Upload folder using huggingface_hub

Dockerfile

@@ -1,4 +1,5 @@
-FROM python:3.13.5-slim
+
+FROM python:3.10-slim
 
 WORKDIR /app
 
@@ -17,4 +18,4 @@ 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"]
+ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.headless=true", "--browser.gatherUsageStats=false", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,84 @@
+import os
+os.environ["STREAMLIT_SERVER_HEADLESS"] = "true"
+
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# ---- Streamlit bootstrap ----
+st.empty()
+st.set_page_config(page_title="SuperKart Sales Prediction")
+st.set_option("browser.gatherUsageStats", False)
+
+# ---- Read secrets ----
+Repo_ID = os.getenv("Repo_ID")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+if not Repo_ID:
+    st.error("❌ Repo_ID secret is missing in HF Space")
+    st.stop()
+
+# ---- Render UI immediately ----
+st.title("🛒 SuperKart Sales Prediction")
+st.write("✅ UI rendered successfully")
+
+# ---- Load model lazily ----
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id=Repo_ID,
+        filename="best_superkart_sales_model_v1.joblib",
+        repo_type="model",
+        token=HF_TOKEN
+    )
+    return joblib.load(model_path)
+
+# ---- Load model AFTER UI ----
+try:
+    with st.spinner("Loading ML model…"):
+        model = load_model()
+        st.success("✅ Model loaded successfully")
+except Exception as e:
+    st.error("❌ Model failed to load")
+    st.exception(e)
+    st.stop()
+
+# ---- UI ----
+st.write("""
+This application predicts the **total product sales** for SuperKart
+based on product characteristics and store attributes.
+""")
+
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_type = st.selectbox("Product Type", [
+    "Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables",
+    "Baking Goods", "Frozen Foods", "Health and Hygiene",
+    "Canned", "Household", "Snack Foods", "Others"
+])
+store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004", "OUT005"])
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+
+product_weight = st.number_input("Product Weight (kg)", 0.1, 50.0, 10.0)
+product_allocated_area = st.number_input("Product Allocated Area", 0.001, 1.0, 0.05, step=0.001)
+product_mrp = st.number_input("Product MRP", 1.0, 1000.0, 100.0)
+store_est_year = st.number_input("Store Establishment Year", 1950, 2025, 2005)
+
+input_data = pd.DataFrame([{
+    "Product_Weight": product_weight,
+    "Product_Allocated_Area": product_allocated_area,
+    "Product_MRP": product_mrp,
+    "Store_Establishment_Year": store_est_year,
+    "Product_Sugar_Content": product_sugar_content,
+    "Product_Type": product_type,
+    "Store_Id": store_id,
+    "Store_Size": store_size,
+    "Store_Location_City_Type": store_city_type,
+    "Store_Type": store_type
+}])
+
+if st.button("Predict Sales"):
+    prediction = model.predict(input_data)[0]
+    st.success(f"Estimated Product Sales: **₹ {prediction:,.2f}**")

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+huggingface_hub==0.32.6
+datasets==3.6.0
+pandas==2.2.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+mlflow==3.0.1
+streamlit==1.28.0

src/streamlit_app.py

@@ -1,40 +1,83 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+os.environ["STREAMLIT_SERVER_HEADLESS"] = "true"
+
 import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# ---- Streamlit bootstrap ----
+st.set_page_config(page_title="SuperKart Sales Prediction")
+
+# ---- Read secrets ----
+Repo_ID = os.getenv("Repo_ID")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+if not Repo_ID:
+    st.error("Repo_ID secret is missing in HF Space")
+    st.stop()
+
+# ---- Render UI immediately ----
+st.title("🛒 SuperKart Sales Prediction")
+st.write("UI rendered successfully")
+
+# ---- Load model lazily ----
+
+repo_path=f"{Repo_ID}/superkart-sales-model"
+
+@st.cache_resource
+def load_model():
+    try:
+        model_path = hf_hub_download(
+            repo_id=repo_path,
+            filename="best_superkart_sales_model_v1.joblib",
+            repo_type="model",
+            token=HF_TOKEN
+        )
+        return joblib.load(model_path)
+    except Exception as e:
+        st.error("Failed to download model from Hugging Face")
+        st.exception(e)
+        st.stop()
+
+model = load_model()
+
+
+# ---- UI ----
+st.write("""
+This application predicts the **total product sales** for SuperKart
+based on product characteristics and store attributes.
+""")
+
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_type = st.selectbox("Product Type", [
+    "Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables",
+    "Baking Goods", "Frozen Foods", "Health and Hygiene",
+    "Canned", "Household", "Snack Foods", "Others"
+])
+store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004", "OUT005"])
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+
+product_weight = st.number_input("Product Weight (kg)", 0.1, 50.0, 10.0)
+product_allocated_area = st.number_input("Product Allocated Area", 0.001, 1.0, 0.05, step=0.001)
+product_mrp = st.number_input("Product MRP", 1.0, 1000.0, 100.0)
+store_est_year = st.number_input("Store Establishment Year", 1950, 2025, 2005)
+
+input_data = pd.DataFrame([{
+    "Product_Weight": product_weight,
+    "Product_Allocated_Area": product_allocated_area,
+    "Product_MRP": product_mrp,
+    "Store_Establishment_Year": store_est_year,
+    "Product_Sugar_Content": product_sugar_content,
+    "Product_Type": product_type,
+    "Store_Id": store_id,
+    "Store_Size": store_size,
+    "Store_Location_City_Type": store_city_type,
+    "Store_Type": store_type
+}])
 
-"""
-# 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])),
-    ))
+if st.button("Predict Sales"):
+    prediction = model.predict(input_data)[0]
+    st.success(f"Estimated Product Sales: **₹ {prediction:,.2f}**")