Create app.py

app.py

@@ -0,0 +1,62 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import pandas as pd
+import pickle
+from fastapi.middleware.cors import CORSMiddleware
+
+# Load pipeline and columns
+with open("modeel.joblib", "rb") as f:
+    model = pickle.load(f)
+
+with open("columns.pkl", "rb") as f:
+    model_columns = pickle.load(f)
+
+app = FastAPI()
+
+# Enable CORS (needed for frontend to connect)
+app.add_middleware(
+    CORSMiddleware,
+    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):
+    Age: int
+    Potential: int
+    Club: str
+    Value: int
+    Wage: int
+    Special: int
+    Preferred_Foot: str
+    International_Reputation: int
+    Weak_Foot: int
+    Skill_Moves: int
+    Body_Type: str
+    Position: str
+    Height: float
+    Weight: int
+    Release_Clause: float
+    Role: str
+    Contract_years: int
+    Fitness_level: str
+    Attacking_WorkRate: str
+    Defensive_WorkRate: str
+
+# Prediction route
+@app.post("/predict")
+def predict(player: InputModel):
+    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}