Main commit

app.py

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+import numpy as np
+import os
+import keras
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from keras.models import Sequential
+from PIL import Image
+from keras.layers import Conv2D, Flatten, Dense, Dropout, BatchNormalization, MaxPooling2D
+from sklearn.preprocessing import OneHotEncoder
+import pickle
+import gradio as gr
+
+def load_model():
+    save_path = 'model.pkl'
+    with open(save_path, 'rb') as file:
+        model = pickle.load(file)
+    return model
+
+def predict_dementia(images, model):
+    predictions = []
+    for image in images:
+        img = Image.fromarray(image.astype('uint8'))
+        img = img.resize((128, 128))
+        img = np.array(img)
+        img = img.reshape(1, 128, 128, 3)
+        
+        prediction = model.predict(img)
+        prediction_class = np.argmax(prediction)
+        predictions.append(names(prediction_class))
+    return predictions
+
+def names(number):
+    if number == 0:
+        return 'Non Demented'
+    elif number == 1:
+        return 'Mild Dementia'
+    elif number == 2:
+        return 'Moderate Dementia'
+    elif number == 3:
+        return 'Very Mild Dementia'
+    else:
+        return 'Error in Prediction'
+
+def main(images):
+    model = load_model()
+    predictions = predict_dementia(images, model)
+    return predictions
+
+iface = gr.Interface(fn=main,
+                     inputs="image",
+                     outputs="text",
+                     title="Dementia Classification",
+                     description="Classify dementia based on brain images",
+                     examples=[["Non(1).jpg"],["Moderate.jpg"],["Mild.jpg"]])
+
+iface.launch(debug =True)

requirements.txt

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+gradio
+tensorflow
+numpy
+pandas
+matplotlib
+scikit-learn
+seaborn