Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+from fbprophet import Prophet
+
+# Sample data (replace with your own usage/maintenance data)
+data = {
+    'equipment_id': ['Excavator', 'Crane', 'Tractor'],
+    'usage_hours': [120, 140, 100],
+    'idle_hours': [30, 20, 50],
+    'movement_frequency': [5, 7, 3],
+    'cost_per_hour': [10, 15, 12]
+}
+
+# Convert to DataFrame
+df = pd.DataFrame(data)
+
+# Function to make predictions (Logistic Regression)
+def model_prediction(equipment_id, usage_hours, idle_hours, movement_frequency, cost_per_hour):
+    # Model input
+    features = np.array([usage_hours, idle_hours, movement_frequency, cost_per_hour]).reshape(1, -1)
+
+    # Train a simple logistic regression model (replace with your own trained model)
+    model = LogisticRegression()
+    X = df[['usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']]
+    y = [0, 1, 0]  # Dummy labels (0 = Repair, 1 = Move)
+    model.fit(X, y)
+
+    # Predict the suggestion
+    prediction = model.predict(features)[0]
+
+    # Define the suggestions
+    suggestions = {0: 'Repair', 1: 'Move'}
+    
+    # Get confidence score
+    confidence = model.predict_proba(features)[0][prediction] * 100
+    
+    # Return the result
+    return f"Suggestion: {suggestions[prediction]}\nConfidence: {confidence:.2f}%"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=model_prediction,
+    inputs=[
+        gr.Dropdown(choices=df['equipment_id'].tolist(), label="Select Equipment"),
+        gr.Number(label="Usage Hours"),
+        gr.Number(label="Idle Hours"),
+        gr.Number(label="Movement Frequency"),
+        gr.Number(label="Cost per Hour")
+    ],
+    outputs="text"
+)
+
+# Launch Gradio interface
+interface.launch()