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Dockerfile

@@ -1,20 +1,13 @@
-FROM python:3.13.5-slim
+FROM python:3.9-slim
 
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
 
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
+COPY . .
 
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,40 @@
+import streamlit as st
+import requests
+
+# Backend API URL (replace with your HF backend space URL when deployed)
+BACKEND_URL = "http://127.0.0.1:7860/predict"
+
+st.title("🛒 SuperKart Sales Forecast")
+st.write("Enter product & store details to predict sales revenue.")
+
+# Inputs (simplified — you can add all features as per dataset)
+Product_Weight = st.number_input("Product Weight", min_value=0.1, max_value=100.0, step=0.1)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["low sugar", "regular", "no sugar"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, step=0.01)
+Product_Type = st.selectbox("Product Type", ["snack foods", "meat", "dairy", "soft drinks", "others"])
+Product_MRP = st.number_input("Product MRP", min_value=1.0, max_value=1000.0, step=1.0)
+Store_Size = st.selectbox("Store Size", ["High", "Medium", "Low"])
+Store_Location_City_Type = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
+
+if st.button("Predict Sales"):
+    input_data = {
+        "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_Size": Store_Size,
+        "Store_Location_City_Type": Store_Location_City_Type,
+        "Store_Type": Store_Type,
+    }
+
+    try:
+        response = requests.post(BACKEND_URL, json=input_data)
+        if response.status_code == 200:
+            result = response.json()
+            st.success(f"✅ Predicted Sales: {result['prediction']:.2f}")
+        else:
+            st.error(f"Error: {response.text}")
+    except Exception as e:
+        st.error(f"Connection Error: {e}")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2