diff --git a/README.md b/README.md
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+# FRED Data Visualization App
+
+This Streamlit app creates visualizations using data from the Federal Reserve Bank of St. Louis (FRED, ALFRED, and Maps APIs).
+
+## Setup
+
+1. Perquisites
+
+a. Ensure you have a FRED API which can be obtained by creating an account at https://fredaccount.stlouisfed.org/login/secure/ and requesting an API from the menu on the left hand side of the page.
+
+b. Python 3.7+ installed on your system.
+
+2. Install Poetry if you haven't already:
+   ```
+   pip install poetry
+   ```
+
+3. Clone this repository:
+   ```
+   git clone <repository-url>
+   cd fred-visualization-app
+   ```
+
+4. Install the project dependencies using Poetry:
+   ```
+   poetry install
+   ```
+
+5. Activate the virtual environment created by Poetry:
+   ```
+   poetry shell
+   ```
+
+6. Create an `.env` file in the project root and add your FRED API key:
+   ```
+   FRED_API_KEY=your_api_key_here
+   ```
+
+## Running the App
+
+To run the Streamlit app, use the following command within the Poetry shell:
+
+```
+streamlit run app.py
+```
+
+The app should open in your default web browser. If it doesn't, you can access it at `http://localhost:8501`.
+
+## Using the App
+
+1. Enter a FRED Series ID in the sidebar (e.g., 'GDP' for Gross Domestic Product).
+2. Select start and end dates for the data you want to visualize.
+3. Click 'Fetch Data' to retrieve and visualize the data.
+4. Use the ALFRED section to view historical releases of a specific series.
+5. Use the Maps section to view geographical data (note: this is a simplified representation and doesn't include actual map visualizations).
+
+## Available Data Sources
+
+- FRED (Federal Reserve Economic Data)
+- ALFRED (Archival Federal Reserve Economic Data)
+- FRED Maps (Geographical Economic Data)
+
+## Troubleshooting
+
+If you encounter any issues:
+
+1. Ensure you have the latest version of Poetry installed:
+   ```
+   pip install --upgrade poetry
+   ```
+
+2. Try removing the existing virtual environment and reinstalling dependencies:
+   ```
+   poetry env remove python
+   poetry install
+   ```
+
+3. If you're still having issues with specific packages, you can try updating them individually:
+   ```
+   poetry update package_name
+   ```
+
+4. Verify that your API key is correct in the `.env` file.
+
+5. Check your internet connection, as the app requires access to the FRED API.
+
+6. If you're experiencing issues related to Streamlit or its dependencies, you can try using a specific Python version that's known to work well with the current Streamlit version:
+   ```
+   poetry env use python3.9
+   poetry install
+   ```
+
+For more information on available FRED series IDs and API usage, visit: https://fred.stlouisfed.org/docs/api/fred/
+
+## License
+
+This project is open source and available under the [MIT License](LICENSE).
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
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index 0000000..e69de29
diff --git a/fred-visualization-app/__init__.py b/fred-visualization-app/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fred-visualization-app/app.py b/fred-visualization-app/app.py
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+++ b/fred-visualization-app/app.py
@@ -0,0 +1,138 @@
+"""
+FRED Data Visualization App
+
+This module provides a Streamlit-based web application for visualizing
+Federal Reserve Economic Data (FRED).
+It allows users to select and compare different economic indicators,
+apply various aggregations,
+and generate insights from the data.
