1. Resources

def discount_calculator():
	"""
	A Streamlit-based interactive discount calculator.
	"""

	# Input fields with improved layout
	with st.container():
		original_price = st.number_input(
			"Price before discount (€)", min_value=0.0, value=0.0, format="%.2f"
		)
		discount = st.slider(
			"Discount (%)", min_value=0, max_value=100, value=0, step=1
		)

		saving = original_price * (discount / 100)
		price_after_discount = original_price - saving

		# Display results in columns for better layout
		col1, col2 = st.columns(2)
		with col1:
			st.metric(
				label="Final Price", value=f"€{price_after_discount:.2f}", border=True
			)
		with col2:
			st.metric(label="You Save", value=f"€{saving:.2f}", border=True)

def display_all_graphs(credentials_status: bool) -> None:
	"""
	Displays various graphs based on the availability of credentials.

	Args:
		credentials_status: The status indicating if credentials are valid.
	"""

	if credentials_status:
		# Show a select box with the years available in the dataset
		available_years = st.session_state.dataframe["Fecha de venta"].dt.year.unique()
		selected_year: str = st.selectbox("Select a year", available_years)

		# Number of products sold month/year
		product_month(df=st.session_state.dataframe, year=int(selected_year))

		# Money earned per month/year
		money_month(df=st.session_state.dataframe, year=int(selected_year))

		# Number of people interested by category in a year
		interest_category(df=st.session_state.dataframe, year=int(selected_year))

		col1, col2 = st.columns(2)
		with col1:
			# Matrix confusion relation between product status and gender
			gender_status(df=st.session_state.dataframe, year=int(selected_year))
		with col2:
			# Matrix confusion relation between product state and country
			status_country(df=st.session_state.dataframe, year=int(selected_year))

def gender_status(df: pd.DataFrame, year: int) -> None:
	"""
	Calculate and visualize the gender distribution by product status for a given year.

	Args:
		df: The sales data.
		year: The year to filter the data for.
	"""

	st.subheader("Gender Status Heatmap")

	# Filter data by the selected year
	data_year = df[df["Fecha de venta"].dt.year == year].copy()

	# Group by gender and product status
	df_2dhist = data_year.pivot_table(
		index="Estado del producto",
		columns="Genero",
		values="Fecha de venta",
		aggfunc="count",
		fill_value=0,
	)

	# Create the heatmap using Plotly
	fig = px.imshow(
		df_2dhist,
		text_auto=True,
		color_continuous_scale="mint",
		labels={"color": "Count"},
		aspect="auto",
	)

	# Adjust the layout to remove the grid, axes background transparency, and colorbar
	fig.update_layout(
		xaxis_title="Gender", yaxis_title="Product Status", coloraxis_showscale=False
	)

	# Display the plot in Streamlit
	st.plotly_chart(fig, use_container_width=True)

def interest_category(df: pd.DataFrame, year: int) -> None:
	"""
	Analyzes and visualizes the number of likes per product category for a given year.

	Args:
		df: The DataFrame containing sales data.
		year: The year for which to filter and analyze the data.

	Displays:
		A bar chart in Streamlit showing the number of likes per product category.
	"""

	# Filter data for the selected year
	df_filtered = df[df["Fecha de venta"].dt.year == year].copy()

	# Group by 'Tipo producto' and count 'Numero de mg'
	df_filtered = df_filtered.groupby("Tipo producto")["Numero de mg"].count()

	# Convert the result to a DataFrame
	df_filtered = pd.DataFrame(
		{"Tipo producto": df_filtered.index, "Numero de mg": df_filtered.values}
	)

	# Show graph in Streamlit
	st.subheader(f"Likes by Category in {year}")
	st.bar_chart(
		df_filtered.set_index("Tipo producto"), x_label="Product Type", y_label="Likes"
	)

def money_month(df: pd.DataFrame, year: int) -> None:
	"""
	Calculates and visualizes the total money earned per month for a given year.

