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.amlignore

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+## 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

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+
+FROM python:3.9-slim
+
+# Set the working directory inside the container.
+# This is where your application will live.
+# Streamlit apps should not run from the root directory.
+WORKDIR /app
+
+# The following section installs necessary system packages
+# and uses `apt-get` with the `rm` command to clean up
+# the cache in a single layer, which reduces the final
+# image size.
+RUN apt-get update \
+    && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# The following two instructions are key for cache efficiency.
+# 1. Copy `requirements.txt` into the container.
+# 2. Install the dependencies.
+# This creates a layer that is only rebuilt when `requirements.txt` changes,
+# which is less frequent than code changes.
+COPY requirements.txt ./
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# Copy all the application files (including app.py) into the container.
+# This layer will be rebuilt every time you change your code.
+COPY . .
+
+# Create a non-root user for security. Running a container as a non-root
+# user is a security best practice to prevent potential attacks.
+RUN useradd -m -u 1000 user
+USER user
+
+# Expose the port on which the Streamlit app will run.
+EXPOSE 8501
+
+# Define a health check for the container.
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+# Use ENTRYPOINT to set the command that runs when the container starts.
+# We're using the exec form to ensure signals are passed correctly.
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

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+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("Pizza Sales Data Analysis Dashboard")
+    df = load_data()
+
+    df = pd.DataFrame(df)
+
+    # Calculate key metrics
+    total_orders = df['order_id'].nunique()
+    total_revenue = df['total_price'].sum()
+    most_popular_size = df['pizza_size'].value_counts().idxmax()
+    most_frequent_category = df['pizza_category'].value_counts().idxmax()
+    total_pizzas_sold = df['quantity'].sum()
+    repeat_customers = df.groupby('order_id').size().value_counts().get(2, 0)
+
+    # Sidebar with key metrics
+    st.sidebar.header("Key Metrics")
+    st.sidebar.metric("Total Orders", total_orders)
+    st.sidebar.metric("Total Revenue", f"${total_revenue:,.2f}")
+    st.sidebar.metric("Most Popular Size", most_popular_size)
+    st.sidebar.metric("Most Popular Category", most_frequent_category)
+    st.sidebar.metric("Total Pizzas Sold", total_pizzas_sold)
+
+    plots = [
+        {"title": "Top Selling Pizzas (by Quantity)", "x": "pizza_name", "y": "quantity", "top": 5},
+        {"title": "Quantity of Pizzas Sold by Category and Time of the Day", "x": "time_of_day", "hue": "pizza_category"},
+        {"title": "Quantity of Pizzas Sold by Size and Time of the Day", "x": "time_of_day", "hue": "pizza_size"},
+        {"title": "Monthly Revenue Trends by Pizza Category", "x": "order_month", "y": "total_price", "hue": "pizza_category", "estimator": "sum", "marker": "o"},
+    ]
+
+    for plot in plots:
+      st.header(plot["title"])
+      
+      fig, ax = plt.subplots()
+      
+      if "Top Selling Pizzas" in plot["title"]:
+        data_aux = df.groupby(plot["x"])[plot["y"]].sum().reset_index().sort_values(by=plot["y"], ascending=False).head(plot["top"])
+        ax.bar(data_aux[plot["x"]].values.tolist(), data_aux[plot["y"]].values.tolist())
+      
+      if "Quantity of Pizzas" 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)
+
+      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)
+    
+
+if __name__ == "__main__":
+    app()

requirements.txt

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+pandas==1.5.2
+matplotlib==3.6.2
+seaborn==0.12.1
+scipy==1.10.0
+numpy==1.23.5
+streamlit==1.46.0