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Dockerfile (2)

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+# Base image: lightweight Python 3.9
+FROM python:3.9-slim-buster
+
+# Set container working directory
+WORKDIR /app
+
+# Copy project files into the container
+COPY . /app
+
+# Environment variable for Streamlit configuration
+ENV STREAMLIT_CONFIG_DIR=/app/.streamlit
+
+# Create the Streamlit configuration directory
+RUN mkdir -p ${STREAMLIT_CONFIG_DIR}
+
+# Install dependencies (no cache)
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Default command to run the Streamlit app
+CMD ["streamlit", "run", "app.py", \
+     "--server.port=8501", \
+     "--server.address=0.0.0.0", \
+     "--server.enableXsrfProtection=false"]

app (2).py

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+import streamlit as st
+import requests
+import pandas as pd
+
+# Configure page
+st.set_page_config(page_title="SuperKart Sales Estimator", layout="centered")
+st.title("SuperKart Sales Forecast Tool")
+
+# Input form
+st.subheader("Provide Product & Store Information")
+
+col_left, col_right = st.columns(2)
+
+with col_left:
+    weight = st.number_input("Product Weight (in kg)", min_value=0.0, step=0.1, value=1.0)
+    sugar_level = st.selectbox("Sugar Content", ["Low", "Medium", "Regular", "reg"])
+    allocated_area = st.number_input("Display Area (sq.m)", min_value=0.01, step=0.01, value=1.5)
+    category = st.selectbox("Category", [
+        "Baking Goods", "Canned", "Dairy", "Frozen Foods", "Health and Hygiene",
+        "Household", "Meat", "Others", "Snack Foods", "Soft Drinks", "Starchy Foods"
+    ])
+    mrp = st.number_input("MRP ($)", min_value=1.0, max_value=1000.0, step=1.0, value=100.0)
+
+with col_right:
+    establishment_year = st.number_input("Year Store Opened", min_value=1990, max_value=2025, step=1, value=2010)
+    store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+    city_tier = st.selectbox("Location Tier", ["Tier 1", "Tier 2", "Tier 3"])
+    store_format = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Grocery Store"])
+
+# Create JSON payload
+payload = {
+    "Product_Weight": weight,
+    "Product_Sugar_Content": sugar_level,
+    "Product_Allocated_Area": allocated_area,
+    "Product_Type": category,
+    "Product_MRP": mrp,
+    "Store_Establishment_Year": establishment_year,
+    "Store_Size": store_size,
+    "Store_Location_City_Type": city_tier,
+    "Store_Type": store_format
+}
+
+# Button to call API
+if st.button("Get Sales Prediction"):
+    with st.spinner("Processing your request..."):
+        try:
+            api_url = "https://mohith96-SuperKartBackend.hf.space/predict"
+            response = requests.post(api_url, json=payload, timeout=10)
+            if response.status_code == 200:
+                result = response.json()
+                if "Predicted_Sales" in result:
+                    st.success(f"Predicted Sales: $ {result['Predicted_Sales']:.2f}")
+                else:
+                    st.warning(f"Unexpected response: {result}")
+            else:
+                st.error(f"API returned {response.status_code}: {response.text}")
+        except Exception as error:
+            st.error(f"Failed to reach API: {error}")

random_forest_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84816b0dbb03c08b0083fe89dae95480cbd696be411c267fff7abf41370f1768
+size 34999706

requirements (2).txt

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+streamlit
+pandas
+numpy
+requests
+plotly