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Dockerfile

@@ -1,16 +1,14 @@
+# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
-# Set the working directory inside the container
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-# Copy all files from the current directory to the container's working directory
+# Copy all files from the current directory on the host to the container's /app directory
 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 Python dependencies listed in requirements.txt
+RUN pip3 install -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_api"]
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -1,83 +1,72 @@
-import joblib
+import streamlit as st
 import pandas as pd
-from flask import Flask, request, jsonify
-
-# Initialize Flask app
-sales_api = Flask("Product Store Sales Predictor")
-
-# Load the trained sales prediction model
-model = joblib.load("sales_prediction_model_v1_0.joblib")
-
-
-# Define a route for the home page
-@sales_api.get('/')
-def home():
-    return "Welcome to the Product Store Sales Prediction API!"
-
-# ==============================
-# Single record prediction endpoint
-# ==============================
-@sales_api.post('/v1/sales')
-def predict_sales():
-    try:
-        # Get JSON data from the request
-        data = request.get_json()
-
-        # Extract all required product/store features from the input 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 dictionary to DataFrame
-        input_df = pd.DataFrame([sample])
-
-        # Make prediction using the trained model
-        prediction = model.predict(input_df).tolist()[0]
-
-        # Return JSON response
-        return jsonify({'Predicted_Product_Store_Sales_Total': prediction})
-
-    except Exception as e:
-        return jsonify({'error': str(e)}), 400
-
-# ==============================
-# Batch prediction endpoint
-# ==============================
-@sales_api.post('/v1/salesbatch')
-def predict_sales_batch():
-    try:
-        # Get uploaded CSV file from the request
-        file = request.files['file']
-
-        # Read CSV file into DataFrame
-        input_data = pd.read_csv(file)
-
-        # Make predictions on the batch
-        predictions = model.predict(input_data).tolist()
-
-        # Add predictions as new column
-        input_data['Predicted_Product_Store_Sales_Total'] = predictions
-
-        # Convert DataFrame to list of dicts for JSON output
-        result = input_data.to_dict(orient='records')
-
-        return jsonify(result)
-
-    except Exception as e:
-        return jsonify({'error': str(e)}), 400
-
-# ==============================
-# Run Flask app
-# ==============================
-if __name__ == '__main__':
-    sales_api.run(debug=True)
+import requests
+
+# ==================================
+# Streamlit UI
+# ==================================
+st.title("Product Store Sales Prediction App")
+st.write("This app predicts total sales for a product in a store based on product and store features.")
+st.write("Adjust the sliders and input fields below to get a prediction for a single product-store combination.")
+
+# -------------------------
+# Single record inputs
+# -------------------------
+Product_Id = st.number_input("Product ID", min_value=1, step=1)
+Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.01, value=0.5)
+Product_Sugar_Content = st.number_input("Sugar Content (%)", min_value=0.0, max_value=100.0, step=0.1, value=10.0)
+Product_Allocated_Area = st.number_input("Allocated Area (sq.m)", min_value=0.0, step=0.1, value=50.0)
+Product_Type = st.text_input("Product Type", value="Food")
+Product_MRP = st.number_input("Maximum Retail Price (MRP)", min_value=0.0, step=0.1, value=100.0)
+
+Store_Id = st.number_input("Store ID", min_value=1, step=1)
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, step=1, value=2000)
+Store_Size = st.number_input("Store Size (sq.m)", min_value=0.0, step=0.1, value=100.0)
+Store_Location_City_Type = st.selectbox("City Type", ["A", "B", "C"])
+Store_Type = st.text_input("Store Type", value="Supermarket")
+
+# -------------------------
+# Single prediction
+# -------------------------
+if st.button("Predict Sales", type='primary'):
+    input_data = {
+        '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': Store_Establishment_Year,
+        'Store_Size': Store_Size,
+        'Store_Location_City_Type': Store_Location_City_Type,
+        'Store_Type': Store_Type
+    }
+
+    # Replace with your Hugging Face Space endpoint
+    API_URL = "https://abhishek1504-learning.hf.space/v1/sales"
+
+    response = requests.post(API_URL, json=input_data)
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Predicted_Product_Store_Sales_Total"]
+        st.success(f"💰 Predicted Total Product-Store Sales: **{predicted_sales:.2f} units**")
+    else:
+        st.error("Error in API request")
+
+# -------------------------
+# Batch prediction
+# -------------------------
+st.subheader("Batch Prediction")
+file = st.file_uploader("Upload CSV file with multiple product-store records", type=["csv"])
+
+if file is not None:
+    if st.button("Predict for Batch", type='primary'):
+        API_BATCH_URL = "https://abhishek1504-learning.hf.space/v1/salesbatch"
+        response = requests.post(API_BATCH_URL, files={"file": file})
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            st.write(result)
+        else:
+            st.error("Error in API request")

requirements.txt

@@ -1,10 +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