Add application file

app.py

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+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+import gradio as gr
+import numpy as np
+
+# Generate random sensor data
+np.random.seed(42)
+data = pd.DataFrame({
+    "Temperature": np.random.randint(0, 100, 100),
+    "Humidity": np.random.randint(0, 100, 100),
+    "Pressure": np.random.randint(0, 100, 100),
+    "Vibration": np.random.randint(0, 100, 100),
+    "Alert": np.random.randint(0, 2, 100)
+})
+
+# Split the data into features and target
+X = data.drop(columns=["Alert"])
+y = data["Alert"]
+
+# Scale the features using the StandardScaler
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Train a random forest classifier on the scaled data
+clf = RandomForestClassifier(random_state=42)
+clf.fit(X_scaled, y)
+
+# Define the input and output components for Gradio
+input_components = [
+    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Temperature"),
+    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Humidity"),
+    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Pressure"),
+    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Vibration"),
+]
+
+output_component = gr.outputs.Textbox(label="Alert")
+
+# Define the predict function to make the prediction using the model
+def predict(temp, humidity, pressure, vibration):
+    input_data = [[temp, humidity, pressure, vibration]]
+    input_data_scaled = scaler.transform(input_data)
+    prediction = clf.predict(input_data_scaled)[0]
+    return "Alert!" if prediction == 1 else "No alert"
+
+# Create the Gradio interface and run the app
+interface = gr.Interface(predict, inputs=input_components, outputs=output_component, title="5G IoT Alert System")
+interface.launch()