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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+2023-07-22[[:space:]]19-52-10.mp4 filter=lfs diff=lfs merge=lfs -text

2023-07-22 19-52-10.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e95b69b12d405e77be51f9c6a69a1413b5d1913a0a04e78cc66f5e8200191362
+size 1783597

app.py

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+import streamlit as st
+import plotly.graph_objects as go
+import json
+import os
+import numpy as np
+from streamlit_option_menu import option_menu
+from markup import app_intro, how_use_intro
+from sklearn.linear_model import LinearRegression
+from default_text import default_text4, default_text5
+from generate_plot import generate_plot, set_openai_api_key
+
+PASSWORD = 'Ethan101'
+
+def authenticate(password):
+    return password == PASSWORD
+
+def tab1():
+    st.header("Economic Simulator and Python Coding Tutor")  
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        st.image("image.jpg", use_column_width=True)
+    with col2:
+        st.markdown(app_intro(), unsafe_allow_html=True)
+    st.markdown(how_use_intro(),unsafe_allow_html=True) 
+
+
+    github_link = '[<img src="https://badgen.net/badge/icon/github?icon=github&label">](https://github.com/ethanrom)'
+    huggingface_link = '[<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">](https://huggingface.co/ethanrom)'
+
+    st.write(github_link + '&nbsp;&nbsp;&nbsp;' + huggingface_link, unsafe_allow_html=True)
+
+def simulate_economy(monthly_individual_income, monthly_individual_expense, start_month, start_year, num_months=12):
+    income_params = json.loads(monthly_individual_income)
+    expense_params = json.loads(monthly_individual_expense)
+
+    # Simulate economic data
+    np.random.seed(42)
+    monthly_income = np.random.normal(loc=income_params["mean"], scale=income_params["standarddeviation"], size=num_months)
+    monthly_income = np.clip(monthly_income, income_params["min"], income_params["max"])
+    
+    monthly_expense = np.random.normal(loc=expense_params["mean"], scale=expense_params["standarddeviation"], size=num_months)
+    monthly_expense = np.clip(monthly_expense, expense_params["min"], expense_params["max"])
+
+    total_income_per_year = np.sum(monthly_income) * 12
+    average_income_per_year = np.mean(monthly_income) * 12
+
+    families_beyond_means = np.sum(monthly_income < monthly_expense)
+    families_paycheck_to_paycheck = np.sum(monthly_income >= monthly_expense)
+
+    return families_beyond_means, families_paycheck_to_paycheck, average_income_per_year, monthly_income, monthly_expense
+
+def plot_line_chart(data, x_label, y_label, title):
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=list(range(len(data))), y=data, mode='lines', name=title))
+    fig.update_layout(title=title, xaxis_title=x_label, yaxis_title=y_label)
+    return fig
+
+def tab2():
+
+    password_input = st.text_input('Enter Password', type='password')
+    if authenticate(password_input):
+
+        st.header("User Inputs")
+        monthly_individual_income = st.text_area("Monthly Individual Income (Python code snippet)", value='''{
+        "mean": 4000,
+        "min": 1200,
+        "max": 15000,
+        "standarddeviation": 2000
+        }''')
+        monthly_individual_expense = st.text_area("Monthly Individual Expense (Python code snippet)", value='''{
+        "mean": 4000,
+        "min": 1200,
+        "max": 15000,
+        "standarddeviation": 2000
+        }''')
+        start_month = st.selectbox("Start Month", ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])
+        start_year = st.number_input("Start Year", min_value=1900, max_value=2100, value=2021)
+
+        if st.button("Run Simulation"):
+            try:
+                num_months = 12
+                families_beyond_means, families_paycheck_to_paycheck, average_income_per_year, monthly_income, monthly_expense = simulate_economy(monthly_individual_income, monthly_individual_expense, start_month, start_year, num_months)
+                st.header("Simulation Results")
+                st.write(f"Number of families living beyond their means: {families_beyond_means}")
+                st.write(f"Number of families living paycheck to paycheck: {families_paycheck_to_paycheck}")
+                st.write(f"Average income per year: ${average_income_per_year:.2f}")
+
+                st.header("Monthly Income and Expense")
+                income_chart = plot_line_chart(monthly_income, "Month", "Income", "Monthly Individual Income")
+                st.plotly_chart(income_chart)
+
+                expense_chart = plot_line_chart(monthly_expense, "Month", "Expense", "Monthly Individual Expense")
+                st.plotly_chart(expense_chart)
+
+                st.header("Code Snippets")
+                st.subheader("Calculation of Number of Families living beyond their means")
+                st.code("""
+import numpy as np
+
