app.py

import numpy as np
import joblib
from tensorflow.keras.models import load_model
import gradio as gr

# Load the saved scaler and model
scaler = joblib.load('scaler.joblib')
model = load_model('rainfall_prediction_model.h5')

def predict_rainfall(Dew_Point, Pressure, Gust_Speed, RH, Wind_Direction,
                     Wind_Speed, Temperature, Rained, Water_Content, Solar_Radiation):
    # Preprocess the input data
    input_data = np.array([[Dew_Point, Pressure, Gust_Speed, RH, Wind_Direction,
                            Wind_Speed, Temperature, Rained, Water_Content, Solar_Radiation]])
    input_data_scaled = scaler.transform(input_data)
    input_data_scaled = input_data_scaled.reshape((input_data_scaled.shape[0], 1, input_data_scaled.shape[1]))
    
    # Make a prediction
    prediction = model.predict(input_data_scaled)
    
    # Output the prediction
    return 'Rain' if prediction[0][0] > 0 else 'No Rain'

# Gradio Interface
inputs = [
    gr.Number(label="Dew Point"),
    gr.Number(label="Pressure"),
    gr.Number(label="Gust Speed"),
    gr.Number(label="Relative Humidity"),
    gr.Number(label="Wind Direction"),
    gr.Number(label="Wind Speed"),
    gr.Number(label="Temperature"),
    gr.Number(label="Rained"),
    gr.Number(label="Water Content"),
    gr.Number(label="Solar Radiation")
]
output = gr.Textbox(label="Prediction")

gr.Interface(fn=predict_rainfall, inputs=inputs, outputs=output, title="Rainfall Prediction").launch()