num_recommendations and placeholder for track history

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

server.py

@@ -1,5 +1,5 @@
 from fastai.collab import load_learner
-from fastapi import FastAPI
+from fastapi import FastAPI, Query
 from fastapi.middleware.cors import CORSMiddleware
 from custom_models import DotProductBias 
 import asyncio
@@ -34,9 +34,22 @@ async def startup_event():
     loop = asyncio.get_event_loop()  # get event loop
     tasks = [asyncio.ensure_future(setup_learner())]  # assign some task
     learn = (await asyncio.gather(*tasks))[0]
-    
+
+@app.get('/user/{user_id}/history')
+async def get_user_track_history(user_id: str):
+    return {
+        "user_id": user_id,
+        "history": [
+            {"track_id": "1", "genre": "Rock", "year": "2008", "artist": "Kings of Leon", "name": "Revelry"},
+            {"track_id": "2", "genre": "Metalcore", "year": "2010", "artist": "Miss May I", "name": "Gears"},
+            {"track_id": "3", "genre": "Electro", "year": "2009", "artist": "David Guetta", "name": "Sexy Bitch"}
+        ]
+    }
+
 @app.get("/recommend/{user_id}")
-async def analyze(user_id: str):
+async def get_recommendations_for_user(user_id: str, num_recommendations: int = Query(5)):
+    print(num_recommendations)
+    print(user_id)
     not_listened_songs = ["Revelry, Kings of Leon, 2008", "Gears, Miss May I, 2010", "Sexy Bitch, David Guetta, 2009"]
     input_dataframe = pd.DataFrame({'user_id': ["440abe26940ae9d9268157222a4a3d5735d44ed8"] * len(not_listened_songs), 'entry': not_listened_songs})
     test_dl = learn.dls.test_dl(input_dataframe)
@@ -47,3 +60,4 @@ async def analyze(user_id: str):
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))
+