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@@ -1,16 +1,17 @@
+# Use the official Python slim 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
 
-# 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:sales_predictor_api"]
+# Expose Streamlit default port
+EXPOSE 7860
+
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.enableCORS=false"]

app.py

@@ -1,53 +1,72 @@
-import numpy as np
-import joblib
+
+import re
+import streamlit as st
 import pandas as pd
-from flask import Flask, request, jsonify
-
-# Initialize the Flask application
-sales_predictor_api = Flask("SuperKart Sales Predictor")
-
-# Load the trained machine learning model
-model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")
-
-# Define a route for the home page (GET request)
-@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)
-@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 requests
+
+valid_store_ids = ["OUT001", "OUT002", "OUT003", "OUT004"]
+store_meta = {
+    "OUT001": {"year": 1987, "size": "High",   "city_type": "Tier 2", "store_type": "Supermarket Type1"},
+    "OUT002": {"year": 1998, "size": "Small",  "city_type": "Tier 3", "store_type": "Food Mart"},
+    "OUT003": {"year": 1999, "size": "Medium", "city_type": "Tier 1", "store_type": "Departmental Store"},
+    "OUT004": {"year": 2009, "size": "Medium", "city_type": "Tier 2", "store_type": "Supermarket Type2"},
+}
+
+st.title("SmartKart: Product Sales Prediction")
+st.subheader("Online Prediction")
+
+product_id = st.text_input("Product ID (2 uppercase letters + 4 digits, e.g., FD6114)", max_chars=6)
+product_id_valid = bool(re.fullmatch(r"[A-Z]{2}\d{4}", product_id))
+if product_id and not product_id_valid:
+    st.error("Invalid Product ID format; it must be 2 uppercase letters followed by 4 digits.")
+elif product_id_valid:
+    st.success("Product ID format is valid.")
+
+store_id = st.selectbox("Store ID", options=valid_store_ids)
+meta = store_meta[store_id]
+st.text(f"Store Establishment Year: **{meta['year']}**")
+st.text(f"Store Size: **{meta['size']}**")
+st.text(f"Store Location City Type: **{meta['city_type']}**")
+st.text(f"Store Type: **{meta['store_type']}**")
+
+product_weight = st.number_input("Product Weight", min_value=0.0, value=12.0, format="%.2f")
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_allocated_area = st.slider("Product Allocated Area Ratio", min_value=0.0, max_value=1.0, step=0.001, value=0.05)
+product_type = st.selectbox("Product Type", [
+    "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
+    "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
+    "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others"
+])
+product_mrp = st.number_input("Product MRP", min_value=0.0, value=150.0, format="%.2f")
+
+input_data = pd.DataFrame([{
+    "Product_Id": product_id,
+    "Product_Weight": product_weight,
+    "Product_Sugar_Content": product_sugar_content,
+    "Product_Allocated_Area": product_allocated_area,
+    "Product_Type": product_type,
+    "Product_MRP": product_mrp,
+    "Store_Id": store_id,
+    "Store_Establishment_Year": meta['year'],
+    "Store_Size": meta['size'],
+    "Store_Location_City_Type": meta['city_type'],
+    "Store_Type": meta['store_type'],
+}])
+
+if st.button("Predict"):
+    if not product_id_valid:
+        st.error("Please fix the Product ID before proceeding.")
+    else:
+        try:
+            with st.spinner("Fetching prediction..."):
+                response = requests.post(
+                    "https://ShanRaja/Model_Sales_Prediction_SuperKart.hf.space/v1/sales",
+                    json=input_data.to_dict(orient="records")[0]
+                )
+            if response.status_code == 200:
+                prediction = response.json().get("Predicted Sales")
+                st.success(f"Predicted Product Sales: {prediction}")
+            else:
+                st.error(f"API Error {response.status_code}: {response.text}")
+        except Exception as e:
+            st.error(f"Prediction request failed: {e}")

requirements.txt

@@ -1,11 +1,3 @@
 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