diff --git a/Assignment4.py b/Assignment4.py
new file mode 100644
index 0000000..472cac4
--- /dev/null
+++ b/Assignment4.py
@@ -0,0 +1,377 @@
+import pandas as pd
+import plotly.express as px
+import statsmodels
+from matplotlib import pyplot as plt
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder
+from plotly import graph_objects as go
+from plotly import figure_factory as ff
+from sklearn.metrics import confusion_matrix
+from itertools import combinations
+import numpy 
+import statistics
+import statsmodels.api as sm
+
+
+
+# insert your dataset here
+
+df2 = pd.read_csv(
+    "https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz"
+)
+print(df2)
+
+#subset the dataset just to test and to avoid long running time
+df = df2.head(100)
+
+# below line is for titanic specific dataset because the column name was already a key in python \
+
+#df.rename(columns={"class": "v_class"}, inplace=True)
+# print(list(df))
+
+# Here we define the response and predictors
+responses = ["traffic_volume"]
+predictors = [
+    "holiday",
+    "temp",
+    "rain_1h",
+    "snow_1h",
+    "clouds_all",
+    "weather_main",
+    "weather_description",
+    "date_time"
+]
+
+print(list(responses))
+print(list(predictors))
+
+# getting rid of missed values ASAP
+# I replaced missing values with mean, it's not the exact way but better than dropping
+# I Explained in the next part where 5 comes from, assumed if the column data type is int but it has only 5 values
+# is categorical
+
+for col in df.columns:
+    if df[col].dtypes == "float" or df[col].dtypes == "int" and df[col].nunique() > 5:
+        df[col].fillna((df[col].mean()), inplace=True)
+    else:
+        df = df.apply(lambda col: col.fillna(col.value_counts().index[0]))
+
+#determine if boolean or continuous
+
+category_labels = {}
+response_type = ''
+for i in responses:
+    if df[i].nunique() == 2:
+        df[i] = df[i].astype("bool")
+        df.replace({False: 0, True: 1}, inplace=True)
+        category_labels = {idx: value for idx, value in enumerate(df[i].unique())}
+        response_type = 'categorical'
+    else:
+        response_type = 'continuous'
+
+print("Response type is:", response_type)
+
+
+# create dictionary for predictors type
+
+# Honestly  this code is not gonna be accurate for ALL variables out there, a categorical variable can be numeric
+# Like cancer stages (1-4) or even float for example software versions. This is close to the most accurate we can get
+# without analyzing the data first. I assumed if there's 5 ints only that's categorical too, and I made 5 up myself.
+
+predictors_type = {"continuous": [], "categorical": []}
+continuous = df.select_dtypes(include=["float", "int"])
+
+for i in predictors:
+    if i in list(continuous) and df[i].nunique() > 5:
+        predictors_type["continuous"].append(i)
+    else:
+        predictors_type["categorical"].append(i)
+print(predictors_type)
+
+
+
+data_cat = df.select_dtypes("object")
+
+# one hot encoder for categorical variables
+onehotencoder = OneHotEncoder(handle_unknown="ignore")
+encoder = onehotencoder.fit_transform(data_cat.values.reshape(-1, 1)).toarray()
+dfOneHot = pd.DataFrame(encoder)
+data = pd.concat([df.select_dtypes(exclude=["object"]), dfOneHot], axis=1)
+data = data.head(len(df))
+#print(data)
+
+# creating train and test set
+for i in responses:
+    x = data.drop(i, axis=1)
+    y = data[i]
+print(x)
+print(y)
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20)
+#
+#
+# I created a dictionary because of the sake of interpretation but lists are easier to work with in this plot example.
