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src/model/carbon_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7d31c566476d3170aec7638427eb9476965f267c5177b3a3a1dadfc447389622
+size 29192433

src/model/model_columns.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03cf25e6d6aa9528091630a69cf9741790601f7b72b53852100e6e8e5b6fe019
+size 303

src/model/predictor.py

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+import joblib
+import numpy as np
+import os
+
+# Load model and columns
+model_path = os.path.join("src", "model", "carbon_model.pkl")
+columns_path = os.path.join("src", "model", "model_columns.pkl")
+
+model = joblib.load(model_path)
+model_columns = joblib.load(columns_path)
+
+def predict_footprint(user_input: dict):
+    input_vector = np.zeros(len(model_columns))
+
+    for feature, value in user_input.items():
+        if feature in model_columns:
+            index = model_columns.index(feature)
+            input_vector[index] = value
+
+    return model.predict([input_vector])[0]

src/utils/reducer.py

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+def suggest_reduction(activities):
+    tips = []
+
+    if activities.get("car_km_per_week", 0) > 50:
+        tips.append("Consider using public transport or carpooling.")
+    if activities.get("meat_meals_per_week", 0) > 5:
+        tips.append("Try a vegetarian meal once or twice a week.")
+    if activities.get("electricity_units_per_month", 0) > 200:
+        tips.append("Use energy-efficient appliances and turn off unused devices.")
+    if activities.get("flights_per_year", 0) > 2:
+        tips.append("Offset your flight emissions or explore virtual meetings.")
+
+    if not tips:
+        tips.append("You're doing great! Keep up the sustainable habits.")
+
+    return tips