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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=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,45 @@
+
+import streamlit as st
+import requests
+
+st.title("SuperKart Sales Forecasting App")
+
+# Input fields for product and store data
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
+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, value=16.0)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=250.0)
+
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Departmental Store", "Food Mart","Supermarket Type1"," Supermarket Type2"])
+
+Product_Id_char = st.selectbox("Product Id Char", ["FD", "DR", "NC"])
+Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=14, step=1)
+Product_Type_Category = st.selectbox("Product Type Category", ["Perishable", "Non Perishable"])
+
+product_data = {
+    "Product_Weight": Product_Weight,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_MRP": Product_MRP,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Product_Id_char": Product_Id_char,
+    "Store_Age_Years": Store_Age_Years,
+    "Product_Type_Category": Product_Type_Category
+}
+
+if st.button("Predict", type="primary"):
+    response = requests.post(
+        "https://nimerml-backend.hf.space/v1/predict",
+        json=product_data
+    )
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Sales"]
+        st.write(f"Predicted Product Store Sales Total: ${predicted_sales:.2f}")
+    else:
+        st.error("Error in API request")

requirements.txt

@@ -1,3 +1,2 @@
-altair
-pandas
-streamlit
+requests==2.32.3
+streamlit==1.45.0