+predictors_con = []
+predictors_cat = []
+for i in predictors:
+    if i in list(continuous):
+        predictors_con.append(i)
+    else:
+        predictors_cat.append(i)
+
+#Plotting the variables
+
+def cont_resp_cat_predictor():
+
+    n = 200
+
+    # Add histogram data and group data together
+    for i in predictors:
+        if i in predictors_cat and len(predictors_cat) > 2:
+            hist_data = list(combinations(predictors_cat, 3))
+        elif i in predictors_cat and 0 < len(predictors_cat) < 3:
+            hist_data = list(combinations(predictors_cat, len(predictors_cat)))
+
+    for j in df.columns:
+        if j in responses and response_type == 'continuous':
+            group_labels = sorted(df[i].unique())
+
+    # Create distribution plot with custom bin_size
+    fig_1 = ff.create_distplot(hist_data, group_labels, bin_size=0.2)
+    fig_1.update_layout(
+        title="Continuous Response by Categorical Predictor",
+        xaxis_title="Response",
+        yaxis_title="Distribution",
+    )
+    fig_1.show()
+
+    fig_2 = go.Figure()
+    for curr_hist, curr_group in zip(hist_data, group_labels):
+        fig_2.add_trace(
+            go.Violin(
+                x=numpy.repeat(curr_group, n),
+                y=curr_hist,
+                name=curr_group,
+                box_visible=True,
+                meanline_visible=True,
+            )
+        )
+    fig_2.update_layout(
+        title="Continuous Response by Categorical Predictor",
+        xaxis_title="Groupings",
+        yaxis_title="Response",
+    )
+    fig_2.show()
+
+    return
+
+
+def cat_resp_cont_predictor():
+
+    n = 200
+    # Add histogram data
+    hist_data = []
+    for i in predictors:
+        if i in predictors_con and len(predictors_con) > 2:
+            hist_data = list(combinations(predictors_con, 3))
+        elif i in predictors_cat and 0 < len(predictors_con) < 3:
+            hist_data = list(combinations(predictors_con, len(predictors_con)))
+
+    for i in df.columns:
+        if i in responses and response_type == 'categorical':
+            group_labels = sorted(df[i].unique())
+
+
+    # Create distribution plot with custom bin_size
+    fig_1 = ff.create_distplot(hist_data, group_labels, bin_size=0.2)
+    fig_1.update_layout(
+        title="Continuous Predictor by Categorical Response",
+        xaxis_title="Predictor",
+        yaxis_title="Distribution",
+    )
+    fig_1.show()
+
+    fig_2 = go.Figure()
+    for curr_hist, curr_group in zip(hist_data, group_labels):
+        fig_2.add_trace(
+            go.Violin(
+                x=numpy.repeat(curr_group, n),
+                y=curr_hist,
+                name=curr_group,
+                box_visible=True,
+                meanline_visible=True,
+            )
+        )
+    fig_2.update_layout(
+        title="Continuous Predictor by Categorical Response",
+        xaxis_title="Response",
+        yaxis_title="Predictor",
+    )
+    fig_2.show()
+
+    return
+
+def cat_response_cat_predictor():
+
+    x = []
+    for i in predictors:
+        if i in predictors_cat and len(predictors_cat) > 2:
+            x = list(*combinations(predictors_cat, 3))
+        elif i in predictors_cat and 0 < len(predictors_cat) < 3:
+            x = list(*combinations(predictors_cat, len(predictors_cat)))
+
+    for i in df.columns:
+        if i in responses and response_type == 'categorical':
+            y = sorted(df[i].unique())
+
+    x_2 = [1 if abs(x_) > 0.5 else 0 for x_ in x]
+    y_2 = [1 if abs(y_) > 0.5 else 0 for y_ in y]
+
+    conf_matrix = confusion_matrix(x_2, y_2)
+
+    fig_no_relationship = go.Figure(
