🔥 Full overwrite: deploy SuperKart UI

Dockerfile

@@ -1,21 +0,0 @@
-FROM python:3.9-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -1,19 +0,0 @@
----
-title: SuperKartUI
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).

app.py

@@ -1,31 +0,0 @@
-from flask import Flask, request, jsonify
-import pandas as pd
-import joblib
-
-app = Flask(__name__)
-
-MODEL_PATH = "best_sales_forecast_model.pkl"
-model = joblib.load(MODEL_PATH)
-
-FEATURE_COLUMNS = [
-    "Product_Weight","Product_Allocated_Area","Product_MRP",
-    "Store_Establishment_Year","Store_Size","Store_Location_City_Type",
-    "Store_Type","Product_Prefix","Product_Num","Store_Age"
-]
-
-@app.route("/")
-def home():
-    return "SuperKart Sales Forecast API is up."
-
-@app.route("/predict", methods=["POST"])
-def predict():
-    payload = request.get_json(force=True)
-    df = pd.DataFrame(payload["data"])
-    X = df[FEATURE_COLUMNS]
-    preds = model.predict(X)
-    return jsonify({"predictions": preds.tolist()})
-
-if __name__ == "__main__":
-    import os
-    port = int(os.environ.get("PORT", 5000))
-    app.run(host="0.0.0.0", port=port)

best_sales_forecast_model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6f13226779001590e025ef62b31e40021305709d38a5ab555e18bc1611208c97
-size 49997859

final_random_forest_model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7bbd80f25f24f85f9461b8ed48e52593e1916c60d892034781d145819376e721
-size 49995363

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))

streamlit_app.py

@@ -3,51 +3,51 @@ import pandas as pd
 import requests
 import os
 
-# Page config
 st.set_page_config(page_title="SuperKart Forecast", layout="centered")
 st.title("🛒 SuperKart Quarterly Sales Forecast")
-st.write("Fill in the product and store details below, then click 🔮 Predict.")
+st.write("Enter details below, then click 🔮 Predict.")
 
-# Backend URL
 BACKEND_URL = os.getenv("BACKEND_URL", "$BACKEND_URL")
 
-# Input form
 with st.form("forecast_form"):
     c1, c2 = st.columns(2)
     with c1:
-        pw   = st.number_input("Product Weight (kg)", 0.0, 100.0, 12.5, 0.1)
-        pa   = st.number_input("Allocated Area Ratio", 0.0, 1.0, 0.08, 0.005)
-        mrp  = st.number_input("Product MRP (₹)", 0.0, 1000.0, 50.0, 1.0)
-        year = st.number_input("Store Established Year", 1900, 2025, 2015, 1)
-        size = st.selectbox("Store Size", ["low", "medium", "high"])
+        pw   = st.number_input("Product Weight (kg)", 0.0,100.0,12.5,0.1)
+        pa   = st.number_input("Allocated Area Ratio",0.0,1.0,0.08,0.005)
+        mrp  = st.number_input("Product MRP (₹)",     0.0,1000.0,50.0,1.0)
+        year = st.number_input("Store Established Year",1900,2025,2015,1)
+        size = st.selectbox("Store Size", ["low","medium","high"])
     with c2:
-        city   = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+        city   = st.selectbox("City Tier", ["Tier 1","Tier 2","Tier 3"])
         stype  = st.selectbox("Store Type", [
-            "Departmental Store", "Supermarket Type 1",
-            "Supermarket Type 2", "Food Mart"
+            "Departmental Store","Supermarket Type 1",
+            "Supermarket Type 2","Food Mart"
         ])
-        prefix = st.text_input("Product Prefix", "FD")
-        pnum   = st.number_input("Product Numeric ID", 0, 100000, 6114, 1)
-        age    = st.number_input("Store Age (yrs)", 0, 50, int(pd.Timestamp.now().year - year), 1)
-    submitted = st.form_submit_button(" Predict")
+        prefix= st.text_input("Product Prefix","FD")
+        pnum  = st.number_input("Product Numeric ID",0,100000,6114,1)
+        age   = st.number_input(
+                  "Store Age (yrs)",0,50,
+                  int(pd.Timestamp.now().year - year),1
+                )
+    submit = st.form_submit_button("🔮 Predict")
 
-if submitted:
-    payload = {"data": [{
-        "Product_Weight": pw,
-        "Product_Allocated_Area": pa,
-        "Product_MRP": mrp,
-        "Store_Establishment_Year": year,
-        "Store_Size": size,
-        "Store_Location_City_Type": city,
-        "Store_Type": stype,
-        "Product_Prefix": prefix,
-        "Product_Num": pnum,
-        "Store_Age": age
+if submit:
+    payload={"data":[{
+        "Product_Weight":pw,
+        "Product_Allocated_Area":pa,
+        "Product_MRP":mrp,
+        "Store_Establishment_Year":year,
+        "Store_Size":size,
+        "Store_Location_City_Type":city,
+        "Store_Type":stype,
+        "Product_Prefix":prefix,
+        "Product_Num":pnum,
+        "Store_Age":age
     }]}
     try:
-        resp = requests.post(f"{BACKEND_URL}/predict", json=payload, timeout=10)
-        resp.raise_for_status()
-        pred = resp.json()["predictions"][0]
-        st.success(f" Forecasted Sales: ₹{pred:,.2f}")
+        r = requests.post(f"{BACKEND_URL}/predict", json=payload, timeout=10)
+        r.raise_for_status()
+        pred = r.json()["predictions"][0]
+        st.success(f"🚀 Forecasted Sales: ₹{pred:,.2f}")
     except Exception as e:
-        st.error(f" Prediction error: {e}")
+        st.error(f"❌ Prediction error: {e}")