Add dockerfile

Dockerfile

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+FROM python:3.10.9-slim
+WORKDIR /app
+COPY . .
+RUN pip install -r requirements.txt
+CMD ["python", "server.py"]

README.md

@@ -1,10 +1,11 @@
 ---
-title: Song Recommender
-emoji: 🐠
-colorFrom: red
+title: Recommender Api
+emoji: 📚
+colorFrom: yellow
 colorTo: purple
-sdk: docker
+sdk: static
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

custom_models.py

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+from fastai.tabular.all import *
+
+def create_params(size):
+    return nn.Parameter(torch.zeros(*size).normal_(0, 0.01))
+
+class DotProductBias(Module):
+    def __init__(self, n_users, n_items, n_factors, y_range=(0, 1.5)):
+        super().__init__()
+        self.user_factors = create_params([n_users, n_factors])
+        self.user_bias = create_params([n_users])
+        self.item_factors = create_params([n_items, n_factors])
+        self.item_bias = create_params([n_items])
+        self.y_range = y_range
+        
+    def forward(self, x):
+        users = self.user_factors[x[:,0]]
+        items = self.item_factors[x[:,1]]
+        res = (users * items).sum(dim=1)
+        res += self.user_bias[x[:,0]] + self.item_bias[x[:,1]]
+        return sigmoid_range(res, *self.y_range)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e924702f82931c0136765bdd8006a8c272769bd1090883b3964179740e5ca541
+size 6970809

requirements.txt

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+fastai
+fastapi
+uvicorn
+asyncio

server.py

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+from fastai.collab import load_learner
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from custom_models import DotProductBias 
+import asyncio
+import uvicorn
+import pandas as pd
+import os
+
+# FastAPI app
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Model filename
+model_filename = 'model.pkl'
+
+async def setup_learner():
+    learn = load_learner(model_filename)
+    learn.dls.device = 'cpu'
+    return learn
+
+learn = None
+@app.on_event("startup")
+async def startup_event():
+    """Setup the learner on server start"""
+    global learn
+    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("/recommend/{user_id}")
+async def analyze(user_id: str):
+    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)
+    predictions = learn.get_preds(dl=test_dl)
+    print(predictions)
+    #pred = learn.predict(file)
+    return {"result": predictions[0].numpy().tolist()}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))