+# Assuming monthly_income and monthly_expense are numpy arrays
+families_beyond_means = np.sum(monthly_income < monthly_expense)
+                """, language="python")
+
+                st.subheader("Calculation of Number of Families living paycheck to paycheck")
+                st.code("""
+import numpy as np
+
+# Assuming monthly_income and monthly_expense are numpy arrays
+families_paycheck_to_paycheck = np.sum(monthly_income >= monthly_expense)
+                """, language="python")
+
+                st.subheader("Calculation of Average income per year")
+                st.code(f"""
+# Assuming monthly_income is a numpy array
+average_income_per_year = np.mean(monthly_income) * 12
+                """, language="python")
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+
+    else:
+        # Password is incorrect, show an error message
+        st.error('Invalid password. Access denied.')
+
+
+def tab3():
+    st.header("Python Plotly Coding Tutor")
+
+    password_input = st.text_input('Enter Password', type='password')
+    if authenticate(password_input):
+
+        # Economy-related example data
+        years = np.arange(2010, 2022)
+        gdp = [12500, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000]
+        unemployment_rate = [8.3, 7.9, 7.2, 6.8, 6.1, 5.6, 5.2, 4.8, 4.3, 4.1, 3.9, 3.7]
+
+        st.subheader("Example: GDP over the Years")
+        st.write("Below is a plot showing the GDP growth over the years.")
+        
+        # Plotting GDP over the years using Plotly
+        fig_gdp = go.Figure()
+        fig_gdp.add_trace(go.Scatter(x=years, y=gdp, mode='lines+markers', name='GDP'))
+        fig_gdp.update_layout(title='GDP Growth Over the Years',
+                            xaxis_title='Year',
+                            yaxis_title='GDP (Billion USD)')
+
+        # Display Python code and explanation
+        st.write("Python code for GDP plot:")
+        st.code("""
+# Import necessary libraries
+import plotly.graph_objects as go
+import numpy as np
+
+# Sample data for years and GDP
+years = np.arange(2010, 2022)
+gdp = [12500, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000]
+
+# Create a Plotly figure object
+fig_gdp = go.Figure()
+
+# Add a line plot for GDP data
+fig_gdp.add_trace(go.Scatter(x=years, y=gdp, mode='lines+markers', name='GDP'))
+
+# Customize the plot layout
+fig_gdp.update_layout(title='GDP Growth Over the Years',
+                      xaxis_title='Year',
+                      yaxis_title='GDP (Billion USD)')
+
+# Display the plot
+st.plotly_chart(fig_gdp)
+    """)
+        st.write("This code uses the Plotly library to create an interactive line plot showing the GDP growth over the years. First, we import the necessary libraries, including Plotly and NumPy (for generating sample data). Next, we define the data for the years and the corresponding GDP values. We then create a Plotly figure object (`fig_gdp`) and add a line plot to it using the `go.Scatter` function. The plot is customized with a title and axis labels using the `update_layout` method. Finally, we use `st.plotly_chart` to display the plot in the Streamlit app.")
+
+        # Display the plot
+        st.plotly_chart(fig_gdp)
+
+        st.subheader("Example: Unemployment Rate over the Years")
+        st.write("Below is a plot showing the unemployment rate over the years.")
+        
+        # Plotting unemployment rate over the years using Plotly
+        fig_unemployment = go.Figure()
+        fig_unemployment.add_trace(go.Scatter(x=years, y=unemployment_rate, mode='lines+markers', name='Unemployment Rate'))
+        fig_unemployment.update_layout(title='Unemployment Rate Over the Years',
+                                    xaxis_title='Year',
+                                    yaxis_title='Unemployment Rate (%)')
+
+        # Display Python code and explanation
+        st.write("Python code for Unemployment Rate plot:")
+        st.code("""
+# Import necessary libraries
+import plotly.graph_objects as go
+import numpy as np
+
+# Sample data for years and unemployment rate
+years = np.arange(2010, 2022)
+unemployment_rate = [8.3, 7.9, 7.2, 6.8, 6.1, 5.6, 5.2, 4.8, 4.3, 4.1, 3.9, 3.7]
+
+# Create a Plotly figure object
+fig_unemployment = go.Figure()
+
+# Add a line plot for unemployment rate data
+fig_unemployment.add_trace(go.Scatter(x=years, y=unemployment_rate, mode='lines+markers', name='Unemployment Rate'))
+
+# Customize the plot layout
+fig_unemployment.update_layout(title='Unemployment Rate Over the Years',
+                               xaxis_title='Year',
+                               yaxis_title='Unemployment Rate (%)')
+
+# Display the plot
+st.plotly_chart(fig_unemployment)
+    """)