+        data=go.Heatmap(z=conf_matrix, zmin=0, zmax=conf_matrix.max())
+    )
+    fig_no_relationship.update_layout(
+        title="Categorical Predictor by Categorical Response (without relationship)",
+        xaxis_title="Response",
+        yaxis_title="Predictor",
+    )
+    fig_no_relationship.show()
+
+
+    x_2 = [1 if abs(x_) > 1.5 else 0 for x_ in x]
+    y_2 = [1 if abs(y_) > 1.5 else 0 for y_ in y]
+
+    conf_matrix = confusion_matrix(x_2, y_2)
+
+    fig_no_relationship = go.Figure(
+        data=go.Heatmap(z=conf_matrix, zmin=0, zmax=conf_matrix.max())
+    )
+    fig_no_relationship.update_layout(
+        title="Categorical Predictor by Categorical Response (with relationship)",
+        xaxis_title="Response",
+        yaxis_title="Predictor",
+    )
+    fig_no_relationship.show()
+
+    return
+
+
+def cont_response_cont_predictor():
+    x = []
+    for i in predictors:
+        if i in predictors_con and len(predictors_con) > 2:
+            x = list(*combinations(predictors_con, 3))
+        elif i in predictors_con and 0 < len(predictors_con) < 3:
+            x = list(*combinations(predictors_con, len(predictors_con)))
+
+    for i in df.columns:
+        if i in responses and response_type == 'categorical':
+            y = sorted(df[i].unique())
+
+    fig = px.scatter(x=x, y=y, trendline="ols")
+    fig.update_layout(
+        title="Continuous Response by Continuous Predictor",
+        xaxis_title="Predictor",
+        yaxis_title="Response",
+    )
+    fig.show()
+
+    return
+
+
+
+# creating models based on response category
+
+for i in responses:
+    if response_type == 'categorical':
+        model = LogisticRegression()
+        model_fitted = model.fit(x_train, y_train)
+        predictor_reg = model_fitted.predict(x_test)
+    else:
+        feature_name = i
+        predictor = statsmodels.api.add_constant(x)
+        model = statsmodels.api.OLS(y, predictor)
+        model_fitted = model.fit()
+        predictor_ols = model_fitted.predict()
+        print(model_fitted)
+        print(f"Variable: {feature_name}")
+        print(model_fitted.summary())
+
+
+        # T-value and P-value
+        t_value = round(model_fitted.tvalues[1], 6)
+        p_value = "{:.6e}".format(model_fitted.pvalues[1])
+        print("P-value is:", p_value)
+        print("T-value is:", t_value)
+
+        # # Plot
+        # fig = px.scatter(x=[i].values, y=y, trendline="ols")
+        # fig.update_layout(
+        #     title=f"Variable: {feature_name}: (t-value={t_value}) (p-value={p_value})",
+        #     xaxis_title=f"Variable: {feature_name}",
+        #     yaxis_title="y",
+        # )
+        # fig.show()
+
+        # Random forest
+        rf = RandomForestRegressor(n_estimators=100)
+        rf.fit(x_train, y_train)
+        sorted_idx = rf.feature_importances_.argsort()
+        plt.barh(feature_name, rf.feature_importances_[sorted_idx])
+        plt.xlabel("Random Forest Feature Importance")
+
+
+          #Bin number based on Scot's Rule
+
+        pred_stdev = statistics.stdev(predictor_ols)
+        pred_mean = statistics.mean(predictor_ols)
+        bin_width = 3.49*(pred_stdev)*(len(predictor_ols))**(-(1/3))
+        bin_number = round(bin_width/len(predictor_ols))
+
+        #Difference with mean of response
+
+        num = round(len(df) / bin_number)
+        list_df = [predictor_ols[i:i + num] for i in range(0, predictor_ols.shape[0], num)]
+
+
+        means =[]
+        lower_bin = []
+        upper_bin = []
+        bin_center = []
+        bin_count = []
+        for j in range(0, num-2):
+            lower_bin.append(list_df[j][0])
+            upper_bin.append(list_df[j][-1])
