| import numpy as np | |
| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| import pandas as pd | |
| model = tf.keras.models.load_model("Oxygen Content.keras") | |
| def predict(Fuel,Air): | |
| Fuel = (Fuel*1700)/17000 | |
| Air = (Air*13000)/130000 | |
| xn = np.array([[Fuel,Air]]) | |
| yn = abs(model.predict(xn)) | |
| Percantage = np.round(yn[0,0]*100, 2) | |
| return Percantage | |
| demo = gr.Interface(fn=predict,inputs=["number", "number"],outputs=["number"], | |
| title="Oxygen Content(%) Analyzer in Flue Gas", | |
| description="Input Fuel Flowrate and Air Flowrate as per controller recorder value for example: 2.2 or 5.5 etc. This model will predict the Oxygen Content(%) in Flue Gas just like real life analyzer.", | |
| ) | |
| demo.launch(share=True) |