HChandeepa/PulmoSense_AI

1from keras.models import load_model2 3 4def do_primary_prediction(spectrogram):5    loaded_model = load_model('binary_model.h5')6    print("model loaded")7    result = loaded_model.predict(spectrogram, batch_size=1)8    print("result", result[0][0])9    if result[0][0] > 0.5:10        return False11    else:12        return True13 14 15def do_secondary_prediction(spectrogram):16    loaded_model = load_model('disease_catego_model.h5')17    print("secondary model loaded")18    result = loaded_model.predict(spectrogram)19    print("secondary result", result)20    # Sort the array in descending order21    sorted_numbers = sorted(result[0], reverse=True)22    # Get the top 3 values23    top3_values = sorted_numbers[:3]24    # Get the indexes of the top 3 values25    indexes_of_top3_values = sorted(range(len(result[0])), key=lambda i: result[0][i], reverse=True)[:3]26    print("sorted_numbers", top3_values)27    print("indexes of top 3 results", indexes_of_top3_values)28    disease_list = ['Asthma', 'Bronchiectasis', 'Bronchiolitis', "Bronchitis", 'COPD', 'Lung Fibrosis',29                    'Pleural Effusion', 'Pneumonia', 'URTI']30    predicted_disease = {}31    diseases = []32    for i in range(3):33        print(i)34        disease = disease_list[indexes_of_top3_values[i]]35        diseases.insert(i, disease)36        # print("disease", disease)37        # predicted_disease[disease] = top3_values[i]38    print("diseases", diseases)39    predicted_disease['diseases'] = diseases40    predicted_disease['probabilities'] = top3_values41    print("predicted diseases",predicted_disease)42    return predicted_disease43