app_file.py

import flask
app = Flask(__name__)


@app.route('/')
def hello_world():
    return 'Hello World!'

@app.route('/index8050')
def index8050():
    return flask.render_template('index8050.html')


@app.route('/predict', methods=['POST'])

def predict():
    try:
        step_dim=160
    
        max_num = 127
        min_num = -128
        meta_data=pd.read_csv("df_metadata_sample.csv")
        signal_data=pq.read_pandas('sample_signal_data.parquet').to_pandas()
        if (signal_data[(signal_data > 127)].any()).any()==True:
            return jsonify("Error!, Amplitude value is too high")
        elif (signal_data[(signal_data < -128)].any()).any()==True:
            return jsonify("Error!, Amplitude value is too low")
        elif (signal_data.isnull().values.any())==True:
            return jsonify("Error!, Amplitude value is missing")
      
        else:
            
            clf = load_model('model.h5',
                     custom_objects={'Attention': Attention(step_dim),
                                     'GlorotUniform':glorot_uniform
                                     ,'LSTMCell':LSTMCell,
                                  'matthews_correlation':matthews_correlation})
            x_test=data_processing(meta_data,signal_data)
            pred = clf.predict(x_test)
            prediction=[]
            for index,i in enumerate(pred): 
                if i<0.5:
                    prediction.append("Partial discharge is not there for the  signal")
                else:
                    prediction.append("Partial discharge is  there for the signal")
            return jsonify({'prediction': prediction})
    except :
        return jsonify("File name is wrong.Try after proper file renameing")
    
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8050)

ok