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2021-210
2021-210
Commit 15ec85bc authored by Shalitha Deshan Jayasekara's avatar Shalitha Deshan Jayasekara 🏘
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Merge branch 'IT18116984_WeerasundaraD.A' into 'master' 

Implement Chatbot,API Cofiguration

See merge request !18
parents 35fd6065 18b07515
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.idea/2021-210.iml
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......@@ -2,7 +2,7 @@
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="inheritedJdk" />
<orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>
\ No newline at end of file
.idea/misc.xml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
<component name="PyCharmProfessionalAdvertiser">
<option name="shown" value="true" />
</component>
</project>
\ No newline at end of file
disease_prediction/.idea/.gitignore deleted 100644 → 0
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# Default ignored files
/workspace.xml
disease_prediction/.idea/disease_prediction.iml deleted 100644 → 0
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>
\ No newline at end of file
disease_prediction/.idea/inspectionProfiles/profiles_settings.xml deleted 100644 → 0
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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>
\ No newline at end of file
disease_prediction/.idea/modules.xml deleted 100644 → 0
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/disease_prediction.iml" filepath="$PROJECT_DIR$/.idea/disease_prediction.iml" />
</modules>
</component>
</project>
\ No newline at end of file
disease_prediction/.idea/vcs.xml deleted 100644 → 0
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
</component>
</project>
\ No newline at end of file
disease_prediction/API.py 0 → 100644
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import numpy
import tensorflow as tf
from flask import Flask
from flask import request
from flask_cors import CORS
from tensorflow.keras.models import load_model
import keras
import predict_disease
app = Flask(__name__)
cors = CORS(app)
@app.route("/predict", methods =['POST'])
def predict():
data = numpy.array(request.get_json())
print(data)
prediction = predict_disease.predict(data)
return {"pred": str(numpy.argmax(prediction))}
if __name__ == '__main__':
app.run(debug=True, port=5001, host="0.0.0.0")
disease_prediction/chatbot_symptom_disease_model.py 0 → 100644
View file @ 15ec85bc
import numpy
import pandas as pd
import tensorflow as tf
from keras.models import load_model
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn import metrics
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(7, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model = load_model('model.h5')
return 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(7, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model_df = pd.read_csv(datasetFilePath)
leDiseases = preprocessing.LabelEncoder()
leDiseases.fit(model_df['Diseases'])
print(leDiseases.classes_)
model_df['Diseases'] = leDiseases.transform(model_df['Diseases'])
# print(dataDf.describe())q
leBreeds = preprocessing.LabelEncoder()
leBreeds.fit(model_df['Breeds'])
model_df['Breeds'] = leBreeds.transform(model_df['Breeds'])
dataDf = model_df.fillna(0)
print(model_df.shape)
X = model_df.values[:,0:62]
y = model_df.values[:,62]
print(y)
x_train,x_test,y_train,y_test = train_test_split(X,y, stratify=y,test_size=0.2)
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))
print("History: ", history)
val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss)
print(val_acc)
model.save('model.h5')
print(history.history)
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
y_pred = model.predict_classes(x_test)
print(y_test)
print(y_pred)
cnf_matrix = metrics.confusion_matrix(y_test, y_pred)
print("===================")
print("Confusion matrix")
print("===================")
print(cnf_matrix)
return model
def predict(model, data):
return model.predict(numpy.array(data))
model = tf.keras.models.Sequential()
trainModel(model, "data.csv")
disease_prediction/data.csv
View file @ 15ec85bc
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
Age,Breeds,anorexia,high_fever,itching,pus_discharge,fever,dripping_urine,cough,vomiting,unable_to_swallowing,scratching,scratching_the_affected_ear_on_the_floor,blood_in_urine,eye_discharge,lethargy,patchy_hair_loss,rubbing_the_affected_ear_on_the_floor,strain_to_urinate,nasal_discharge,bloody_diarrhea,increased_salivation,crusty_skin_ulcers,frequently_rotating_movement,diarrhea,frequently_urination,mild_fever,drooling,skin_redness,rash,painful,pale_gums,licking_genital_area,abdominal_pain,seizures,inflammation_of_skin,hyperemic_gums,bloating,paralysis,unpleasant_odor,redness_on_the_ear,whining_when_urination,weight_loss,hypersensitivity,stiff_joints,muscle_twitching,weakness,odd_behavior,thickening_of_skin,abnormal_bleeding,dehydration,sores_on_the_abdomen,enlarged_lymph_nodes,sores_on_the_legs,increased_capillary_refill_time,difficulty_breathing,sores_on_the_ears,pain,sores_on_the_chest,sores_on_the_elbows,partial_paralysis,complete_paralysis,Diseases
2,german sheppherd,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,Parvovirus
2,german sheppherd,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,Parvovirus
2,german sheppherd,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,Parvovirus
10,mongrel,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Rabies
10,mongrel,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Rabies
10,mongrel,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Rabies
6,mongrel,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,1,1,0,0,Canine Scabies
4,labrador retriever,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,1,1,0,0,Canine Scabies
6,german sheppherd,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Otitis Media
6,german sheppherd,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Otitis Media
6,german sheppherd,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Otitis Media
3,labrador retriever,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
4,german sheppherd,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
7,mongrel,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
