Upload folder using huggingface_hub

Dockerfile

@@ -1,5 +1,5 @@
 # Use a minimal base image with Python 3.9 installed
-FROM python:3.9
+FROM python:3.11-slim
 
 # Set the working directory inside the container to /app
 WORKDIR /app

app.py

@@ -5,19 +5,12 @@ import joblib
 import os
 
 from huggingface_hub import HfApi, login
-#login(os.getenv("HF_TOKEN"))
-
-# Download the model from the Model Hub
-#model_path = hf_hub_download(
-    #repo_id="tam3222/tourism",
-    #filename="best_engine_prediction_model_v1.joblib"
-#)
 
 HF_TOKEN = os.getenv("HF_TOKEN")  # Optional if repo is public
 
 model_path = hf_hub_download(
     repo_id="tam3222/Engine_Model",
-    filename="models/RandomForest.pkl", 
+    filename="models/RandomForest.pkl",  
     repo_type="model",
     token=HF_TOKEN  # can be None for public repos
 )

requirements.txt

@@ -2,11 +2,23 @@ pandas==2.2.2
 numpy==1.26.4
 matplotlib==3.8.4
 seaborn==0.13.2
+
+# Hugging Face
 datasets==2.19.1
 huggingface_hub==0.32.6
+
+# MLflow
 mlflow==3.0.1
+
+# ngrok
 pyngrok==7.1.3
+
+# Machine Learning
 scikit-learn==1.6.0
 xgboost==2.1.4
+imbalanced-learn==0.12.3
+
+# Utilities
 joblib==1.5.1
 streamlit==1.35.0
+