app.py

import gradio as gr
import random
import time

# thresholds for soil moisture (in %)
PRIMARY_THRESHOLD = 30  # Below this, irrigation is necessary
SECONDARY_THRESHOLD = 20  # Below this, irrigation is critical

def calculate_irrigation_runtime(soil_moisture):
    """Calculate irrigation runtime based on soil moisture."""
    if soil_moisture < SECONDARY_THRESHOLD:
        return 10  # Critical moisture, longer irrigation
    elif soil_moisture < PRIMARY_THRESHOLD:
        return 7  # Moderate moisture, shorter irrigation
    else:
        return 0  # No irrigation needed

def irrigation_decision(soil_moisture, rainfall_prediction):
    if soil_moisture < SECONDARY_THRESHOLD:
        if rainfall_prediction == "Yes":
            return "Irrigation ON 🌧️🚿 (Critical soil moisture)"
        else:
            return "Irrigation ON 🚿 (Critical soil moisture)"
    elif soil_moisture < PRIMARY_THRESHOLD:
        if rainfall_prediction == "Yes":
            return "Irrigation OFF ⛔ (Rainfall expected, conserving water)"
        else:
            return "Irrigation ON 🚿 (Low soil moisture)"
    else:
        return "Irrigation OFF ⛔ (Soil moisture sufficient)"

def simulate_values_and_runtime():
    # Show loading message for data collection
    yield "Collecting data from IoT device... 🌐📡", "", "", "", ""

    time.sleep(2)  # Simulate delay for fetching data

    # Simulate IoT data
    simulated_moisture = random.uniform(10, 50)
    rainfall_prediction = random.choice(["Yes", "No"])
    decision = irrigation_decision(simulated_moisture, rainfall_prediction)

    # Display simulated data and initial decision
    yield "", f"{simulated_moisture:.2f}", rainfall_prediction, decision, ""

    # Simulate irrigation runtime if irrigation is ON
    if "Irrigation ON" in decision:
        runtime = calculate_irrigation_runtime(simulated_moisture)
        if runtime > 0:
            for minute in range(1, runtime + 1):
                time.sleep(1)  # Simulate a minute as 1 second for demonstration
                status = f"Irrigation running... ⏳ ({minute}/{runtime} minutes)"
                yield "", f"{simulated_moisture:.2f}", rainfall_prediction, decision, status

            # Turn off irrigation after the duration
            yield "", f"{simulated_moisture:.2f}", rainfall_prediction, decision, "Irrigation OFF ⛔ (Completed runtime)"
    else:
        yield "", f"{simulated_moisture:.2f}", rainfall_prediction, decision, "Irrigation remains OFF ⛔"

def app(soil_moisture, rainfall_prediction):
    decision = irrigation_decision(float(soil_moisture), rainfall_prediction)
    return decision

# Gradio Interface
with gr.Blocks(title="Smart Irrigation System 🌱💧") as demo:
    gr.Markdown("# Smart Irrigation System 🌱💧")
    gr.Markdown(
        """
        This app helps farmers optimize water usage by predicting irrigation needs based on soil moisture and rainfall forecast for Dinajpur.
        """
    )

    with gr.Row():
        soil_moisture_input = gr.Slider(
            minimum=0,
            maximum=100,
            step=1,
            label="🌡️ Soil Moisture (%)",
            value=30
        )
        rainfall_prediction_input = gr.Radio(
            ["Yes", "No"], label="🌧️ Rainfall Prediction for Dinajpur", value="No"
        )

    decision_output = gr.Textbox(label="💡 Irrigation Decision")

    simulate_button = gr.Button("Simulate IoT Data & Runtime 🌐⏳")
    collecting_data_output = gr.Textbox(label="Status")
    simulate_output_moisture = gr.Textbox(label="Simulated Soil Moisture (%)")
    simulate_output_rainfall = gr.Textbox(label="Simulated Rainfall Prediction")
    simulate_decision_output = gr.Textbox(label="Simulated Irrigation Decision")
    irrigation_runtime_output = gr.Textbox(label="Irrigation Runtime Status")

    with gr.Row():
        submit_button = gr.Button("Submit 🚀")

    # Function connections
    submit_button.click(
        app, inputs=[soil_moisture_input, rainfall_prediction_input], outputs=[decision_output]
    )

    simulate_button.click(
        simulate_values_and_runtime,
        inputs=[],
        outputs=[
            collecting_data_output,
            simulate_output_moisture,
            simulate_output_rainfall,
            simulate_decision_output,
            irrigation_runtime_output
        ]
    )

# Launch the app
demo.launch()