+            bin_count.append(len(list_df[j]))
+            chunk_mean = statistics.mean(list_df[j])
+            means.append(chunk_mean)
+        print(means)
+        print(lower_bin)
+        print(upper_bin)
+        print(bin_count)
+
+        i_list = list(range(0, num-2))
+
+        for i in means:
+            pop_mean = (means - pred_mean)**2
+
+        unweighted = pop_mean/bin_number
+        print(unweighted)
+
+
+        unweighted_table = pd.DataFrame(
+            {'i': i_list,
+             'lower bin': lower_bin,
+             'upper bin': upper_bin,
+             'bin count': bin_count,
+             'bin means': means,
+             'population mean': pop_mean,
+             'MeanSquaredDiff': unweighted
+
+             })
+
+        print(unweighted_table)
diff --git a/README.md b/README.md
index 3cd99cc..c31fa59 100644
--- a/README.md
+++ b/README.md
@@ -5,8 +5,8 @@
 - Setup a python 3.x venv (usually in `.venv`)
   - You can run `./scripts/create-venv.sh` to generate one
 - `pip3 install --upgrade pip`
-- Install pip-tools `pip3 install pip-tools`
-- Update dev requirements: `pip-compile --output-file=requirements.dev.txt requirements.dev.in --upgrade`
+- Install pip-tools ` `
+- Update dev requirement s: `pip-compile --output-file=requirements.dev.txt requirements.dev.in --upgrade`
 - Update requirements: `pip-compile --output-file=requirements.txt requirements.in --upgrade`
 - Install dev requirements `pip3 install -r requirements.dev.txt`
 - Install requirements `pip3 install -r requirements.txt`
diff --git a/mariadb-java-client-3.0.8.jar b/mariadb-java-client-3.0.8.jar
new file mode 100644
index 0000000..0c21e78
Binary files /dev/null and b/mariadb-java-client-3.0.8.jar differ
diff --git a/requirements.dev.txt b/requirements.dev.txt
index e05a14b..3ad9ca3 100644
--- a/requirements.dev.txt
+++ b/requirements.dev.txt
@@ -1,11 +1,15 @@
 #
-# This file is autogenerated by pip-compile with python 3.8
+# This file is autogenerated by pip-compile with python 3.10
 # To update, run:
 #
 #    pip-compile --output-file=requirements.dev.txt requirements.dev.in
 #
 anyio==3.6.1
     # via jupyter-server
+appnope==0.1.3
+    # via
+    #   ipykernel
+    #   ipython
 argon2-cffi==21.3.0
     # via
     #   jupyter-server
@@ -13,9 +17,9 @@ argon2-cffi==21.3.0
     #   notebook
 argon2-cffi-bindings==21.2.0
     # via argon2-cffi
-asttokens==2.0.5
+asttokens==2.0.8
     # via stack-data
-attrs==21.4.0
+attrs==22.1.0
     # via jsonschema
 babel==2.10.3
     # via jupyterlab-server
@@ -29,19 +33,19 @@ bleach==5.0.1
     # via nbconvert
 build==0.8.0
     # via pip-tools
-certifi==2022.6.15
+certifi==2022.9.24
     # via requests
 cffi==1.15.1
     # via argon2-cffi-bindings
 cfgv==3.3.1
     # via pre-commit
-charset-normalizer==2.1.0
+charset-normalizer==2.1.1
     # via requests
 click==8.1.3
     # via
     #   black
     #   pip-tools
-debugpy==1.6.2
+debugpy==1.6.3
     # via ipykernel
 decorator==5.1.1
     # via ipython
@@ -49,35 +53,29 @@ defusedxml==0.7.1
     # via nbconvert
 detect-secrets==1.3.0
     # via -r requirements.dev.in
-distlib==0.3.5
+distlib==0.3.6
     # via virtualenv
 entrypoints==0.4
-    # via
-    #   jupyter-client
-    #   nbconvert
-executing==0.9.1
+    # via jupyter-client
+executing==1.1.1
     # via stack-data
-fastjsonschema==2.16.1
+fastjsonschema==2.16.2
     # via nbformat
-filelock==3.7.1
+filelock==3.8.0
     # via virtualenv
 flake8==4.0.1
     # via -r requirements.dev.in
-identify==2.5.2
+identify==2.5.6
     # via pre-commit
-idna==3.3
+idna==3.4
     # via
     #   anyio
     #   requests
-importlib-metadata==4.12.0
-    # via jupyterlab-server
