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.amlignore

@@ -0,0 +1,6 @@
+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

Dockerfile

@@ -1,15 +1,41 @@
-FROM python:3.13.5-slim
+FROM python:3.9-slim
 
-WORKDIR /app
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+
+
+# Run apt-get update and install as root
+USER root
 
 RUN apt-get update && apt-get install -y \
     build-essential \
+
     curl \
+
+    software-properties-common \
+
     git \
+
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+USER user
+
+
+# COPY requirements.txt ./
+
+# COPY src/ ./src/
+
+COPY . .
 
 RUN pip3 install -r requirements.txt
 
@@ -17,4 +43,4 @@ 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"]
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Load data
+def load_data():
+    df = pd.read_csv("processed_data.csv")  # Replace with your dataset
+    return df
+
+# Create Streamlit app
+def app():
+    # Title for the app
+    st.title("Retail Sales Data Insights Dashboard")
+
+    # Load data
+    df = load_data()
+
+    # Key Metrics from the data
+    total_orders = df['Transaction ID'].nunique()
+    total_products_sold = df['Quantity'].sum()
+    total_revenue = df['Total Amount'].sum()
+    most_popular_product_cat = df['Product Category'].value_counts().idxmax()
+    most_frequent_age_cat = df['Age Category'].value_counts().idxmax()
+
+    # Display metrics in the sidebar
+    st.sidebar.header("Key Metrics")
+    st.sidebar.metric("Total Orders", total_orders)
+    st.sidebar.metric("Total Products Sold", total_products_sold)
+    st.sidebar.metric("Total Revenue", f"${total_revenue:,.2f}")
+    st.sidebar.metric("Most Popular Product Category", most_popular_product_cat)
+    st.sidebar.metric("Most Frequent Age Category", most_frequent_age_cat)
+
+    
+    plots = [
+    {"title": "Total Products Sold by Product and Age Categories", "x": "Product Category", "hue": "Age Category"},
+    {"title": "Monthly Revenue Trends by Product Category", "x": "month", "y": "Total Amount", "hue": "Product Category", "estimator": "sum", "marker": "o"},
+    {"title": "Monthly Revenue Trends by Age Category", "x": "month", "y": "Total Amount", "hue": "Age Category", "estimator": "sum", "marker": "o"},
+    {"title": "Revenue by Product Category", "x": "Product Category", "y": "Total Amount", "estimator": "sum"},
+    ]
+
+    for plot in plots:
+        st.header(plot["title"])
+
+        fig, ax = plt.subplots()
+
+        if "Total Products" in plot["title"]:
+            sns.countplot(data=df, x=plot["x"], hue=plot["hue"], ax=ax)
+
+        if "Monthly Revenue" in plot["title"]:
+            sns.lineplot(data=df, x=plot["x"], y=plot["y"], hue=plot["hue"], estimator=plot["estimator"], errorbar=None, marker=plot["marker"], ax=ax)
+
+        if "Revenue by Product" in plot["title"]:
+            sns.barplot(data=df, x=plot["x"], y=plot["y"], estimator=plot["estimator"], errorbar=None, ax=ax)
+
+        ax.set_xlabel(" ".join(plot["x"].split("_")).capitalize())
+        if "y" in plot.keys():
+            ax.set_ylabel(" ".join(plot["y"].split("_")).capitalize())
+        else:
+            ax.set_ylabel("Quantity")
+        ax.legend(bbox_to_anchor=(1,1))
+
+        st.pyplot(fig)
+        plt.show()
+    
+
+if __name__ == "__main__":
+    app()

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==1.5.2
+matplotlib==3.6.2
+seaborn==0.12.1
+scipy==1.10.0
+numpy==1.23.5
+streamlit