Upload folder using huggingface_hub

Dockerfile

@@ -1,16 +1,18 @@
+
+# Use the official Streamlit image
 FROM python:3.10-slim
 
-# Set the working directory inside the container
+# Set working directory
 WORKDIR /app
 
-# Copy all files from the current directory to the container's working directory
+# Copy app files
 COPY . .
 
-# Install dependencies from the requirements file without using cache to reduce image size
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose Streamlit default port
+EXPOSE 7860
 
-# 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:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:t_sales_predictor_api"]
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.enableCORS=false"]

app.py

@@ -1,53 +1,68 @@
-import numpy as np
-import joblib
+import streamlit as st
 import pandas as pd
-from flask import Flask, request, jsonify
-
-# Initialize the Flask application
-t_sales_predictor_api = Flask("SuperKart Sales Predictor")
-
-# Load the trained machine learning model
-model = joblib.load("t_superkart_sales_prediction_model_v1_0.joblib")
-
-# Define a route for the home page (GET request)
-@t_sales_predictor_api.get('/')
-def home():
-    """
-    This function handles GET requests to the root URL ('/') of the API.
-    It returns a simple welcome message.
-    """
-    return "Welcome to the SuperKart Sales Prediction API!"
-
-# Define an endpoint for single property prediction (POST request)
-@t_sales_predictor_api.post('/v1/sales')
-def predict_sales():
-    """
-    This function handles POST requests to the '/v1/sales' endpoint.
-    It expects a JSON payload containing property details and returns
-    the predicted rental price as a JSON response.
-    """
-    # Get the JSON data from the request body
-    data = request.get_json()
-      # Extract relevant features from the JSON data
-    sample = {
-        'Product_Id': data['product_id'],
-        'Product_Weight': data['product_weight'],
-        'Product_Sugar_Content': data['product_sugar_content'],
-        'Product_Allocated_Area': data['product_allocated_area'],
-        'Product_Type': data['product_type'],
-        'Product_MRP': data['product_mrp'],
-        'Store_Id': data['store_id'],
-        'Store_Establishment_Year': data['store_establishment_year'],
-        'Store_Size': data['store_size'],
-        'Store_Location_City_Type': data['store_location_city_type'],
-        'Store_Type': data['store_type'],
-    }
-
-    # Convert the extracted data into a Pandas DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make prediction
-    pred = model.predict(input_data)[0]
-
-    # Return the actual price
-    return jsonify({'Predicted Sales': round(float(pred), 2)})
+import joblib
+
+# Load trained model
+@st.cache_resource
+def load_model():
+    return joblib.load("t_superkart_sales_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+st.set_page_config(page_title="SuperKart Sales Predictor", layout="wide")
+st.title("🛒 SuperKart Sales Prediction App")
+
+st.write("Fill in the product and store details below to predict sales.")
+
+# Input fields
+col1, col2 = st.columns(2)
+
+with col1:
+    product_weight = st.number_input("Product Weight", min_value=0.0, step=0.01)
+    product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, step=0.001)
+    product_mrp = st.number_input("Product MRP", min_value=0.0, step=0.01)
+    product_store_sales_total = st.number_input("Product Store Sales Total", min_value=0.0, step=0.01)
+    store_age = st.number_input("Store Age (Years)", min_value=0, step=1)
+
+with col2:
+    product_sugar_content = st.selectbox("Sugar Content", ["no sugar", "regular"])
+    product_type = st.selectbox(
+        "Product Type",
+        ["breads", "breakfast", "canned", "dairy", "frozen foods",
+         "fruits and vegetables", "hard drinks", "health and hygiene",
+         "household", "meat", "others", "seafood", "snack foods",
+         "soft drinks", "starchy foods"]
+    )
+    store_size = st.selectbox("Store Size", ["small", "medium"])
+    store_location_city_type = st.selectbox("Store Location City Type", ["tier 2", "tier 3"])
+    store_type = st.selectbox("Store Type", ["food mart", "supermarket type1", "supermarket type2"])
+    product_group_code = st.selectbox("Product Group Code", ["fd", "nc"])
+
+# Convert inputs to DataFrame
+input_data = {
+    "Product_Weight": product_weight,
+    "Product_Allocated_Area": product_allocated_area,
+    "Product_MRP": product_mrp,
+    "Product_Store_Sales_Total": product_store_sales_total,
+    "Store_Age": store_age,
+    f"Product_Sugar_Content_{product_sugar_content}": True,
+    f"Product_Type_{product_type}": True,
+    f"Store_Size_{store_size}": True,
+    f"Store_Location_City_Type_{store_location_city_type}": True,
+    f"Store_Type_{store_type}": True,
+    f"Product_Group_Code_{product_group_code}": True
+}
+
+input_df = pd.DataFrame([input_data]).astype(object)
+
+# Align with model features
+if hasattr(model, "feature_names_in_"):
+    input_df = input_df.reindex(columns=model.feature_names_in_, fill_value=0)
+
+# Prediction
+if st.button("Predict"):
+    try:
+        prediction = model.predict(input_df)[0]
+        st.success(f"Predicted Product Sales: {prediction:.2f}")
+    except Exception as e:
+        st.error(f"Prediction failed: {e}")

requirements.txt

@@ -1,11 +1,6 @@
-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
-uvicorn[standard]
-streamlit==1.43.2
+streamlit
+requests
+pandas
+scikit-learn
+joblib
+