6,german sheppherd,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
9,mongrel,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
11,pomeranian,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
7,mongrel,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
5,german sheppherd,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
8,mongrel,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
3,mongrel,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
8,german sheppherd,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
5,pomeranian,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
6,german sheppherd,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
3,labrador retriever,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
7,german sheppherd,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
5,german sheppherd,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,Tick Fever
4,mongrel,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Urinary tract infections (UTIs)
3,mongrel,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Urinary tract infections (UTIs)
6,german sheppherd,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,Canine Distemper
4,german sheppherd,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Canine Distemper
7,mongrel,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Canine Distemper
10,pomeranian,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Canine Distemper
8,mongrel,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,Canine Distemper
8,labrador retriever,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Canine Distemper
7,pomeranian,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Canine Distemper
5,mongrel,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Canine Distemper
6,german sheppherd,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,Canine Distemper
9,mongrel,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Canine Distemper
4,mongrel,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Canine Distemper
6,pomeranian,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Canine Distemper
7,german sheppherd,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,Canine Distemper
8,mongrel,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Canine Distemper
7,pomeranian,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Canine Distemper
9,german sheppherd,1,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Canine Distemper
disease_prediction/disease_prediction_model.py
View file @ 15ec85bc
......@@ -3,6 +3,20 @@ import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn import preprocessing
dataDf = pd.read_csv("data.csv")
leDiseases = preprocessing.LabelEncoder()
leDiseases.fit(dataDf['Diseases'])
print(leDiseases.classes_)
dataDf['Diseases'] = leDiseases.transform(dataDf['Diseases'])
# print(dataDf.describe())
leBreeds = preprocessing.LabelEncoder()
leBreeds.fit(dataDf['Breeds'])
dataDf['Breeds'] = leBreeds.transform(dataDf['Breeds'])
dataDf = dataDf.fillna(0)
model = tf.keras.models.Sequential()
......@@ -10,19 +24,24 @@ def trainModel(model, datasetFilePath):
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(256, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(11, activation=tf.nn.softmax))
model.add(tf.keras.layers.Dense(7, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model_df = pd.read_csv(datasetFilePath)
print(model_df.shape)
X = model_df.values[:,0:18]
y = model_df.values[:,18]
print(y)
print(dataDf.shape)
X = dataDf[['Age','Breeds','anorexia','high_fever','itching','pus_discharge','fever','dripping_urine','cough','vomiting','unable_to_swallowing','scratching','scratching_the_affected_ear_on_the_floor','blood_in_urine','eye_discharge','lethargy','patchy_hair_loss','rubbing_the_affected_ear_on_the_floor','strain_to_urinate','nasal_discharge','bloody_diarrhea','increased_salivation','crusty_skin_ulcers','frequently_rotating_movement','diarrhea','frequently_urination','mild_fever','drooling','skin_redness','rash','painful','pale_gums','licking_genital_area','abdominal_pain','seizures','inflammation_of_skin','hyperemic_gums','bloating','paralysis','unpleasant_odor','redness_on_the_ear','whining_when_urination','weight_loss','hypersensitivity','stiff_joints','muscle_twitching','weakness','odd_behavior','thickening_of_skin','abnormal_bleeding','dehydration','sores_on_the_abdomen','enlarged_lymph_nodes','sores_on_the_legs','increased_capillary_refill_time','difficulty_breathing','sores_on_the_ears','pain','sores_on_the_chest','sores_on_the_elbows','partial_paralysis','complete_paralysis']]
y = dataDf[['Diseases']]
# print(y)
x_train,x_test,y_train,y_test = train_test_split(X,y, stratify=y,test_size=0.2)
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)
# x_train = tf.keras.utils.normalize(x_train, axis=1)
# x_test = tf.keras.utils.normalize(x_test, axis=1)
print("==================")
print(x_train)
print("==================")
print(y_train)
print("==================")
history = model.fit(x_train, y_train, epochs=50, validation_data=(x_test, y_test))
val_loss, val_acc = model.evaluate(x_test, y_test)
......
disease_prediction/pred_api.py 0 → 100644
View file @ 15ec85bc
import numpy
import tensorflow as tf
from flask import Flask
from flask import request
from flask_cors import CORS
from tensorflow.keras.models import load_model
import keras
app = Flask(__name__)
cors = CORS(app)
@app.route("/predict", methods =['POST'])
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(7, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model = load_model('model.h5')
data = numpy.array(request.get_json())
print(data)
prediction = model.predict(data)
return {"pred": str(numpy.argmax(prediction))}
if __name__ == '__main__':
app.run(debug=True, port=5001)
disease_prediction/prediction_test.py 0 → 100644
View file @ 15ec85bc
import tensorflow as tf
import numpy
from tensorflow.keras.models import load_model
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(7, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model = load_model('model.h5')
prediction = model.predict(numpy.array([[6,1,1,1,1,1,104,1,0,1,1,1,0,1,1,1,0,0,0,1,1,0,0,0,1,1,1,1,6,1,1,1,1,1,104,1,0,1,1,1,0,1,1,1,0,0,0,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1]]))
print(prediction)
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