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Dockerfile

@@ -1,13 +1,16 @@
-FROM python:3.12-slim
+# 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 current directory to container
+# Copy all files from the current directory on the host to the container's /app directory
 COPY . .
 
-# Install dependencies
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
 
-# Run Flask app with Gunicorn
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_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"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -1,89 +1,82 @@
 
-# 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
+import streamlit as st
+import requests
 
-# Initialize Flask app with a name
-superkart_api = Flask("SuperKart_API")
+st.title("SuperKart Sales Prediction App")
 
-# Load trained model pipeline
-model = joblib.load("Gradient-Boosting-Regressor-Tuned.joblib")
+# Input fields for product and store data
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
 
-# Home route
-@superkart_api.get('/')
-def home():
-    return "SuperKart Sales Prediction API is running"
+Product_Sugar_Content = st.selectbox(
+    "Product Sugar Content",
+    ["Low Sugar", "Regular", "No Sugar"]
+)
 
-# Prediction endpoint
-@superkart_api.post('/v1/predict')
-def predict_sales():
-    # Get JSON input
-    data = request.get_json()
+Product_Allocated_Area = st.number_input(
+    "Product Allocated Area",
+    min_value=0.0,
+    value=10.0
+)
 
-    # 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'
-    ]
+Product_MRP = st.number_input(
+    "Product MRP",
+    min_value=0.0,
+    value=100.0
+)
 
-    # 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_Size = st.selectbox(
+    "Store Size",
+    ["Small", "Medium", "High"]
+)
 
-    # 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_Location_City_Type = st.selectbox(
+    "Store Location City Type",
+    ["Tier 1", "Tier 2", "Tier 3"]
+)
 
-    # 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']
-    }
+Store_Type = st.selectbox(
+    "Store Type",
+    ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"]
+)
 
-    # Convert to Polars DataFrame
-    input_data = pl.DataFrame([sample])
+Product_Id_char = st.text_input(
+    "Product ID Character",
+    value="P001"
+)
 
-    # Convert to numpy for sklearn compatibility (safe step)
-    prediction = model.predict(input_data.to_pandas()).tolist()[0]
+Store_Age_Years = st.number_input(
+    "Store Age (Years)",
+    min_value=0,
+    value=10
+)
 
-    return jsonify({'Sales': prediction})
+Product_Type_Category = st.selectbox(
+    "Product Type Category",
+    ["Food", "Drinks", "Household", "Health", "Other"]
+)
 
+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
+}
 
-# Run app
-if __name__ == '__main__':
-    superkart_api.run(debug=True)
+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")

requirements.txt

@@ -1,155 +1,2 @@
-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-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
+requests==2.32.3
+streamlit==1.45.0