Update route_optimizer/green_route_optimizer.py

route_optimizer/green_route_optimizer.py

@@ -1,48 +1,65 @@
 # route_optimizer/green_route_optimizer.py
 
 import math
+from geopy.geocoders import Nominatim
 
 class GreenRouteOptimizer:
     """
-    Green Route Optimizer class.
-    Provides route optimization and recommends the most eco-friendly route.
+    Green Route Optimizer for rail, road, and ship transport.
+    Accepts place names (with optional country/state codes) and recommends the greenest route.
     """
 
-    # Approximate emission factors (kg CO2 per km)
+    # Example emission factors (kg CO2 per km)
     EMISSION_FACTORS = {
-        "driving": 0.21,   # example: car
-        "cycling": 0.0,    # bicycles produce zero direct emissions
-        "walking": 0.0     # walking produces zero direct emissions
+        "rail": 0.041,   # kg CO2 per km
+        "road": 0.21,    # kg CO2 per km
+        "ship": 0.015    # kg CO2 per km
     }
 
-    def __init__(self):
-        pass
+    def __init__(self, user_agent="green_route_optimizer"):
+        self.geolocator = Nominatim(user_agent=user_agent)
 
-    def optimize(self, start, end, mode="driving"):
+    def geocode(self, place_name):
         """
-        Returns a mock optimized route for a given transport mode.
-        Uses Euclidean distance as a simple approximation.
+        Converts a place name to latitude and longitude.
+        Example input: "New York, NY" or "London, UK"
+        Returns a tuple: (lat, lon)
         """
-        distance_km = math.dist(start, end)
+        location = self.geolocator.geocode(place_name)
+        if location:
+            return (location.latitude, location.longitude)
+        else:
+            raise ValueError(f"Could not geocode place: {place_name}")
+
+    def optimize(self, start_coords, end_coords, mode="road"):
+        """
+        Returns a mock optimized route for the specified transport mode.
+        Distance is calculated as Euclidean distance (approximation).
+        """
+        distance_km = math.dist(start_coords, end_coords)
         optimized_route = {
-            "start": start,
-            "end": end,
+            "start": start_coords,
+            "end": end_coords,
             "mode": mode,
             "optimized_distance_km": round(distance_km, 2),
             "estimated_emission_kg": round(distance_km * self.EMISSION_FACTORS.get(mode, 0.21), 3)
         }
         return optimized_route
 
-    def recommend_green_routes(self, start, end):
+    def recommend_green_routes(self, start_place, end_place):
         """
-        Recommends the greenest route among driving, cycling, and walking.
-        Returns a sorted list by estimated emissions.
+        Recommends the greenest route among rail, road, and ship using place names.
+        Returns a list of routes sorted by lowest estimated emissions.
         """
+        # Convert place names to coordinates
+        start_coords = self.geocode(start_place)
+        end_coords = self.geocode(end_place)
+
         routes = []
         for mode in self.EMISSION_FACTORS.keys():
-            route = self.optimize(start, end, mode)
+            route = self.optimize(start_coords, end_coords, mode)
             routes.append(route)
 
-        # Sort routes by estimated emissions (ascending)
+        # Sort by estimated emissions (ascending)
         routes_sorted = sorted(routes, key=lambda x: x["estimated_emission_kg"])
         return routes_sorted