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Dockerfile

@@ -1,16 +1,13 @@
-# Use a minimal base image with Python 3.9 installed
-FROM python:3.9-slim
+FROM python:3.12-slim
 
-# Set the working directory inside the container to /app
+# Set the working directory inside the container
 WORKDIR /app
 
-# Copy all files from the current directory on the host to the container's /app directory
+# Copy all files from current directory to container
 COPY . .
 
-# Install Python dependencies listed in requirements.txt
-RUN pip3 install -r requirements.txt
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade -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
+# Run Flask app with Gunicorn
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_api"]

app.py

@@ -1,82 +1,89 @@
 
-import streamlit as st
-import requests
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import polars as pl  # Polars instead of pandas
+from flask import Flask, request, jsonify  # Flask API
 
-st.title("SuperKart Sales Prediction App")
+# Initialize Flask app with a name
+superkart_api = Flask("SuperKart_API")
 
-# Input fields for product and store data
-Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
+# Load trained model pipeline
+model = joblib.load("Gradient-Boosting-Regressor-Tuned.joblib")
 
-Product_Sugar_Content = st.selectbox(
-    "Product Sugar Content",
-    ["Low Sugar", "Regular", "No Sugar"]
-)
+# Home route
+@superkart_api.get('/')
+def home():
+    return "SuperKart Sales Prediction API is running"
 
-Product_Allocated_Area = st.number_input(
-    "Product Allocated Area",
-    min_value=0.0,
-    value=10.0
-)
+# Prediction endpoint
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    # Get JSON input
+    data = request.get_json()
 
-Product_MRP = st.number_input(
-    "Product MRP",
-    min_value=0.0,
-    value=100.0
-)
+    # Extract features
+    required_fields = [
+        'Product_Weight',
+        'Product_Sugar_Content',
+        'Product_Allocated_Area',
+        'Product_MRP',
+        'Store_Size',
+        'Store_Location_City_Type',
+        'Store_Type',
+        'Product_Id_char',
+        'Store_Age_Years',
+        'Product_Type_Category'
+    ]
 
-Store_Size = st.selectbox(
-    "Store Size",
-    ["Small", "Medium", "High"]
-)
+    # Check for missing or null values
+    missing_fields = [
+        field for field in required_fields
+        if field not in data or data[field] is None or data[field] == ""
+    ]
 
-Store_Location_City_Type = st.selectbox(
-    "Store Location City Type",
-    ["Tier 1", "Tier 2", "Tier 3"]
-)
+    # If any field is missing → return error
+    if missing_fields:
+        return jsonify({
+            "error": "Missing or empty fields in request body",
+            "missing_fields": missing_fields,
+            "expected_format": {
+                "Product_Weight": "float",
+                "Product_Sugar_Content": "string",
+                "Product_Allocated_Area": "float",
+                "Product_MRP": "float",
+                "Store_Size": "string",
+                "Store_Location_City_Type": "string",
+                "Store_Type": "string",
+                "Product_Id_char": "string",
+                "Store_Age_Years": "float",
+                "Product_Type_Category": "string"
+            }
+        }), 400
 
-Store_Type = st.selectbox(
-    "Store Type",
-    ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"]
-)
+    # Safe to build sample
+    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']
+    }
 
-Product_Id_char = st.text_input(
-    "Product ID Character",
-    value="P001"
-)
+    # Convert to Polars DataFrame
+    input_data = pl.DataFrame([sample])
 
-Store_Age_Years = st.number_input(
-    "Store Age (Years)",
-    min_value=0,
-    value=10
-)
+    # Convert to numpy for sklearn compatibility (safe step)
+    prediction = model.predict(input_data.to_pandas()).tolist()[0]
 
-Product_Type_Category = st.selectbox(
-    "Product Type Category",
-    ["Food", "Drinks", "Household", "Health", "Other"]
-)
+    return jsonify({'Sales': prediction})
 
-product_data = {
-    "Product_Weight": Product_Weight,
-    "Product_Sugar_Content": Product_Sugar_Content,
-    "Product_Allocated_Area": Product_Allocated_Area,
-    "Product_MRP": Product_MRP,
-    "Store_Size": Store_Size,
-    "Store_Location_City_Type": Store_Location_City_Type,
-    "Store_Type": Store_Type,
-    "Product_Id_char": Product_Id_char,
-    "Store_Age_Years": Store_Age_Years,
-    "Product_Type_Category": Product_Type_Category
-}
 
