Update app.py

app.py

@@ -5,6 +5,7 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel, Field
 from sklearn.neighbors import KDTree
 import numpy as np
+import uvicorn
 
 # Configurable number of scales
 NUM_SCALES = 8
@@ -96,16 +97,15 @@ def recommend(user_id: str, region_radius: float = 0.2, top_k: int = 5):
     ]
 
 # Sample test data when run directly
-if __name__ == "__main__":
-    # Install dependencies: fastapi, uvicorn, numpy, scikit-learn, pydantic
-    import uvicorn
+
     # Create random test users and content
-    repo.add_user(Profile(id="user1", scales=list(np.random.rand(NUM_SCALES))))
-    for i in range(1, 21):
-        repo.add_content(Content(
+repo.add_user(Profile(id="user1", scales=list(np.random.rand(NUM_SCALES))))
+for i in range(1, 21):
+    repo.add_content(Content(
             id=f"content{i}",
             scales=list(np.random.rand(NUM_SCALES)),
             metadata={"title": f"Item {i}"}
         ))
-    recommend("user1")
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+recommend("user1")
+print("ran")
+uvicorn.run(app, host="0.0.0.0", port=7860)