+"""
+import os
+import pandas as pd
+import plotly.graph_objects as go
+import streamlit as st
+from dotenv import load_dotenv
+from fredapi import Fred
+
+from .fred_categories import category_component, fetch_fred_categories
+
+# Load environment variables
+load_dotenv()
+
+# Initialize FRED API
+fred = Fred(api_key=os.getenv('FRED_API_KEY'))
+
+# Streamlit configuration
+st.set_page_config(layout="wide")
+st.title('FRED Data Visualization App')
+
+# --- Data Selection Sidebar ---
+st.sidebar.header('Data Selection')
+
+# 1. Category Selection (using memoization for efficiency)
+@st.cache_data
+def get_fred_categories():
+    return fetch_fred_categories()
+
+
+categories = get_fred_categories()
+category_names = [cat['name'] for cat in categories]
+selected_category_name = st.sidebar.selectbox("Select Category", options=category_names)
+selected_category_id = next(
+    cat['id'] for cat in categories if cat['name'] == selected_category_name
+)
+
+# 2. Series Selection (using memoization for efficiency)
+@st.cache_data
+def get_series_list(category_id):
+    return fred.get_series_in_category(category_id)
+
+
+series_list = get_series_list(selected_category_id)
+series_id = st.sidebar.selectbox('Select FRED Series:', options=series_list)
+
+# 3. Comparison Series (Conditional)
+enable_comparison = st.sidebar.checkbox('Compare with another dataset')
+series_id2 = None
+if enable_comparison:
+    series_id2 = st.sidebar.selectbox(
+        'Select second FRED Series:',
+        options=series_list,
+        key='series2'  # Important for widget state
+    )
+
+# 4. Date Range Selection
+start_date = st.sidebar.date_input('Start Date')
+end_date = st.sidebar.date_input('End Date')
+
+# Aggregation option
+aggregation = st.sidebar.selectbox(
+    'Aggregation',
+    options=['None', 'Weekly', 'Monthly', 'Quarterly', 'Yearly']
+)
+
+# Visualization options
+viz_type = st.sidebar.selectbox(
+    'Visualization Type',
+    options=['Line', 'Bar', 'Scatter', '3D Scatter', 'Area']
+)
+
+@st.cache_data
+def get_fred_data(series_id, start_date, end_date):
+    """
+    Fetch data from FRED API for a given series and date range.
+
+    Args:
+        series_id (str): The FRED series identifier
+        start_date (str): Start date for the data range
+        end_date (str): End date for the data range
+
+    Returns:
+        pd.DataFrame: DataFrame containing the fetched data
+    """
+    try:
+        data = fred.get_series(series_id, start_date, end_date)
+        df = pd.DataFrame(data).reset_index()
+        df.columns = ['Date', 'Value']
+        return df
+    except fredapi.fred.HTTPError as e:
+        st.error(f"Error fetching data from FRED: {str(e)}")
+        return pd.DataFrame(columns=['Date', 'Value'])
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {str(e)}")
+        return pd.DataFrame(columns=['Date', 'Value'])
+
+
+def aggregate_data(df, agg_type):
+    """
+    Aggregate data based on the specified aggregation type.
+
+    Args:
+        df (pd.DataFrame): Input DataFrame
+        agg_type (str): Aggregation type (
+            'None', 'Weekly', 'Monthly', 'Quarterly', 'Yearly'
+        )
+
+    Returns:
+        pd.DataFrame: Aggregated DataFrame
+    """
+    df['Date'] = pd.to_datetime(df['Date'])
+
+    resampling_options = {
+        'Weekly': 'W',
+        'Monthly': 'M',
+        'Quarterly': 'Q',
+        'Yearly': 'Y'
+    }
+
+    if agg_type in resampling_options:
+        return df.resample(
+            """ How to use this app:
+            1. Select a category and series from the sidebar
+            2. Choose to compare with another dataset (optional)
+            3. Select start and end dates
+            4. Choose aggregation and visualization type
+            5. Click 'Fetch Data' to visualize
+            6. Explore insights below the visualization
+            7. Use the FRED Categories section for more information"""
+        )
diff --git a/fred-visualization-app/fred_categories.py b/fred-visualization-app/fred_categories.py
new file mode 100644
index 0000000..0f8f4b0
--- /dev/null
+++ b/fred-visualization-app/fred_categories.py
@@ -0,0 +1,48 @@
+import requests
+from dash import html, dcc
+import dash_bootstrap_components as dbc
+
+FRED_API_KEY = 'your_fred_api_key_here' 
+# Replace with your actual FRED API key
+
+def fetch_fred_categories():
+    url = f"https://api.stlouisfed.org/fred/category/children?category_id=0&api_key={FRED_API_KEY}&file_type=json"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json()['categories']
+    else:
+        return []
+
+
+def create_category_list():
+    categories = fetch_fred_categories()
+    return html.Div([
+        html.H3("FRED Categories"),
+        dbc.ListGroup([
+            dbc.ListGroupItem([
+                html.Div(f"{category['name']} (ID: {category['id']})"),
+                html.A(
+                    "View on FRED",
+                    href=f"https://fred.stlouisfed.org/categories/{category['id']}",
+                    target="_blank"
+                )
+            ]) for category in categories
+        ]),
+        html.Div([
+            html.A("View all categories on FRED", href="https://fred.stlouisfed.org/categories", target="_blank"),
+            html.Br(),
+            html.A("Search for FRED series", href="https://fred.stlouisfed.org/tags/series", target="_blank")
+        ], className="mt-3")
+    ])
+
+
+def category_component():
+    return html.Div([
+        html.H2("FRED Categories and Series"),
+        dbc.Input(
+            id="category-search",
+            type="text",
+            placeholder="Search categories..."