+        st.write("This code uses the Plotly library to create an interactive line plot showing the unemployment rate over the years. Similar to the previous example, we import the necessary libraries and define the data for the years and the corresponding unemployment rate. We then create a Plotly figure object (`fig_unemployment`) and add a line plot to it using the `go.Scatter` function. The plot is customized with a title and axis labels using the `update_layout` method. Finally, we use `st.plotly_chart` to display the plot in the Streamlit app.")
+
+        # Display the plot
+        st.plotly_chart(fig_unemployment)
+
+        st.subheader("Try Your Own Plotly Code!")
+        st.write("You can type in your Plotly code below and click the 'Run Code' button to see your plot.")
+        
+        # Code input text area
+        code_input = st.text_area("Type your Plotly code here:")
+
+        # Run button
+        if st.button("Run Code"):
+            try:
+                # Execute the user's code
+                exec(code_input)
+            except Exception as e:
+                st.error(f"Error: {e}")
+
+    else:
+        # Password is incorrect, show an error message
+        st.error('Invalid password. Access denied.')
+
+
+def tab4():
+    st.header("Customizable Plot with Plotly")
+
+    password_input = st.text_input('Enter Password', type='password')
+    if authenticate(password_input):
+
+        example_x_values = [2010, 2011, 2012, 2013, 2014, 2015]
+        example_y_values = [12500, 13000, 14000, 15000, 16000, 17000]
+
+        st.subheader("Customize Your Plot:")
+        col1, col2 = st.columns([1, 2])
+        with col1:
+            x_axis = st.text_input("Enter X-axis title:", "Years")
+            y_axis = st.text_input("Enter Y-axis title:", "GDP")
+            chart_type = st.selectbox("Choose Chart Type:", ["Scatter", "Line", "Bar"])
+            line_mode = st.selectbox("Choose Line Mode:", ["lines", "lines+markers", "markers"])
+            plot_color = st.color_picker("Choose Plot Color:", "#1f77b4")
+        with col2:
+            x_values = st.text_area("Enter X-axis values (comma-separated):", ", ".join(map(str, example_x_values)))
+            y_values = st.text_area("Enter Y-axis values (comma-separated):", ", ".join(map(str, example_y_values)))
+
+        try:
+            x_values = [float(x.strip()) for x in x_values.split(",")]
+            y_values = [float(y.strip()) for y in y_values.split(",")]
+        except ValueError:
+            st.error("Invalid input for x or y axis. Please enter valid numeric values.")
+
+        fig_custom = go.Figure()
+
+        if chart_type == "Scatter":
+            fig_custom.add_trace(go.Scatter(x=x_values, y=y_values, mode=line_mode, name=y_axis, marker_color=plot_color))
+        elif chart_type == "Line":
+            fig_custom.add_trace(go.Line(x=x_values, y=y_values, mode=line_mode, name=y_axis, line_color=plot_color))
+        elif chart_type == "Bar":
+            fig_custom.add_trace(go.Bar(x=x_values, y=y_values, name=y_axis, marker_color=plot_color))
+
+        fig_custom.update_layout(title=f"{y_axis} vs. {x_axis}",
+                                xaxis_title=x_axis,
+                                yaxis_title=y_axis)
+
+        st.subheader("Customized Plot:")
+        st.plotly_chart(fig_custom)
+
+        st.subheader("Python Code to Create the Customized Plot:")
+        code = f"""
+import plotly.graph_objects as go
+
+x_values = {x_values}
+y_values = {y_values}
+
+fig_custom = go.Figure()
+"""
+
+        if chart_type == "Scatter":
+            code += f"""
+fig_custom.add_trace(go.Scatter(x=x_values, y=y_values, mode='{line_mode}', name='{y_axis}', marker_color='{plot_color}'))
+"""
+        elif chart_type == "Line":
+            code += f"""
+fig_custom.add_trace(go.Line(x=x_values, y=y_values, mode='{line_mode}', name='{y_axis}', line_color='{plot_color}'))
+"""
+        elif chart_type == "Bar":
+            code += f"""
+fig_custom.add_trace(go.Bar(x=x_values, y=y_values, name='{y_axis}', marker_color='{plot_color}'))
+"""
+
+        code += f"""
+fig_custom.update_layout(title='{y_axis} vs. {x_axis}',
+                         xaxis_title='{x_axis}',
+                         yaxis_title='{y_axis}')
+"""
+
+        st.code(code)
+
+    else:
+        # Password is incorrect, show an error message
+        st.error('Invalid password. Access denied.')
+
+def tab5():
+    st.header("Building Predictive Models with Plotly")
+
+    password_input = st.text_input('Enter Password', type='password')
+    if authenticate(password_input):
+
+        np.random.seed(42)
+        x = np.arange(1, 11)
+        y = 2 * x + 3 + np.random.randn(10)
+
+        st.subheader("Linear Regression Example:")
+        st.write("Let's consider a simple linear regression example using the following data:")
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.write("X (Independent Variable):", x)
+        with col2:    
+            st.write("Y (Dependent Variable):", y)
+
+        fig_data = go.Figure()
+        fig_data.add_trace(go.Scatter(x=x, y=y, mode='markers', name='Data Points'))
+        fig_data.update_layout(title='Data Points for Linear Regression',
