Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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"]

app.py

@@ -0,0 +1,70 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+
+# -------------------------
+# Configuration
+# -------------------------
+HF_MODEL_REPO = "VIKASHRAM/superkart"
+MODEL_FILENAME = "best_model_v1.joblib"
+
+# -------------------------
+# Download & load model
+# -------------------------
+model = None
+try:
+    model_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename=MODEL_FILENAME, repo_type="model", token=os.getenv("HF_TOKEN"))
+    model = joblib.load(model_path)
+    st.write(f"Loaded model from Hugging Face: {HF_MODEL_REPO}/{MODEL_FILENAME}")
+except Exception as e:
+    st.warning(f"Could not download model from Hugging Face ({HF_MODEL_REPO}).\nError: {e}\nFalling back to local file if present.")
+    if os.path.exists(MODEL_FILENAME):
+        model = joblib.load(MODEL_FILENAME)
+        st.write(f"Loaded local model file: {MODEL_FILENAME}")
+    else:
+        st.error("Model not available. Please upload the model to HF or place it locally.")
+        st.stop()
+
+# -------------------------
+# Streamlit UI
+# -------------------------
+st.title("SuperKart Sales Prediction App")
+st.write("Predict product sales at different stores using trained ML model.")
+
+# --- Customer details
+Product_Weight = st.number_input("Product Weight", value=12.66)
+Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar","No Sugar","Medium Sugar","High Sugar"])
+Product_Allocated_Area = st.number_input("Allocated Area", value=0.027, step=0.001, format="%.3f")
+Product_Type = st.text_input("Product Type", "Frozen Foods")
+Product_MRP = st.number_input("Product MRP", value=117.08)
+Store_Id = st.text_input("Store Id", "OUT004")
+Store_Establishment_Year = st.number_input("Store Establishment Year", value=2009, step=1)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.text_input("Store Type", "Supermarket Type2")
+
+
+# Assemble input into DataFrame matching training columns (raw — pipeline should handle preprocessing)
+input_df = pd.DataFrame([{
+    "Product_Weight": Product_Weight,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_Type": Product_Type,
+    "Product_MRP": Product_MRP,
+    "Store_Id": Store_Id,
+    "Store_Establishment_Year": Store_Establishment_Year,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type
+}])
+
+st.subheader("Input Preview")
+st.dataframe(input_df.T, width=700)
+
+# Prediction
+if st.button("Predict Sales"):
+    prediction = model.predict(input_df)
+    st.subheader("Prediction Result")
+    st.write(f"Predicted Product Store Sales Total: {prediction[0]:.2f}")

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4