backend/app/dependencies.py

@@ -0,0 +1 @@
+import app.src

backend/app/main.py

@@ -1,10 +1,15 @@
-from optimizer import solve_optimization
-from optimizer import landmark
+from src.optimizer import solve_optimization
+from src.optimizer import landmark
+from fastapi import FastAPI
 
-def main():
+app = FastAPI()
+
+
+@app.get("/optimize/{max_steps}/{print_to_console}")
+def main(max_steps: int, print_to_console: bool):
     
     # CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
-    max_steps = 16
+    #max_steps = 16
 
 
     # Initialize all landmarks (+ start and goal). Order matters here
@@ -18,8 +23,11 @@ def main():
     landmarks.append(landmark("arrivée", -1, (0, 0)))
 
 
-    visiting_order = solve_optimization(landmarks, max_steps, True)
+    visiting_order = solve_optimization(landmarks, max_steps, print_to_console)
 
+    #return visiting_order
+
+    return("max steps :", max_steps, "\n", visiting_order)
 
 
 if __name__ == "__main__":

backend/app/src/optimizer.py

@@ -10,7 +10,6 @@ class landmark :
         self.attractiveness = attractiveness
         self.loc = loc
 
-
 # Convert the solution of the optimization into the list of edges to follow. Order is taken into account
 def untangle(resx: list) :
     N = len(resx)                   # length of res
@@ -43,6 +42,7 @@ def untangle(resx: list) :
 def print_res(res, landmarks: list, P) :
     X = abs(res.x)
     order = untangle(X)
+    things = []
 
     """N = int(np.sqrt(len(X)))
     for i in range(N):
@@ -59,11 +59,12 @@ def print_res(res, landmarks: list, P) :
 
     for idx in order : 
         print('- ' + landmarks[idx].name)
+        things.append(landmarks[idx].name)
 
     steps = path_length(P, abs(res.x))
     print("\nSteps walked : " + str(steps))
 
-    return order
+    return things
 
 # Checks for cases of circular symmetry in the result
 def has_circle(resx: list) :
@@ -260,7 +261,7 @@ def path_length(P: list, resx: list) :
     return np.dot(P, resx)
 
 # Main optimization pipeline
-def solve_optimization (landmarks, max_steps, printing) :
+def solve_optimization (landmarks, max_steps, printing_console) :
 
     # SET CONSTRAINTS FOR INEQUALITY
     c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
@@ -307,12 +308,11 @@ def solve_optimization (landmarks, max_steps, printing) :
             raise ValueError("No solution could be found, even when increasing max_steps using the heuristic")
     
 
-    if printing is True :
+    if printing_console is True :
         if i != 0 :
             print(f"Neded to recompute paths {i} times because of unconnected loops...")
             X = print_res(res, landmarks, P)
             return X
-
     else :
         return untangle(res.x)