-importlib-resources==5.9.0
-    # via jsonschema
-ipykernel==6.15.1
+ipykernel==6.16.0
     # via
     #   nbclassic
     #   notebook
-ipython==8.4.0
+ipython==8.5.0
     # via
     #   ipykernel
     #   jupyterlab
@@ -97,13 +95,13 @@ jinja2==3.1.2
     #   nbclassic
     #   nbconvert
     #   notebook
-json5==0.9.8
+json5==0.9.10
     # via jupyterlab-server
-jsonschema==4.7.2
+jsonschema==4.16.0
     # via
     #   jupyterlab-server
     #   nbformat
-jupyter-client==7.3.4
+jupyter-client==7.4.2
     # via
     #   ipykernel
     #   jupyter-server
@@ -119,49 +117,49 @@ jupyter-core==4.11.1
     #   nbconvert
     #   nbformat
     #   notebook
-jupyter-server==1.18.1
+jupyter-server==1.21.0
     # via
     #   jupyterlab
     #   jupyterlab-server
     #   nbclassic
     #   notebook-shim
-jupyterlab==3.4.4
+jupyterlab==3.4.8
     # via -r requirements.dev.in
 jupyterlab-pygments==0.2.2
     # via nbconvert
-jupyterlab-server==2.15.0
+jupyterlab-server==2.15.2
     # via jupyterlab
 markupsafe==2.1.1
     # via
     #   jinja2
     #   nbconvert
-matplotlib-inline==0.1.3
+matplotlib-inline==0.1.6
     # via
     #   ipykernel
     #   ipython
 mccabe==0.6.1
     # via flake8
-mistune==0.8.4
+mistune==2.0.4
     # via nbconvert
 mypy-extensions==0.4.3
     # via black
-nbclassic==0.4.3
+nbclassic==0.4.6
     # via jupyterlab
-nbclient==0.6.6
+nbclient==0.7.0
     # via nbconvert
-nbconvert==6.5.0
+nbconvert==7.2.1
     # via
     #   jupyter-server
     #   nbclassic
     #   notebook
-nbformat==5.4.0
+nbformat==5.7.0
     # via
     #   jupyter-server
     #   nbclassic
     #   nbclient
     #   nbconvert
     #   notebook
-nest-asyncio==1.5.5
+nest-asyncio==1.5.6
     # via
     #   ipykernel
     #   jupyter-client
@@ -188,15 +186,15 @@ pandocfilters==1.5.0
     # via nbconvert
 parso==0.8.3
     # via jedi
-pathspec==0.9.0
+pathspec==0.10.1
     # via black
-pep517==0.12.0
+pep517==0.13.0
     # via build
 pexpect==4.8.0
     # via ipython
 pickleshare==0.7.5
     # via ipython
-pip-tools==6.8.0
+pip-tools==6.9.0
     # via -r requirements.dev.in
 platformdirs==2.5.2
     # via
@@ -209,9 +207,9 @@ prometheus-client==0.14.1
     #   jupyter-server
     #   nbclassic
     #   notebook
-prompt-toolkit==3.0.30
+prompt-toolkit==3.0.31
     # via ipython
-psutil==5.9.1
+psutil==5.9.2
     # via ipykernel
 ptyprocess==0.7.0
     # via
@@ -225,7 +223,7 @@ pycparser==2.21
     # via cffi
 pyflakes==2.4.0
     # via flake8
-pygments==2.12.0
+pygments==2.13.0
     # via
     #   ipython
     #   nbconvert
@@ -235,13 +233,13 @@ pyrsistent==0.18.1
     # via jsonschema
 python-dateutil==2.8.2
     # via jupyter-client
-pytz==2022.1
+pytz==2022.4
     # via babel
 pyyaml==6.0
     # via
     #   detect-secrets
     #   pre-commit
-pyzmq==23.2.0
+pyzmq==24.0.1
     # via
     #   ipykernel
     #   jupyter-client
@@ -262,13 +260,13 @@ six==1.16.0
     #   asttokens
     #   bleach
     #   python-dateutil
-sniffio==1.2.0
+sniffio==1.3.0
     # via anyio
 soupsieve==2.3.2.post1
     # via beautifulsoup4
-stack-data==0.3.0
+stack-data==0.5.1
     # via ipython
-terminado==0.15.0
+terminado==0.16.0
     # via
     #   jupyter-server
     #   nbclassic
@@ -281,6 +279,7 @@ tomli==2.0.1
     # via
     #   black
     #   build
+    #   jupyterlab
     #   pep517
 tornado==6.2
     # via
@@ -291,7 +290,7 @@ tornado==6.2
     #   nbclassic
     #   notebook
     #   terminado
-traitlets==5.3.0
+traitlets==5.4.0
     # via
     #   ipykernel
     #   ipython
@@ -304,11 +303,9 @@ traitlets==5.3.0
     #   nbconvert
     #   nbformat
     #   notebook
-typing-extensions==4.3.0
-    # via black
-urllib3==1.26.11
