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import joblib
import pandas as pd
from flask import Flask, request, jsonify
# -----------------------------
# Load pipeline (preprocessor + model)
# -----------------------------
MODEL_PATH = "superkart_sales_model_v1.joblib"
model = joblib.load(MODEL_PATH)
# -----------------------------
# Helpers: map strings -> ordinal codes (if user sends strings)
# Your training expected numeric Store_Size & City_Type
# -----------------------------
SIZE_MAP = {"Small": 1, "Medium": 2, "High": 3}
CITY_MAP = {"Tier 3": 1, "Tier 2": 2, "Tier 1": 3}
# Required columns in the SAME names used during training
EXPECTED_COLUMNS = [
"Product_Weight",
"Product_Allocated_Area",
"Product_MRP",
"Store_Establishment_Year",
"Store_Size", # numeric 1/2/3 OR strings -> mapped
"Store_Location_City_Type", # numeric 1/2/3 OR strings -> mapped
"Product_Sugar_Content", # categorical
"Product_Type", # categorical
"Store_Type" # categorical
]
def coerce_and_validate(df: pd.DataFrame) -> pd.DataFrame:
# Keep only expected cols, in order
df = df.copy()
missing = [c for c in EXPECTED_COLUMNS if c not in df.columns]
if missing:
raise ValueError(f"Missing required columns: {missing}")
df = df[EXPECTED_COLUMNS]
# Map strings for ordinal columns if needed
if df["Store_Size"].dtype == object:
df["Store_Size"] = df["Store_Size"].map(SIZE_MAP)
if df["Store_Location_City_Type"].dtype == object:
df["Store_Location_City_Type"] = df["Store_Location_City_Type"].map(CITY_MAP)
# Final sanity: ensure numeric for ordinal columns
for col in ["Store_Size", "Store_Location_City_Type",
"Product_Weight", "Product_Allocated_Area", "Product_MRP", "Store_Establishment_Year"]:
df[col] = pd.to_numeric(df[col], errors="coerce")
# Basic NA handling (model was trained on clean data; here we drop rows with NA)
if df.isna().any().any():
# You can switch to imputation if preferred
df = df.dropna(axis=0).copy()
return df
# -----------------------------
# Flask app
# -----------------------------
app = Flask("SuperKart Sales Predictor")
@app.get("/")
def home():
return "SuperKart Sales Prediction API is up!"
@app.post("/v1/predict")
def predict_single():
"""
JSON body example:
{
"Product_Weight": 12.5,
"Product_Allocated_Area": 30,
"Product_MRP": 199.0,
"Store_Establishment_Year": 2008,
"Store_Size": "Medium", // or 2
"Store_Location_City_Type": "Tier 1", // or 3
"Product_Sugar_Content": "Regular",
"Product_Type": "Snack Foods",
"Store_Type": "Supermarket Type 1"
}
"""
try:
data = request.get_json(force=True)
df = pd.DataFrame([data])
df = coerce_and_validate(df)
if df.empty:
return jsonify({"error": "Input invalid or resulted in empty rows after cleaning."}), 400
pred = float(model.predict(df)[0])
return jsonify({"Predicted_Product_Store_Sales_Total": round(pred, 2)})
except Exception as e:
return jsonify({"error": str(e)}), 400
@app.post("/v1/predict_batch")
def predict_batch():
"""
Form-data upload: file=CSV
CSV must include the EXPECTED_COLUMNS headers.
"""
try:
if "file" not in request.files:
return jsonify({"error": "Please upload a CSV file with key 'file'."}), 400
file = request.files["file"]
df = pd.read_csv(file)
df_clean = coerce_and_validate(df)
if df_clean.empty:
return jsonify({"error": "All rows invalid or empty after cleaning."}), 400
preds = model.predict(df_clean)
out = df.copy()
out["Predicted_Product_Store_Sales_Total"] = preds
# Return top rows to avoid huge payloads
return out.head(50).to_json(orient="records")
except Exception as e:
return jsonify({"error": str(e)}), 400
if __name__ == "__main__":
# For local dev (Colab), use:
app.run(host="0.0.0.0", port=7860, debug=True)
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