Upload folder using huggingface_hub

Dockerfile

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+# Use slim Python image
+FROM python:3.9-slim
+
+# Set working directory inside the container
+WORKDIR /app
+
+# Copy project files into the container
+COPY . .
+
+# Install dependencies and print package list to verify gunicorn is installed
+RUN pip install --no-cache-dir --upgrade pip \
+ && pip install --no-cache-dir -r requirements.txt \
+ && echo "Installed packages:" \
+ && pip list
+
+# Expose the port Hugging Face expects
+EXPOSE 7860
+
+# Start the Flask app using gunicorn
+# - app: refers to app.py
+# - app: the Flask app object in app.py (corrected from rental_price_predictor_api)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]
+     

SuperKart_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:24da6828e3b20a1c6b5341217ea94c563b2f0e091b64def76aff316904ca3e87
+size 687933

app.py

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+
+# Import necessary libraries
+import numpy as np
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+import traceback
+import math
+
+# Define the path where the model is saved
+model_file_name = "SuperKart_v1_0.joblib"
+
+try:
+    # Load the trained machine learning model
+    model = joblib.load(model_file_name)
+except FileNotFoundError:
+    print(f"Error: Model file not found at {model_file_name}")
+    model = None
+except Exception as e:
+    print(f"Error loading model: {e}")
+    traceback.print_exc()
+    model = None
+
+# Initialize the Flask app
+app = Flask(__name__)
+
+@app.route('/')
+def home():
+    return "Welcome to the Super Kart Product Sales Price Prediction API!"
+
+# ---------------- single Prediction Endpoint ----------------
+@app.route('/v1/salesprice', methods=['POST'])
+def predict_sales_price():
+    if model is None:
+        return jsonify({"error": "Model not loaded. Cannot make predictions."}), 500
+
+    try:
+        property_data = request.get_json(force=True)
+
+        expected_keys = [
+            'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
+            'Product_Type', 'Product_MRP', 'Store_Size',
+            'Store_Location_City_Type', 'Store_Type', 'Store_Age'
+        ]
+        if not all(key in property_data for key in expected_keys):
+            missing_keys = [key for key in expected_keys if key not in property_data]
+            return jsonify({"error": f"Missing keys in input data: {missing_keys}"}), 400
+
+        sample = {key: property_data.get(key) for key in expected_keys}
+        input_data = pd.DataFrame([sample])
+
+        predicted_sales_price = model.predict(input_data)
+        predicted_price = round(float(predicted_sales_price[0]), 2)
+
+        if math.isinf(predicted_price) or math.isnan(predicted_price):
+            return jsonify({"error": "Prediction resulted in an invalid value."}), 400
+
+        return jsonify({'Predicted Price': predicted_price}), 200
+
+    except Exception as e:
+        print(f"Error during single prediction: {e}")
+        traceback.print_exc()
+        return jsonify({"error": "Internal server error", "details": str(e)}), 500
+
+# ---------------- Batch Prediction Endpoint ----------------
+@app.route('/v1/salespricebatch', methods=['POST'])
+def predict_sales_price_batch():
+    """
+    Expects a CSV file with one product per row.
+    Returns JSON: a list of dicts with `row_id` and predicted price.
+    """
+    if model is None:
+        return jsonify({"error": "Model not loaded. Cannot make predictions."}), 500
+
+    if 'file' not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+
+    try:
+        file = request.files['file']
+        input_data = pd.read_csv(file)
+
+        expected_columns = [
+            'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
+            'Product_Type', 'Product_MRP', 'Store_Size',
+            'Store_Location_City_Type', 'Store_Type', 'Store_Age'
+        ]
+        missing_columns = [col for col in expected_columns if col not in input_data.columns]
+        if missing_columns:
+            return jsonify({"error": f"Missing required columns: {missing_columns}"}), 400
+
+        input_data.reset_index(inplace=True)
+        input_data.rename(columns={'index': 'row_id'}, inplace=True)
+
+        predictions = model.predict(input_data[expected_columns])
+        predicted_prices = [round(float(p), 2) for p in predictions]
+
+        results = [
+            {"row_id": row_id, "Predicted Price": price}
+            for row_id, price in zip(input_data['row_id'], predicted_prices)
+        ]
+
+        return jsonify(results), 200
+
+    except Exception as e:
+        print(f"Error during batch prediction: {e}")
+        traceback.print_exc()
+        return jsonify({"error": "Internal server error during batch prediction.", "details": str(e)}), 500
+
+if __name__ == '__main__':
+    pass

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+streamlit==1.43.2
+flask-cors==3.0.10