+        ),
+        html.Div(id="category-list", children=[create_category_list()])
+    ])
diff --git a/fred-visualization-app/test_fred_app.py b/fred-visualization-app/test_fred_app.py
new file mode 100644
index 0000000..25c19ff
--- /dev/null
+++ b/fred-visualization-app/test_fred_app.py
@@ -0,0 +1,127 @@
+""" Pytest is a framework for writing and running tests in Python. """
+
+from unittest.mock import patch, MagicMock
+import pytest
+import pandas as pd
+import plotly.graph_objects as go
+from .app import (
+    get_fred_data,
+    aggregate_data,
+    create_visualization,
+    generate_insights,
+    fetch_fred_categories,
+    category_component)
+
+@pytest.fixture
+def mock_fred_data():
+    return pd.DataFrame({
+        'Date': pd.date_range(start='2020_01_01', end='2020_12_31', freq='D'),
+        'Value': range(366)
+    })
+
+@pytest.fixture
+def mock_fred():
+    with patch('fredapi.Fred') as mock:
+        mock.get_series.return_value = pd.Series(
+            range(366),
+            index=pd.date_range(
+                start='2020_01_01',
+                end='2020_12_31',
+                freq='D'
+            )
+        )
+        yield mock
+
+def test_get_fred_data(mock_fred):
+    result = get_fred_data('GDP', '2020_01_01', '2020_12_31')
+    assert isinstance(result, pd.DataFrame)
+    assert len(result) == 366
+    assert list(result.columns) == ['Date', 'Value']
+
+def test_get_fred_data_error_handling(mock_fred):
+    mock_fred.return_value.get_series.side_effect = Exception("API Error")
+    result = get_fred_data('GDP', '2020_01_01', '2020_12_31')
+    assert isinstance(result, pd.DataFrame)
+    assert result.empty
+
+def test_0get_fred_data_empty_date_range(mock_fred):
+    result = get_fred_data('GDP', '2020_01_01', '2020_01_01')
+    assert isinstance(result, pd.DataFrame)
+    assert result.empty
+
+@pytest.mark.parametrize("agg_type,expected_len", [
+    ('None', 366),
+    ('Weekly', 53),
+    ('Monthly', 12),
+    ('Quarterly', 4),
+    ('Yearly', 1)
+])
+def test_aggregate_data(mock_fred_data, agg_type, expected_len):
+    result = aggregate_data(mock_fred_data, agg_type)
+    assert len(result) == expected_len
+
+def test_aggregate_data_empty_dataframe():
+    empty_df = pd.DataFrame(columns=['Date', 'Value'])
+    result = aggregate_data(empty_df, 'Monthly')
+    assert result.empty
+
+def test_create_visualization(mock_fred_data):
+    with patch('app.viz_type', 'Line'):
+        fig = create_visualization(mock_fred_data)
+        assert isinstance(fig, go.Figure)
+        assert len(fig.data) == 1
+
+def test_create_visualization_with_comparison(mock_fred_data):
+    df2 = mock_fred_data.copy()
+    df2['Value'] = df2['Value'] * 2
+    with patch('app.viz_type', 'Line'):
+        fig = create_visualization(mock_fred_data, df2)
+        assert isinstance(fig, go.Figure)
+        assert len(fig.data) == 2
+
+@pytest.mark.parametrize("viz_type", ['Line', 'Bar', 'Scatter', '3D Scatter', 'Area'])
+def test_create_visualization_types(mock_fred_data, viz_type):