+                            xaxis_title='X (Independent Variable)',
+                            yaxis_title='Y (Dependent Variable)')
+
+        with col1:
+            st.plotly_chart(fig_data)
+
+        model = LinearRegression()
+        x_reshaped = x.reshape(-1, 1)
+        model.fit(x_reshaped, y)
+
+        st.subheader("Interactivity and Model Adjustment:")
+        st.write("You can interact with the chart by adjusting the values of the slope and intercept.")
+        st.write("Changing these parameters will modify the regression line and the predictions.")
+        st.write("Feel free to experiment and observe how the line fits the data differently.")
+
+        # Sliders for slope and intercept
+        slope_slider = st.slider("Slope (Coefficient)", min_value=-10.0, max_value=10.0, value=2.0, step=0.1)
+        intercept_slider = st.slider("Intercept", min_value=-10.0, max_value=10.0, value=3.0, step=0.1)
+
+        # Calculate predictions based on user-adjusted slope and intercept
+        y_pred_adjusted = slope_slider * x + intercept_slider
+
+        fig_regression = go.Figure()
+        fig_regression.add_trace(go.Scatter(x=x, y=y, mode='markers', name='Data Points'))
+        fig_regression.add_trace(go.Scatter(x=x, y=y_pred_adjusted, mode='lines', name='Regression Line', line=dict(color='red')))
+        fig_regression.update_layout(title='Linear Regression',
+                                    xaxis_title='X (Independent Variable)',
+                                    yaxis_title='Y (Dependent Variable)')
+
+        st.plotly_chart(fig_regression)
+
+        st.subheader("Interpreting Model Coefficients:")
+        st.write("The slope of the regression line represents how much Y changes for a one-unit increase in X.")
+        st.write("The intercept is the value of Y when X is 0. In our example, the intercept is 3.")
+        st.write("For each unit increase in X, Y increases by the slope you adjusted using the slider.")
+
+        st.subheader("Python Code for Linear Regression:")
+        code = """
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+# Sample data for linear regression example
+x = np.arange(1, 11)
+y = 2 * x + 3 + np.random.randn(10)
+
+# Perform linear regression and get the predicted values
+model = LinearRegression()
+x_reshaped = x.reshape(-1, 1)
+model.fit(x_reshaped, y)
+slope = model.coef_[0]
+intercept = model.intercept_
+
+# Display the coefficients
+print("Slope (Coefficient):", slope)
+print("Intercept:", intercept)
+"""
+        st.code(code)
+    else:
+        # Password is incorrect, show an error message
+        st.error('Invalid password. Access denied.')
+
+
+def tab6():
+    st.header("Auto Plot Generator")
+    st.markdown("Auto Generate code and plot for a given question")
+    password_input = st.text_input('Enter Password', type='password')
+    if authenticate(password_input):
+
+        openai_api_key = st.text_input("Enter your OpenAI API key:", type='password')
+
+        # Add the video display
+        video_file = "2023-07-22 19-52-10.mp4"
+        if os.path.exists(video_file):
+            st.video(video_file)
+        else:
+            st.warning("Video file not found.")
+
+        main_question = st.text_area("Enter Information here:", height=400, value=default_text4)
+        sub_question = st.text_area("Enter question here:", value=default_text5)
+
+        result = None
+
+        if st.button("Generate Code"):
+            if openai_api_key:
+                set_openai_api_key(openai_api_key)
+                with st.spinner('Thinking...'):
+                    result = generate_plot(main_question, sub_question)
+                st.code(result)
+                st.session_state.generated_code = result
+            else:
+                st.warning("Please enter your OpenAI API key.")
+
+        if st.button("Show Plot"):
+            if 'generated_code' in st.session_state:
+                with st.spinner('Generating Plot...'):
+                    exec(st.session_state.generated_code)
+            else:
+                st.warning("Please generate the code first.")
+
+    else:
+        # Password is incorrect, show an error message
+        st.error('Invalid password. Access denied.')
+
+def main():
+    st.set_page_config(page_title="Economic Simulator and Python Coding Tutor", page_icon=":memo:", layout="wide")
+    tabs = ["Intro", "Simulate", "Learn about plotly usage", "Building custom plots", "Building Predictive Models", "AI Plot Generation"]
+
+    with st.sidebar:
+
+        current_tab = option_menu("Select a Tab", tabs, menu_icon="cast")
+
+    tab_functions = {
+    "Intro": tab1,
+    "Simulate": tab2,
+    "Learn about plotly usage": tab3,
+    "Building custom plots": tab4,
+    "Building Predictive Models": tab5,
+    "AI Plot Generation": tab6,
+    }
+
+    if current_tab in tab_functions:
+        tab_functions[current_tab]()
+
+if __name__ == "__main__":
+    main()

