Upload main.py

main.py

@@ -0,0 +1,90 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load the model
+try:
+    model = joblib.load("titanic_model.pkl")
+    logger.info(f"Model loaded successfully. Feature names: {model.feature_names_in_}")
+except Exception as e:
+    logger.error(f"Error loading model: {e}")
+    raise
+
+# Create the Pydantic model for the input data
+class Passenger(BaseModel):
+    pclass: int
+    sex: str
+    age: float
+    sibsp: int
+    parch: int
+    fare: float
+    embarked: str
+
+
+# {
+#   "pclass": 1,
+#   "sex": "male",
+#   "age": 30,
+#   "sibsp": 0,
+#   "parch": 0,
+#   "fare": 100,
+#   "embarked": "S"
+# }
+
+# Create the FastAPI instance
+app = FastAPI()
+
+# Create the root endpoint
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the Titanic Survival Prediction API"}
+
+# Create the predict endpoint
+@app.post("/predict")
+def predict(passenger: Passenger):
+    try:
+        # Convert the input data to a DataFrame
+        input_dict = passenger.model_dump()
+        logger.info(f"Input data: {input_dict}")
+        
+        input_data = pd.DataFrame([input_dict])
+        logger.info(f"DataFrame created with columns: {input_data.columns.tolist()}")
+        
+        # One-Hot Encode the input data
+        input_data = pd.get_dummies(input_data)
+        logger.info(f"After one-hot encoding, columns: {input_data.columns.tolist()}")
+        
+        # Check if model has feature_names_in_ attribute
+        if not hasattr(model, 'feature_names_in_'):
+            raise HTTPException(status_code=500, detail="Model does not have feature_names_in_ attribute")
+        
+        logger.info(f"Model expects columns: {model.feature_names_in_}")
+        
+        # Align the input data columns with the model columns
+        input_data = input_data.reindex(columns=model.feature_names_in_, fill_value=0)
+        logger.info(f"After reindexing, columns: {input_data.columns.tolist()}")
+        
+        # Check if we have the right number of features
+        if input_data.shape[1] != len(model.feature_names_in_):
+            raise HTTPException(
+                status_code=500, 
+                detail=f"Feature mismatch: Input has {input_data.shape[1]} features, model expects {len(model.feature_names_in_)}"
+            )
+        
+        # Predict the survival of the passenger
+        prediction = model.predict(input_data)
+        
+        return {
+            "prediction": int(prediction[0]),
+            "prediction_probability": float(model.predict_proba(input_data)[0][1]) if hasattr(model, 'predict_proba') else None
+        }
+        
+    except Exception as e:
+        logger.error(f"Prediction error: {e}")
+        raise HTTPException(status_code=500, detail=f"Prediction failed: {str(e)}")