Merge branch 'IT18116984_WeerasundaraD.A' into 'master'

Ann model changes

See merge request !24

disease_prediction/chatbot_symptom_disease_model.py

@@ -10,8 +10,9 @@ from sklearn import preprocessing
 def loadModel(model):
     model = tf.keras.models.Sequential()
     model.add(tf.keras.layers.Flatten())
-    model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
-    model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
     model.add(tf.keras.layers.Dense(7, activation=tf.nn.softmax))
 
     model.compile(optimizer='adam',
@@ -23,8 +24,9 @@ def loadModel(model):
 def trainModel(model, datasetFilePath):
     model = tf.keras.models.Sequential()
     model.add(tf.keras.layers.Flatten())
-    model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
-    model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
     model.add(tf.keras.layers.Dense(7, activation=tf.nn.softmax))
 
     model.compile(optimizer='adam',
@@ -39,6 +41,7 @@ def trainModel(model, datasetFilePath):
 
     leBreeds = preprocessing.LabelEncoder()
     leBreeds.fit(model_df['Breeds'])
+    print(leBreeds.classes_)
     model_df['Breeds'] = leBreeds.transform(model_df['Breeds'])
     dataDf = model_df.fillna(0)
     print(model_df.shape)
@@ -49,7 +52,7 @@ def trainModel(model, datasetFilePath):
 
     x_train = tf.keras.utils.normalize(x_train, axis=1)
     x_test = tf.keras.utils.normalize(x_test, axis=1)
-    history = model.fit(x_train, y_train, epochs=15, validation_data=(x_test, y_test))
+    history = model.fit(x_train, y_train, epochs=75, validation_data=(x_test, y_test))
     print("History: ", history)
 
     val_loss, val_acc = model.evaluate(x_test, y_test)

disease_prediction/data_pld.csv

+age,symptom1,symptom2,symptom3,symptom4,symptom5,symptom6,symptom7,symptom8,symptom9,symptom10,symptom11,symptom12,symptom13,symptom14,symptom15,symptom16,symptom17,disease
+1,1,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,3
+1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,3
+1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,3
+1,1,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0
+1,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0
+1,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0
+1,0,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0
+1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0
+4,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,0,0
+4,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,0
+4,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,0
+4,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0
+4,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1
+4,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1
+4,1,1,1,1,1,0,1,1,1,0,1,0,0,1,1,1,0,1
+4,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1
+6,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1
+6,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,1,1,1
+6,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1
+6,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1
+6,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,2
+6,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,2
+6,1,1,1,1,1,0,1,1,1,1,1,1,0,0,1,0,1,2
+6,0,1,1,1,1,0,0,0,1,0,0,1,1,0,1,0,0,2
+6,1,1,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1,2
+6,1,1,1,1,1,1,1,1,0,1,0,1,0,1,0,0,1,2
+6,1,1,1,1,1,1,0,1,1,1,1,1,0,0,1,1,1,3
+1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,3
+1,1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,1,0,3
+1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,3
+1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,3
+1,1,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0
+1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0
+1,1,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0
+1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
+1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0
+1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+1,1,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0
+1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0
+4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0
+4,0,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,0
+4,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0
+4,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0
+4,1,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,0,2
+4,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,2
+4,0,0,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,2
+4,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,2
+4,1,1,0,1,1,1,1,1,1,1,1,0,1,1,1,1,1,2
+4,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,2
+4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,2
\ No newline at end of file

disease_prediction/disease_prediction_model.py

@@ -5,6 +5,7 @@ from sklearn.model_selection import train_test_split
 import matplotlib.pyplot as plt
 from sklearn import preprocessing
 
+# load the dataset using pandas library
 dataDf = pd.read_csv("data.csv")
 
 leDiseases = preprocessing.LabelEncoder()

disease_prediction/pred_api.py

@@ -15,8 +15,9 @@ def predict():
     model = tf.keras.models.Sequential()
     model = tf.keras.models.Sequential()
     model.add(tf.keras.layers.Flatten())
-    model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
-    model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
+    model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
     model.add(tf.keras.layers.Dense(7, activation=tf.nn.softmax))
 
     model.compile(optimizer='adam',
@@ -29,4 +30,4 @@ def predict():
     return {"pred": str(numpy.argmax(prediction))}
 
 if __name__ == '__main__':
-    app.run(debug=True, port=5001)
+    app.run(debug=True, port=5001, host="0.0.0.0")

disease_prediction/predict_disease.py

@@ -17,10 +17,17 @@ model.compile(optimizer='adam',
               metrics=['accuracy'])
 model = load_model('model.h5')
 
+pred = model.predict(numpy.array([[2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]]))
+
 def predict(data):
-    return model.predict(numpy.array(data))
+    print(numpy.array(data))
+    pred = model.predict(numpy.array([[2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]]))
+    print(pred)
+    return pred
 
 # Test prediction
-print(predict([[1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0]]))
-print(numpy.argmax(predict([[1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0]])))
-print(disease_labels[numpy.argmax(predict([[1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0]]))])
+# input = [2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]
+# print(len(input))
+# print(predict([[2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]]))
+# print(numpy.argmax(predict([[1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0]])))
+# print(disease_labels[numpy.argmax(predict([[1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0]]))])