	Args:
		df: The DataFrame containing sales data.
		year: The year for which to calculate the total money earned per month.
	"""

	# First we filter by year
	df_filtered = df[df["Fecha de venta"].dt.year == year].copy()

	# Then we create a column by month
	df_filtered["Mes"] = df_filtered["Fecha de venta"].dt.month

	# We group the money obtained per month
	df_money_month = df_filtered.groupby("Mes")["Precio producto"].sum()

	# We fill in those months without data with values at 0
	all_months = range(1, 13)
	df_money_month = df_money_month.reindex(all_months, fill_value=0)

	# Convert to DataFrame and include month names
	df_money_month = pd.DataFrame(
		{"Mes": months_names, "Dinero": df_money_month.values}
	)

	# Ensure correct order with pd.Categorical
	df_money_month["Mes"] = pd.Categorical(
		df_money_month["Mes"], categories=months_names, ordered=True
	)

	# Show graph in Streamlit
	st.subheader(f"Money earned per month in {year}")
	st.bar_chart(df_money_month.set_index("Mes"), x_label="Month", y_label="Cash")

def product_month(df: pd.DataFrame, year: int) -> None:
	"""_summary_

	Args:
		df (pd.DataFrame): _description_
		year (int): _description_
	"""

	st.subheader(f"Products sold per month in {year}")

	col1, col2 = st.columns(2, gap="large")

	# Filter data by year
	df_filtered_year = df[df["Fecha de venta"].dt.year == year].copy()

	df_filtered = df_filtered_year.copy()
	df_filtered["Mes"] = df_filtered_year["Fecha de venta"].dt.month
	df_filtered = (
		df_filtered.groupby(["Mes", "Tipo producto"]).size().reset_index(name="count")
	)

	# Convert month numbers to month names
	df_filtered["Mes"] = df_filtered["Mes"].apply(lambda x: months_names[x - 1])

	# Ensure correct order with pd.Categorical
	df_filtered["Mes"] = pd.Categorical(
		df_filtered["Mes"], categories=months_names, ordered=True
	)

	# Pivot for Streamlit bar chart
	df_pivotado = df_filtered.pivot(
		index="Mes", columns="Tipo producto", values="count"
	).fillna(0)

	# Remove months with no products
	df_pivotado = df_pivotado.loc[(df_pivotado > 0).any(axis=1)]

	with col1:
		# Display chart in Streamlit
		st.bar_chart(df_pivotado, horizontal=True, x_label="Products", y_label="Month")
	with col2:
		top_3_products = obtain_top(df=df_filtered_year, top=3, column="Tipo producto")
		st.metric(label="Top 3 products", value=str(top_3_products), border=True)

		a, b = st.columns(2)
		a.metric(
			label="Total products sold",
			value=int(df_filtered_year["Tipo producto"].value_counts().sum()),
			border=True,
		)
		b.metric(
			label="Total cash obtained",
			value=str(float(df_filtered_year["Precio producto"].sum().round(4))) + " €",
			border=True,
		)

def status_country(df: pd.DataFrame, year: int) -> None:
	"""
	Calculate and visualize the product status distribution by country for a given year.

	Args:
		df: The sales data.
		year: The year to filter the data for.

	Returns:
		plotly.graph_objs.Figure: The heatmap figure.
	"""

	st.subheader("Product Status by Country Heatmap")

	# Filter data for the specified year
	data_year = df[df["Fecha de venta"].dt.year == year].copy()

	# Group by product status and count the occurrences by country
	df_2dhist = data_year.pivot_table(
		index="Pais",
		columns="Estado del producto",
		values="Fecha de venta",
		aggfunc="count",
		fill_value=0,
	)

	# Create the heatmap using Plotly
	fig = px.imshow(
		df_2dhist,
		text_auto=True,
		color_continuous_scale="mint",
		labels={"color": "Count"},
		aspect="auto",
	)

	# Adjust the layout to remove the grid, axes background transparency, and colorbar
	fig.update_layout(
		xaxis_title="Product Status",
		yaxis_title="Country",
		coloraxis_showscale=False,  # Hide the colorbar
	)

	# Display the plot in Streamlit
	st.plotly_chart(fig, use_container_width=True)