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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# 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"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+import json # Used for debugging output
+
+# CRITICAL FIX: Use the actual URL of your running Docker Backend Space
+# Replace 'Himadri1102-SuperKart-Model-Backend11' with the exact URL slug if different
+BACKEND_URL = "https://Himadri1102-SuperKart-Model-Backend11.hf.space"
+SINGLE_PREDICT_ENDPOINT = f"{BACKEND_URL}/v1/sales"
+BATCH_PREDICT_ENDPOINT = f"{BACKEND_URL}/v1/salesbatch"
+
+# --- UI Setup ---
+st.title("SuperKart Sales Revenue Forecast")
+st.markdown("Predicts the total sales revenue for a specific product and store combination.")
+
+st.subheader("1. Product and Store Inputs")
+st.markdown("⚠️ **WARNING**: The model requires ALL 29+ features (scaled/encoded). This UI collects only key features and defaults the rest for demonstration.")
+
+# --- Collect Key User Input (Based on Bivariate Analysis) ---
+product_mrp = st.number_input("Product MRP (Maximum Retail Price)", min_value=30.0, max_value=300.0, value=150.0, step=5.0)
+product_weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0, value=12.0, step=0.1)
+store_age = st.number_input("Store Age (Years)", min_value=5, max_value=40, value=20)
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+
+# --- Feature Preparation (Simulating Preprocessing Pipeline) ---
+
+# CRITICAL: This mapping MUST match the Label Encoding in your training/pipeline code
+store_size_encoded = {'Small': 0, 'Medium': 1, 'High': 2}[store_size]
+
+# In a real pipeline, the model input must be SCALED (StandardScaler).
+# For simplicity here, we assume the API pipeline handles the scaling, 
+# but the raw inputs are passed. However, the final pipeline expects the scaled value.
+# To make this functional, we must provide mock OHE values.
+
+def create_payload(mrp, weight, age, size_enc):
+    """
+    Creates a full feature payload, defaulting OHE features to zero, 
+    and passing the key raw numerical/encoded values.
+    """
+    # NOTE: In a real environment, you must send the actual SCALED values
+    # of MRP, Weight, and Age, not the raw ones.
+    payload = {
+        'Product_MRP': mrp,
+        'Product_Weight': weight,
+        'Store_Age': age,
+        'Store_Size_Encoded': size_enc, # This is the encoded value
+
+        # Mocking the OHE features (defaulting most categories to False/0)
+        'Product_Sugar_Content_No Sugar': False,
+        'Product_Sugar_Content_Regular': True, # Default to Regular for testing
+        'Product_Type_Snack Foods': True,      # Default to Snack Foods for testing
+        'Product_Type_Baking Goods': False,
+        'Product_Type_Breads': False,
+        'Store_Location_City_Type_Tier 2': True,
+        'Store_Type_Supermarket Type1': True,
+        # ... and all other 20+ OHE features from your model input list...
+        # We only list a few for demonstration clarity.
+    }
+    
+    # You need ALL 29+ feature keys here for the API call to work correctly.
+    # The current list is incomplete and will likely fail the model predict step.
+    return payload
+
+
+# --- Prediction Logic ---
+if st.button("Predict Sales Revenue"):
+    
+    # 1. Create the full data payload
+    input_payload = create_payload(product_mrp, product_weight, store_age, store_size_encoded)
+    
+    # 2. Call the Backend API
+    response = requests.post(SINGLE_PREDICT_ENDPOINT, json=input_payload)
+    
+    if response.status_code == 200:
+        try:
+            prediction = response.json()['Predicted Total Sales']
+            st.success(f"📈 Predicted Sales Revenue: **${prediction:,.2f}**")
+        except KeyError:
+             st.error("Prediction successful but key 'Predicted Total Sales' was missing from API response.")
+             st.json(response.json())
+    else:
+        st.error(f"Error making prediction. Status Code: {response.status_code}")
+        st.json(response.json())
+
+
+# --- Batch Prediction Section ---
+st.subheader("2. Batch Prediction (CSV Upload)")
+
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+if uploaded_file is not None:
+    if st.button("Predict Batch Sales"):
+        response = requests.post(BATCH_PREDICT_ENDPOINT, files={"file": uploaded_file})
+        
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.dataframe(pd.DataFrame(list(predictions.items()), columns=['ID', 'Predicted Sales']))
+        else:
+            st.error(f"Error making batch prediction. Status Code: {response.status_code}")
+            st.json(response.json())

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2