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Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 7575 available to the world outside this container
+EXPOSE 7575
+
+# Run the application when the container launches
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7575"]

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+import numpy as np
+
+app = FastAPI(title="Airbnb Price Prediction in Copenhagen")
+
+# Load model and preprocessing objects
+model_xgb = joblib.load('model_xgb.joblib')
+scaler = joblib.load('scaler.joblib')
+ohe = joblib.load('ohe.joblib')
+
+class RoomFeatures(BaseModel):
+    neighbourhood_cleansed: str
+    room_type: str
+    instant_bookable: bool
+    accommodates: int
+    bedrooms: int
+    beds: int
+    minimum_nights_avg_ntm: int
+
+@app.post("/predict")
+async def predict_price(features: RoomFeatures):
+    try:
+        # Prepare categorical features
+        cat_features = pd.DataFrame({
+            'neighbourhood_cleansed': [features.neighbourhood_cleansed],
+            'room_type': [features.room_type]
+        })
+        cat_encoded = pd.DataFrame(
+            ohe.transform(cat_features).todense(),
+            columns=ohe.get_feature_names_out(['neighbourhood_cleansed', 'room_type'])
+        )
+
+        # Prepare numerical features
+        num_features = pd.DataFrame({
+            'instant_bookable': [int(features.instant_bookable)],
+            'accommodates': [features.accommodates],
+            'bedrooms': [features.bedrooms],
+            'beds': [features.beds],
+            'minimum_nights_avg_ntm': [features.minimum_nights_avg_ntm]
+        })
+        num_scaled = pd.DataFrame(scaler.transform(num_features), columns=num_features.columns)
+
+        # Combine features
+        combined_features = pd.concat([num_scaled, cat_encoded], axis=1)
+
+        # Make prediction
+        predicted_price = model_xgb.predict(combined_features)[0]
+
+        # Calculate price range
+        lower_range = max(0, round(predicted_price - 350))
+        upper_range = round(predicted_price + 350)
+
+        return {
+            "predicted_price": round(predicted_price),
+            "suggested_price_range": {
+                "lower": lower_range,
+                "upper": upper_range
+            }
+        }
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Airbnb Price Prediction API for Copenhagen"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)