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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

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
+    # Write a code snippet to calculate key metrics from the pizza orders dataframe, including the 
+    # total number of unique orders, total revenue generated, the most popular pizza size, the most 
+    # frequent pizza category, total pizzas sold
+    total_orders = df['order_id'].drop_duplicates()    #function which can calculate the number of unique values
+    total_revenue = df['total_price'].sum()  #function which can sum the column
+    most_popular_size = df['pizza_size'].value_counts().idxmax()    #function which can get the maximum value 
+    most_frequent_category = df['pizza_category'].value_counts().idxmax()    #function which can count of value of each product
+    total_pizzas_sold = df['quantity'].sum()
+
+
+    """"""""""""
+
+    # Sidebar with key metrics
+    # Write a code snippet to display key metrics in the sidebar of a Streamlit application. 
+    # Show the total number of orders, total revenue (formatted as currency), the most popular
+    # pizza size, the most popular pizza category, and the total number of pizzas sold 
+    # using the st.sidebar.metric function.
+    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)
+
+    
+
+    """""""""""""
+
+    # Provide the details of the plots here
+    
+    plots = [
+        {"title": "Top Selling Pizzas (by Quantity)", "x": "pizza_name", "y": "quantity", "top": 5},    #Write the appropriiate column as per the title given
+        {"title": "Quantity of Pizzas Sold by Category and Time of the Day", "x": "order_time", "hue": "pizza_category"},   #Write the appropriiate column as per the title given
+        {"title": "Quantity of Pizzas Sold by Size and Time of the Day", "x": "order_time", "hue": "pizza_size"},  #Write the appropriiate column as per the title given
+        {"title": "Monthly Revenue Trends by Pizza Category", "x": "order_date", "y": "total_price", "hue": "pizza_category", "estimator": "sum", "marker": "o"}, #Write the appropriiate column as per the title given
+    ]
+
+    for plot in plots:
+      st.header(plot["title"])
+      
+      fig, ax = plt.subplots()
+      
+      if "Top Selling" in plot["title"]:
+        sns.countplot(data=df, x=plot["x"], y=plot["y"],top=plot["top"])
+      if "Category" in plot["title"]:
+        sns.countplot(data=df, x=plot["x"], hue=plot["hue"])
+      if "Size" in plot["title"]:
+        sns.countplot(data=df, x=plot["x"], hue=plot["hue"])
+      if "Revenue" in plot["title"]:
+        sns.countplot(data=df, x=plot["x"], y=plot["y"],ax=plot["top"], hue=plot["hue"], estimator=plot["estimator"], marker=plot["marker"])
+
+      else:
+        ax.set_ylabel("quantity")
+      ax.legend(bbox_to_anchor=(1,1)) 
+     
+
+      st.pyplot(fig)
+      plt.show()
+    
+
+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