Upload folder using huggingface_hub

Dockerfile

@@ -1,19 +1,16 @@
-# Use Python 3.9 as base image
-FROM python:3.9
+FROM python:3.9-slim
 
-# Set working directory
+# Set the working directory inside the container
 WORKDIR /app
 
-# Copy requirements.txt and install dependencies
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+# Copy all files from the current directory to the container's working directory
+COPY . .
 
-# Copy application code
-COPY app.py .
-
-# Expose port (matches port specified in app.py)
-EXPOSE 7860
-
-# Run the app using uvicorn
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:superkart_api`: Runs the Flask app (assuming `app.py` contains the Flask instance named `superkart_api`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_api"]

app.py

@@ -1,47 +1,44 @@
-import os
-from fastapi import FastAPI, Request
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-from huggingface_hub import hf_hub_download
-import joblib
-import pandas as pd
-
-app = FastAPI()
-
-# Add CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Force Hugging Face to use a writable directory
-os.environ["HF_HUB_CACHE"] = "/tmp/hf_cache"
-
-# Create the cache directory if it doesn't exist
-cache_dir = "/tmp/hf_cache"
-os.makedirs(cache_dir, exist_ok=True)
-
-# Diagnostic print to confirm we can write to /tmp
-print("Writable directories in /tmp:")
-print(os.listdir("/tmp"))
-
-# Download the model from Hugging Face
-model_path = hf_hub_download(
-    repo_id="MichaelNieto/super_model",
-    filename="best_model.joblib",
-    cache_dir=cache_dir
-)
-
-# Load the model
-model = joblib.load(model_path)
-
-# Define the prediction endpoint
-@app.post("/v1/predict")
-async def predict(request: Request):
-    data = await request.json()
-    input_df = pd.DataFrame([data])
-    prediction = model.predict(input_df)[0]
-    result = {"Sales": round(float(prediction), 2)}
-    return JSONResponse(content=result)
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+import os  # For creating directories if needed
+
+# Ensure the model directory exists
+os.makedirs("model", exist_ok=True)
+
+# Initialize Flask app with a name
+superkart_api = Flask("superkart_api")  # Name of the app
+
+# Load the trained churn prediction model
+model = joblib.load("model/best_model.joblib")  # Path to the serialized model
+
+# Define a route for the home page
+@superkart_api.get('/')
+def home():
+    return "Welcome to Superkart Sales Prediction API!"  # Welcome message
+
+# Define an endpoint to predict churn for a single customer
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    data = request.get_json()
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Product_Allocated_Area': data['Product_Allocated_Area'],
+        'Product_MRP': data['Product_MRP'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Product_Id_char': data['Product_Id_char'],
+        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Type_Category': data['Product_Type_Category']
+    }
+    input_data = pd.DataFrame([sample])
+    prediction = model.predict(input_data).tolist()[0]
+    return jsonify({'Sales': prediction})
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    superkart_api.run(debug=True)

requirements.txt

@@ -1,15 +1,12 @@
 pandas==2.2.2
-numpy==2.0.0
+numpy==2.0.2
 scikit-learn==1.6.1
 seaborn==0.13.2
 joblib==1.4.2
 xgboost==2.1.4
-werkzeug==2.2.2
+Werkzeug==2.2.2
 flask==2.2.2
 gunicorn==20.1.0
 requests==2.32.3
 uvicorn[standard]
 streamlit==1.43.2
-fastapi==0.109.0
-uvicorn==0.29.0
-huggingface_hub