+urllib3==1.26.12
     # via requests
-virtualenv==20.16.1
+virtualenv==20.16.5
     # via pre-commit
 wcwidth==0.2.5
     # via prompt-toolkit
@@ -316,14 +313,10 @@ webencodings==0.5.1
     # via
     #   bleach
     #   tinycss2
-websocket-client==1.3.3
+websocket-client==1.4.1
     # via jupyter-server
 wheel==0.37.1
     # via pip-tools
-zipp==3.8.1
-    # via
-    #   importlib-metadata
-    #   importlib-resources
 
 # The following packages are considered to be unsafe in a requirements file:
 # pip
diff --git a/requirements.in b/requirements.in
index 296d654..9fcd45c 100644
--- a/requirements.in
+++ b/requirements.in
@@ -1 +1,12 @@
-numpy
\ No newline at end of file
+fastapi
+flake8==5.0.4
+isort[requirements]
+numpy
+pandas
+pip-tools
+pre-commit
+plotly
+scikit-learn
+statsmodels
+matplotlib
+matplotlib
diff --git a/requirements.txt b/requirements.txt
index b000696..51c0cbe 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,135 @@
 #
-# This file is autogenerated by pip-compile with python 3.8
+# This file is autogenerated by pip-compile with python 3.10
 # To update, run:
 #
 #    pip-compile --output-file=requirements.txt requirements.in
 #
-numpy==1.23.1
+anyio==3.6.1
+    # via starlette
+build==0.8.0
+    # via pip-tools
+cfgv==3.3.1
+    # via pre-commit
+click==8.1.3
+    # via pip-tools
+contourpy==1.0.5
+    # via matplotlib
+cycler==0.11.0
+    # via matplotlib
+distlib==0.3.6
+    # via virtualenv
+fastapi==0.85.0
     # via -r requirements.in
+filelock==3.8.0
+    # via virtualenv
+flake8==5.0.4
+    # via -r requirements.in
+fonttools==4.37.4
+    # via matplotlib
+identify==2.5.6
+    # via pre-commit
+idna==3.4
+    # via anyio
+isort[requirements]==5.10.1
+    # via -r requirements.in
+joblib==1.2.0
+    # via scikit-learn
+kiwisolver==1.4.4
+    # via matplotlib
+matplotlib==3.6.1
+    # via -r requirements.in
+mccabe==0.7.0
+    # via flake8
+nodeenv==1.7.0
+    # via pre-commit
+numpy==1.23.4
+    # via
+    #   -r requirements.in
+    #   contourpy
+    #   matplotlib
+    #   pandas
+    #   patsy
+    #   scikit-learn
+    #   scipy
+    #   statsmodels
+packaging==21.3
+    # via
+    #   build
+    #   matplotlib
+    #   statsmodels
+pandas==1.5.0
+    # via
+    #   -r requirements.in
+    #   statsmodels
+patsy==0.5.3
+    # via statsmodels
+pep517==0.13.0
+    # via build
+pillow==9.2.0
+    # via matplotlib
+pip-tools==6.9.0
+    # via -r requirements.in
+platformdirs==2.5.2
+    # via virtualenv
+plotly==5.10.0
+    # via -r requirements.in
+pre-commit==2.20.0
+    # via -r requirements.in
+px==0.1.0
+    # via -r requirements.in
+pycodestyle==2.9.1
+    # via flake8
+pydantic==1.10.2
+    # via fastapi
+pyflakes==2.5.0
+    # via flake8
+pyparsing==3.0.9
+    # via
+    #   matplotlib
+    #   packaging
+python-dateutil==2.8.2
+    # via
+    #   matplotlib
+    #   pandas
+pytz==2022.4
+    # via pandas
+pyyaml==6.0
+    # via pre-commit
+scikit-learn==1.1.2
+    # via -r requirements.in
+scipy==1.9.2
+    # via
+    #   scikit-learn
+    #   statsmodels
+six==1.16.0
+    # via
+    #   patsy
+    #   python-dateutil
+sniffio==1.3.0
+    # via anyio
+starlette==0.20.4
+    # via fastapi
+statsmodels==0.13.2
+    # via -r requirements.in
+tenacity==8.1.0
+    # via plotly
+threadpoolctl==3.1.0
+    # via scikit-learn
+toml==0.10.2
+    # via pre-commit
+tomli==2.0.1
+    # via
+    #   build
+    #   pep517
+typing-extensions==4.4.0
+    # via pydantic
+virtualenv==20.16.5
+    # via pre-commit
+websocket-client==1.4.1
+    # via px
+wheel==0.37.1
+    # via pip-tools
+
+# The following packages are considered to be unsafe in a requirements file:
+# pip
+# setuptools