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Dockerfile

@@ -1,20 +1,17 @@
-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
+# 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"]
 
-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"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Store Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for store and product
+
+Product_Weight = st.number_input("Product_Weight", min_value=0.1, max_value=100.0, value=90.0)
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "No Sugar", "Regular"])
+Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.001, max_value=1.0, value=0.045, step=0.001)
+Product_Type = st.selectbox("Product_Type", ["Fruits and Vegetables","Snack Foods","Frozen Foods","Dairy","Household","Baking Goods","Canned","Health and Hygiene",
+                                             "Meat","Soft Drinks","Breads","Hard Drinks","Others","Starchy Foods","Breakfast","Seafood"])
+Product_MRP = st.number_input("Product_MRP", min_value=10.0, max_value=500.0, value=150.75)
+Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1980, max_value=2025, step=1, value=2009)
+Store_Size = st.selectbox("Store_Size", ["High", "Medium", "Small"])
+Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store_Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+
+# Convert user input into a DataFrame
+input_data = 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_Establishment_Year': Store_Establishment_Year,
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+        response = requests.post("https://nishantpathak461-backend_stores.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
+
+        if response.status_code == 200:
+            prediction = response.json()['predicted_sales']
+            st.metric(f"Predicted Sales", f"₹{prediction:.2f}")
+        else:
+            st.error("Error in API request")
+
+
+
+# Section for batch prediction
+#st.subheader("Batch Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+#uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+#if uploaded_file is not None:
+  #  if st.button("Predict Batch"):
+       # response = requests.post("https://<jyotisharma/storesalesfrontend>.hf.space/v1/salesbatch", files={"file": uploaded_file})  # Send file to Flask API
+       # if response.status_code == 200:
+        #    predictions = response.json()
+         #   st.success("Batch predictions completed!")
+          #  st.write(predictions)  # Display the predictions
+     #   else:
+       #     st.error("Error making batch prediction.")

requirements.txt

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