Create app.py

app.py

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+import pickle
+import numpy as np
+import pandas as pd
+from PIL import Image, ImageOps
+
+model = pickle.load(open("catboost.pkl", "rb"))
+
+def classify_image(image):
+  image = Image.fromarray(image)
+  labels = ['Brain Tumor Present', 'No Brain Tumor']
+  image = image.resize((120, 120))
+  image = ImageOps.grayscale(image)
+  image = np.array(image).reshape((1, -1))
+  res = {labels[0]:float(model.predict_proba(image)[0][1]), labels[1]: float(model.predict_proba(image)[0][0])}
+  if model.predict_proba(image)[0][0] < 0.5:
+      pred = "The MRI image contains a Brain Tumor"
+      symptoms = "Possible Symptoms : New or increasingly strong headaches, blurred vision, loss of balance, confusion and seizures (In some cases, there may be no symptoms as well)"
+  
+  else:
+      pred = "The MRI image does not have a Brain Tumor"
+      symptoms = "Possible Symptoms : None"
+  return pred, res, symptoms
+  
+  
+label1 = gr.outputs.Label(label="Prediction")
+label2 = gr.outputs.Label(label="Confidence Score")
+label3 = gr.outputs.Label(label="Symptoms")
+image = gr.inputs.Image()
+
+interface = gr.Interface(title = "Brain Tumor Classifier",
+             description="This an Online tool representing AI for a good cause, this online AI powered web application is built by Rauhan Ahmed Siddiqui, using this tool, one could know whether his/her brain MRI report contains a tumor or not with great accuracy, no matter how difficult it is to see that from a human eye.",
+             fn=classify_image,
+             inputs=image,
+             outputs=[label1, label2, label3],
+             examples=[["1 no.jpeg"],["3 no.jpeg"],["Y4.jpeg"],["Y6.jpeg"]],
+             interpretation=None,
+             layout="unaligned",
+             theme='dark-grass')
+ 
+interface.launch()