Upload folder using huggingface_hub

Dockerfile

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+# Base image with Python
+FROM python:3.10-slim
+
+# Set working directory inside container
+WORKDIR /app
+
+# Copy requirements first (for caching)
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy all project files (API + model)
+COPY . .
+
+# Expose port for Flask
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "app.py"]

app.py

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+
+from flask import Flask, request, jsonify
+import joblib
+import numpy as np
+import os
+
+
+from huggingface_hub import hf_hub_download
+import joblib
+
+
+
+model_path = hf_hub_download(
+    repo_id="affanthinks/superkart",
+    filename="AGreatLearning/tuned_bagging_model.pkl",  # include directory
+    revision="main",                                     # ensures correct branch
+    token=os.getenv("HF_TOKEN")                          # authentication
+)
+
+model = joblib.load(model_path)
+print("✅ Model loaded successfully from", model_path)
+
+
+
+
+# Initialize app
+app = Flask("predict_revenue")
+
+@app.route("/")
+def home():
+    return jsonify({"message": "Supermarket Revenue Prediction API is running!"})
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    try:
+        # Get JSON input
+        data = request.get_json(force=True)
+        features = np.array(data["features"]).reshape(1, -1)
+
+        # Predict
+        prediction = model.predict(features)[0]
+
+        return jsonify({"predicted_revenue": float(prediction)})
+
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+if "predict_revenue" == "__main__":
+    app.run(host="0.0.0.0", port=7860, debug=True)

requirements.txt

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+flask
+numpy==1.26.4
+pandas==2.2.2
+scikit-learn==1.5.1
+joblib==1.4.2
+huggingface_hub
+