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)