Update app.py

app.py

@@ -3,43 +3,63 @@ import pandas as pd
 import numpy as np
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
+from statsmodels.tsa.arima.model import ARIMA
 import matplotlib.pyplot as plt
-import statsmodels.api as sm
-from fbprophet import Prophet
 
-# Sample data (replace with your own usage/maintenance data)
+# Sample data (this would be replaced with real equipment 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]
+    'cost_per_hour': [10, 15, 12],
 }
 
-# Convert to DataFrame
+# Convert data to DataFrame
 df = pd.DataFrame(data)
 
-# Function to make predictions (Logistic Regression)
+# Function to perform Time Series Analysis (using ARIMA)
+def time_series_analysis(equipment_id, usage_hours):
+    # Simulate time series data (here using random data for demonstration)
+    dates = pd.date_range('2025-01-01', periods=10, freq='D')
+    usage_data = np.random.randint(100, 200, size=10)  # Dummy usage data
+    
+    # Create a DataFrame for time series data
+    ts_df = pd.DataFrame({'ds': dates, 'usage': usage_data})
+    
+    # Fit ARIMA model (for simplicity, we use ARIMA(1,1,1) here)
+    model = ARIMA(ts_df['usage'], order=(1, 1, 1))
+    model_fit = model.fit()
+
+    # Forecast the next value (predict the future usage)
+    forecast = model_fit.forecast(steps=1)
+    
+    # Return forecasted value
+    return f"Forecasted usage for the next day: {forecast[0]:.2f}"
+
+# Logistic Regression model (using usage and idle hours to predict equipment status)
 def model_prediction(equipment_id, usage_hours, idle_hours, movement_frequency, cost_per_hour):
-    # Model input
+    # Feature extraction (from usage data)
     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)
+    # Logistic Regression model (this is an example, replace with your trained model)
     model = LogisticRegression()
+    
+    # Train on dummy data (replace this with historical usage data for actual training)
     X = df[['usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']]
-    y = [0, 1, 0]  # Dummy labels (0 = Repair, 1 = Move)
+    y = [0, 1, 0]  # 0 = Repair, 1 = Move (example)
     model.fit(X, y)
-
-    # Predict the suggestion
+    
+    # Predict suggestion (0 = Repair, 1 = Move)
     prediction = model.predict(features)[0]
-
-    # Define the suggestions
+    
+    # Define suggestions
     suggestions = {0: 'Repair', 1: 'Move'}
     
-    # Get confidence score
+    # Confidence score (example)
     confidence = model.predict_proba(features)[0][prediction] * 100
     
-    # Return the result
+    # Return the suggestion and confidence
     return f"Suggestion: {suggestions[prediction]}\nConfidence: {confidence:.2f}%"
 
 # Gradio Interface