default_text.py

@@ -0,0 +1,23 @@
+default_text4 = """ The dataset contains the following columns:
+
+    "Month": The month of the sales data (e.g., January, February, March).
+    "Sales": The total sales amount (in dollars) for each month.
+
+Here are the first few rows of the dataset:
+Month	Sales
+January	1500
+February	1800
+March	2200
+April	1900
+May	2500
+June	2800
+July	3000
+August	3200
+September	2900
+October	2700
+November	2300
+December	3500
+
+"""
+
+default_text5 = """What is the overall trend in monthly sales for the past year?"""

generate_plot.py

@@ -0,0 +1,26 @@
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import OpenAI
+import openai
+import os
+
+
+
+template = """You are given following information and a question. Generate a python code with plotly to find the answer. Provide Text as comments only in code.
+
+Information:
+{main_question}
+=============
+Question:
+{sub_question}
+Answer:"""
+
+prompt = PromptTemplate(template=template, input_variables=["main_question", "sub_question"])
+
+def set_openai_api_key(api_key):
+    openai.api_key = api_key
+
+def generate_plot(main_question, sub_question):
+    llm = OpenAI(temperature=0)
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    response = llm_chain.run(main_question=main_question, sub_question=sub_question)
+    return response

markup.py

@@ -0,0 +1,29 @@
+def app_intro():
+    return """
+    <div style='text-align: left;'>
+    <h2 style='text-align: center;'>Economic Simulator and Python Coding Tutor</h2>
+    <h3 style='text-align: center;'>Introduction</h3>
+            
+    <p>This app allows you to run basic economic simulations with values plugged in as Python code snippets.</p>
+
+    <h4>Information:</h4>
+    <ul>
+        <li><b>Try different simulations:</b> In this demo, you can easily try different simulations by modifying the Python code snippets for monthly individual income and expense.</li>
+        <li><b>Calculate Results:</b> You can calculate the number of families living beyond their means, the number of families living paycheck to paycheck, and the average income per year based on the provided Python code snippets.</li>
+    </ul>
+    </div>          
+    """
+
+def how_use_intro():
+    return """
+    <div style='text-align: left;'>
+    <h3 style='text-align: center;'>About this Demo</h3>
+    <br>
+    <h4>How to Use:</h4>
+    <ul>
+    <li><b>Testing:</b> To run a simulation, provide the Python code snippets for monthly individual income and expense in the text areas. Select the start month and year, and click the "Run Simulation" button. The app will display the simulation results in charts and provide code snippets for the calculations.</li>
+    <li><b>Reverse Simulation:</b> You can perform a reverse simulation by checking the "Perform Reverse Simulation" box. This will allow you to input the desired number of families living beyond their means, number of families living paycheck to paycheck, and average income per year. The app will estimate the corresponding Python code snippets for monthly individual income and expense based on the provided results.</li>
+    </ul>
+    <br>
+    </div>
+    """

requirements.txt

@@ -0,0 +1,7 @@
+plotly
+numpy
+scikit-learn
+streamlit_option_menu
+pandas
+langchain
+openai