Create app.py

app.py

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+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()