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Dockerfile

@@ -1,20 +1,14 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
 
+# 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
-
-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"]
+# 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 +1,72 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# ==================================
+# Streamlit UI
+# ==================================
+st.title("Product Store Sales Prediction App")
+st.write("This app predicts total sales for a product in a store based on product and store features.")
+st.write("Adjust the sliders and input fields below to get a prediction for a single product-store combination.")
+
+# -------------------------
+# Single record inputs
+# -------------------------
+Product_Id = st.number_input("Product ID", min_value=1, step=1)
+Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.01, value=0.5)
+Product_Sugar_Content = st.number_input("Sugar Content (%)", min_value=0.0, max_value=100.0, step=0.1, value=10.0)
+Product_Allocated_Area = st.number_input("Allocated Area (sq.m)", min_value=0.0, step=0.1, value=50.0)
+Product_Type = st.text_input("Product Type", value="Food")
+Product_MRP = st.number_input("Maximum Retail Price (MRP)", min_value=0.0, step=0.1, value=100.0)
+
+Store_Id = st.number_input("Store ID", min_value=1, step=1)
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, step=1, value=2000)
+Store_Size = st.number_input("Store Size (sq.m)", min_value=0.0, step=0.1, value=100.0)
+Store_Location_City_Type = st.selectbox("City Type", ["A", "B", "C"])
+Store_Type = st.text_input("Store Type", value="Supermarket")
+
+# -------------------------
+# Single prediction
+# -------------------------
+if st.button("Predict Sales", type='primary'):
+    input_data = {
+        'Product_Id': Product_Id,
+        '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
+    }
+
+    # Replace with your Hugging Face Space endpoint
+    API_URL = "https://abhishek1504-learning-frontend.hf.space/v1/sales"
+
+    response = requests.post(API_URL, json=input_data)
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Predicted_Product_Store_Sales_Total"]
+        st.success(f" Predicted Total Product-Store Sales: **{predicted_sales:.2f} units**")
+    else:
+        st.error("Error in API request")
+
+# -------------------------
+# Batch prediction
+# -------------------------
+st.subheader("Batch Prediction")
+file = st.file_uploader("Upload CSV file with multiple product-store records", type=["csv"])
+
+if file is not None:
+    if st.button("Predict for Batch", type='primary'):
+        API_BATCH_URL = "https://abhishek1504-learning-frontend.hf.space/v1/salesbatch"
+        response = requests.post(API_BATCH_URL, files={"file": file})
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            st.write(result)
+        else:
+            st.error("Error in API request")

requirements.txt

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