
    other_class = "Pneumonia"
    pneumonia_prediction = "Chest XRay shows signs of pneumonia"
 
  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)

  pneumonia_conf_plt = create_plot(data_for_plot)
  return pneumonia_prediction,pneumonia_conf_plt


  
with gr.Blocks() as demo:
  with gr.Row():
    with gr.Column(scale=4):
      with gr.Row():
          imgInput = gr.Image()
    with gr.Column(scale=1):
      pneumonia = gr.Textbox(label='Presence of pneumonia')
      plot = gr.Plot(label="Plot")
      
  submit_button = gr.Button(value="Submit")
  submit_button.click(fn=predict_pneumonia, inputs=[imgInput], outputs=[pneumonia,plot])

  gr.Examples(
        examples=["normal_Sample.jpg","pneumonia_sample.jpg"],
        inputs=imgInput,
        outputs=[pneumonia,plot],
        fn=predict_pneumonia,
        cache_examples=True,
    )

demo.launch()