-if st.button("Predict", type='primary'):
-    response = requests.post(
-        "https://scottizi-greatlearningdeploymentproject.hf.space/v1/predict",
-        json=product_data
-    )
-
-    if response.status_code == 200:
-        result = response.json()
-        predicted_sales = result["Sales"]
-        st.write(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
-    else:
-        st.error("Error in API request")
+# Run app
+if __name__ == '__main__':
+    superkart_api.run(debug=True)

requirements.txt

@@ -1,2 +1,156 @@
-requests==2.32.3
-streamlit==1.45.0
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.13.0
+argon2-cffi==25.1.0
+argon2-cffi-bindings==25.1.0
+arrow==1.4.0
+asttokens==3.0.1
+async-lru==2.3.0
+attrs==26.1.0
+babel==2.18.0
+beautifulsoup4==4.14.3
+bleach==6.3.0
+blinker==1.9.0
+certifi==2026.4.22
+cffi==2.0.0
+charset-normalizer==3.4.7
+click==8.3.3
+comm==0.2.3
+contourpy==1.3.3
+cycler==0.12.1
+dacite==1.9.2
+debugpy==1.8.20
+decorator==5.2.1
+defusedxml==0.7.1
+executing==2.2.1
+fastjsonschema==2.21.2
+fg-data-profiling==4.19.1
+filelock==3.29.0
+filetype==1.2.0
+flask==3.1.3
+fonttools==4.62.1
+fqdn==1.5.1
+fsspec==2026.4.0
+h11==0.16.0
+hf-xet==1.5.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==1.14.0
+idna==3.13
+imagehash==4.3.2
+ipykernel==7.2.0
+ipython==9.13.0
+ipython-pygments-lexers==1.1.1
+ipywidgets==8.1.8
+isoduration==20.11.0
+itsdangerous==2.2.0
+jedi==0.20.0
+jinja2==3.1.6
+joblib==1.5.3
+json5==0.14.0
+jsonpointer==3.1.1
+jsonschema==4.26.0
+jsonschema-specifications==2025.9.1
+jupyter-client==8.8.0
+jupyter-core==5.9.1
+jupyter-events==0.12.1
+jupyter-lsp==2.3.1
+jupyter-server==2.18.2
+jupyter-server-terminals==0.5.4
+jupyterlab==4.5.7
+jupyterlab-pygments==0.3.0
+jupyterlab-server==2.28.0
+jupyterlab-widgets==3.0.16
+kiwisolver==1.5.0
+lark==1.3.1
+llvmlite==0.45.1
+markdown-it-py==4.2.0
+markupsafe==3.0.3
+matplotlib==3.10.0
+matplotlib-inline==0.2.2
+mdurl==0.1.2
+minify-html==0.18.1
+mistune==3.2.1
+multimethod==1.12
+nbclient==0.10.4
+nbconvert==7.17.1
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.6.1
+notebook==7.5.6
+notebook-shim==0.2.4
+numba==0.62.1
+numpy==2.3.5
+nvidia-nccl-cu12==2.30.4
+packaging==26.2
+pandas==2.3.3
+pandocfilters==1.5.1
+parso==0.8.7
+patsy==1.0.2
+pexpect==4.9.0
+phik==0.12.5
+pillow==12.2.0
+pip==26.1.1
+platformdirs==4.9.6
+polars==1.40.1
+polars-runtime-32==1.40.1
+prometheus-client==0.25.0
+prompt-toolkit==3.0.52
+psutil==7.2.2
+ptyprocess==0.7.0
+pure-eval==0.2.3
+puremagic==2.2.0
+pyarrow==24.0.0
+pycparser==3.0
+pydantic==2.13.4
+pydantic-core==2.46.4
+pygments==2.20.0
+pyparsing==3.3.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.2.2
+python-json-logger==4.1.0
+pytz==2026.2
+pywavelets==1.9.0
+pyyaml==6.0.3
+pyzmq==27.1.0
+referencing==0.37.0
+requests==2.33.1
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rfc3987-syntax==1.1.0
+rich==15.0.0
+rpds-py==0.30.0
+scikit-learn==1.8.0
+scipy==1.16.3
+seaborn==0.13.2
+send2trash==2.1.0
+setuptools==75.8.0
+shellingham==1.5.4
+six==1.17.0
+soupsieve==2.8.3
+stack-data==0.6.3
+statsmodels==0.14.6
+terminado==0.18.1
+threadpoolctl==3.6.0
+tinycss2==1.4.0
+tornado==6.5.5
+tqdm==4.67.3
+traitlets==5.15.0
+typeguard==4.5.1
+typer==0.25.1
+typing-extensions==4.15.0
+typing-inspection==0.4.2
+tzdata==2026.2
+uri-template==1.3.0
+urllib3==2.7.0
+visions==0.8.1
+wcwidth==0.7.0
+webcolors==25.10.0
+webencodings==0.5.1
+websocket-client==1.9.0
+werkzeug==3.1.8
+wheel==0.47.0
+widgetsnbextension==4.0.15
+wordcloud==1.9.6
+xgboost==3.2.0
+gunicorn==21.2.0