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main.py

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+from flask import Flask, request, jsonify
+from keras.models import load_model
+import pandas as pd
+import numpy as np
+import pickle
+
+app = Flask(__name__)
+
+# load the model
+model = load_model('model.h5')
+
+# load the dataset
+data = pd.read_csv('dataset.csv')
+
+disease = pd.get_dummies(data['Disease'])
+
+with open('mlb.pkl', 'rb') as f:
+    mlb = pickle.load(f)
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+
+    symptoms_to_predict = request.json['symptoms']
+
+    symptoms_array = mlb.transform([symptoms_to_predict])
+
+    disease_prediction = model.predict(np.expand_dims(symptoms_array, axis=2))
+
+    predicted_disease_index = np.argmax(disease_prediction)
+
+    predicted_disease = disease.columns[predicted_disease_index]
+
+    predicted_antibiotics = data.loc[data['Disease'] == predicted_disease, 'Antibiotics'].values[0]
+
+    output = {
+        'disease': predicted_disease,
+        'antibiotics': predicted_antibiotics
+    }
+
+    # return the output as JSON
+    response = jsonify(output)
+    return response
+
+if __name__ == '__main__':
+    app.run(debug=False)

mlb.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:243ed1a5d8c28402ec07e767138908815c71a6d27cc52ea245f9d534a9278564
+size 3166

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:440254f8854a4b0cfb4bb6dc0d4a65b3f60efc261d5be1ca696e6564645cebcb
+size 16940424