+    with patch('app.viz_type', viz_type):
+        fig = create_visualization(mock_fred_data)
+        assert isinstance(fig, go.Figure)
+        if viz_type == '3D Scatter':
+            assert isinstance(fig.data[0], go.Scatter3d)
+        else:
+            assert isinstance(fig.data[0], go.Scatter) or isinstance(fig.data[0], go.Bar)
+
+def test_generate_insights(mock_fred_data):
+    insights = generate_insights(mock_fred_data)
+    assert isinstance(insights, str)
+    assert "Insights for" in insights
+    assert "Average value:" in insights
+    assert "Minimum value:" in insights
+    assert "Maximum value:" in insights
+
+def test_generate_insights_with_comparison(mock_fred_data):
+    df2 = mock_fred_data.copy()
+    df2['Value'] = df2['Value'] * 2
+    insights = generate_insights(mock_fred_data, df2)
+    assert isinstance(insights, str)
+    assert "Comparison with" in insights
+    assert "Correlation:" in insights
+    assert "Average value difference:" in insights
+
+@patch('app.fred.get_categories')
+def test_fetch_fred_categories(mock_get_categories):
+    mock_get_categories.return_value = [
+        {'id': 1, 'name': 'Category 1'},
+        {'id': 2, 'name': 'Category 2'}
+    ]
+    categories = fetch_fred_categories()
+    assert len(categories) == 2
+    assert categories[0]['name'] == 'Category 1'
+    assert categories[1]['id'] == 2
+
+@patch('app.st')
+def test_category_component(mock_st):
+    mock_st.empty.return_value = MagicMock()
+    component = category_component()
+    assert component is not None
+    mock_st.empty.assert_called_once()
+
+# Add more tests as needed for other functions and edge cases
\ No newline at end of file
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..000a841
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,31 @@
+[tool.poetry]
+name = "fred-visualization-app"
+version = "0.1.0"
+description = "A Streamlit app for visualizing FRED, ALFRED, and Maps data"
+authors = ["Alex Jensen <thanotosq@gmail.com>"]
+packages = [{include = "fred-visualization-app"},]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.9.7 || >3.9.7,<3.12"
+pandas = "^2.0.0"
+streamlit = "1.22.0"
+fredapi = "0.5.1"
+python-dotenv = "1.0.0"
+jinja2 = "3.1.2"
+numpy = "1.23.5"
+scipy = "1.10.1"
+tenacity = "8.2.2"
+tornado = "6.2"
+plotly = "^5.22.0"
+dash = "^2.17.1"
+werkzeug = "~2.3.7"
+flask = "~2.3.3"
+dash-bootstrap-components = "^1.6.0"
+
+[tool.poetry.dev-dependencies]
+pytest = "^7.3.1"
+pytest-mock = "*"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..d3bab34
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,10 @@
+streamlit==1.22.0
+pandas==1.5.3
+plotly==5.14.1
+fredapi==0.5.1
+python-dotenv==1.0.0
+jinja2==3.1.2
+numpy==1.23.5
+scipy==1.10.1
+tenacity==8.2.2
+tornado==6.2
\ No newline at end of file
diff --git a/sample.env b/sample.env
new file mode 100644
index 0000000..dc5dde0
--- /dev/null
+++ b/sample.env
@@ -0,0 +1 @@
+FRED_API_KEY=YOUR_FRED_API_KEY_HERE
\ No newline at end of file