Update app.py

app.py

@@ -1,39 +1,43 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
-import pandas as pd
-import pickle
 from fastapi.middleware.cors import CORSMiddleware
+from huggingface_hub import hf_hub_download
+import joblib
+import pandas as pd
+
+# -----------------------------
+# Download model + columns from Hugging Face Hub
+# -----------------------------
+REPO_ID = "MAZEN00/Player"
 
-# Load pipeline and columns
-with open("modeel.joblib", "rb") as f:
-    model = pickle.load(f)
+model_path = hf_hub_download(repo_id=REPO_ID, filename="modeel.joblib")
+columns_path = hf_hub_download(repo_id=REPO_ID, filename="columns.pkl")
 
-with open("columns.pkl", "rb") as f:
-    model_columns = pickle.load(f)
+model = joblib.load(model_path)
+model_columns = joblib.load(columns_path)
 
+# -----------------------------
+# FastAPI setup
+# -----------------------------
 app = FastAPI()
 
-# Enable CORS (needed for frontend to connect)
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  
+    allow_origins=["*"], 
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# Health check route
-@app.get("/")
-def read_root():
-    return {"status": "ok", "message": "FastAPI is running on Hugging Face 🚀"}
-
+# -----------------------------
 # Input schema
-class InputModel(BaseModel):
+# -----------------------------
+class PlayerInput(BaseModel):
     Age: int
     Potential: int
     Club: str
-    Value: int
-    Wage: int
+    Value: float
+    Wage: float
     Special: int
     Preferred_Foot: str
     International_Reputation: int
@@ -42,7 +46,7 @@ class InputModel(BaseModel):
     Body_Type: str
     Position: str
     Height: float
-    Weight: int
+    Weight: float
     Release_Clause: float
     Role: str
     Contract_years: int
@@ -50,13 +54,12 @@ class InputModel(BaseModel):
     Attacking_WorkRate: str
     Defensive_WorkRate: str
 
-# Prediction route
+# -----------------------------
+# Prediction endpoint
+# -----------------------------
 @app.post("/predict")
-def predict(player: InputModel):
+def predict(player: PlayerInput):
     data = pd.DataFrame([player.dict()])
-    data = data[model_columns]
-
-    prediction = model.predict(data)
-    prediction_int = int(prediction[0])  # make sure it's an integer
-
-    return {"prediction": prediction_int}
+    data = data.reindex(columns=model_columns, fill_value=0)
+    prediction = model.predict(data)[0]
+    return {"prediction": int(prediction)}