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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# Define the command to run the Streamlit app on port "8501" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

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+import os, joblib, pandas as pd
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Dict, Any
+from huggingface_hub import hf_hub_download
+
+# from google.colab import userdata
+# access_token = userdata.get("Login") 
+
+# access_token = HfApi(token=os.environ["Login"])
+
+# HF_MODEL_REPO = os.environ.get("HF_MODEL_REPO", "<YOUR_HF_USERNAME>/<YOUR_MODEL_REPO>")
+# MODEL_FILENAME = os.environ.get("MODEL_FILENAME", "best_pipeline.joblib")
+# HF_TOKEN = os.environ.get("Login", None)
+
+import os
+HF_TOKEN = os.getenv("Login")
+
+HF_MODEL_REPO = "https://huggingface.co/keerthas/tourism-package-model"
+MODEL_FILENAME = "best_pipeline.joblib"
+# HF_TOKEN = access_token
+
+app = FastAPI(title="Tourism Package Prediction Service")
+
+def load_model_from_hf():
+    try:
+        path = hf_hub_download(repo_id=HF_MODEL_REPO, filename=MODEL_FILENAME, token=HF_TOKEN)
+        model = joblib.load(path)
+        return model
+    except Exception as e:
+        raise RuntimeError(f"Failed to download/load model from HF: {e}")
+
+MODEL = load_model_from_hf()
+
+class Record(BaseModel):
+    __root__: List[Dict[str, Any]]
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+@app.post("/predict")
+def predict(records: Record):
+    try:
+        df = pd.DataFrame(records.__root__)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid input: {e}")
+    try:
+        preds = MODEL.predict(df)
+        proba = MODEL.predict_proba(df).tolist() if hasattr(MODEL, "predict_proba") else None
+        return {"predictions": preds.tolist(), "probabilities": proba}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Model prediction error: {e}")

requirements.txt

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+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1