Create app.py

app.py

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+from keras.models import load_model
+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
+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
+
+
+  
+with gr.Blocks() 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()