diff --git a/README.md b/README.md
index 1317e03..a3a6c43 100644
--- a/README.md
+++ b/README.md
@@ -68,8 +68,7 @@ Before you go ahead make sure you have [Python 3.10](https://www.python.org/down
 
 ### How to install and setup an Elasticsearch server locally
 
-Before you go ahead make sure you have [docker](https://docker.com/) installed and available in your system. The below steps are required only for the initial setup. To re-run an existing docker container, run `docker start my_es_server` and enter the previously saved password if prompted.
-
+Before you go ahead make sure you have [docker](https://docker.com/) installed and available in your system. The steps below are required only for the initial setup. To re-run an existing docker container, run `docker start my_es_server` and enter the previously saved password if prompted.
 
 1. Create the elasticsearch server:
     ```bash
@@ -110,11 +109,11 @@ Before you go ahead make sure you have [docker](https://docker.com/) installed a
     ```bash
     printenv <env-var-name>
     ```
-- To query Database using Django models
+- To query the database using Django models
     ```bash
     python3 manage.py dbshell
     ```
-- Make Django Migrations
+- Make Django migrations
     ```bash
     python3 manage.py makemigrations
     ```
diff --git a/scripts/get_ensembl_metadata.py b/scripts/get_ensembl_metadata.py
index 096ffb8..c65769e 100644
--- a/scripts/get_ensembl_metadata.py
+++ b/scripts/get_ensembl_metadata.py
@@ -10,16 +10,16 @@ def get_taxon_metadata(db_connection):
     """
     The function connects with ensembl My sql database
     and runs a sql query to retrieve data needed to load
-    into django models as fixtures. 
+    into django models as fixtures.
 
     Parameters:
     db_connection (sqlalchemy.create_engine): A sqlalchemy engine.
 
     Returns:
-    pandas dataframe (pd.DataFrame): tabluar data.
+    pandas dataframe (pd.DataFrame): tabular data.
 
     """
-    
+
     query = f"""select distinct taxonomy_id ,o.name ,url_name 
                     ,display_name ,scientific_name ,strain
                     from organism o
diff --git a/scripts/get_metazoa_species.py b/scripts/get_metazoa_species.py
index 117275b..f83fea8 100644
--- a/scripts/get_metazoa_species.py
+++ b/scripts/get_metazoa_species.py
@@ -9,7 +9,6 @@
 
 
 def get_taxon_ids(url):
-
     """
     The function scrapes the metazoa taxonomy ids data
     from a fixed url: https://metazoa.ensembl.org/species.html
@@ -19,7 +18,7 @@ def get_taxon_ids(url):
     that belong to metazoa.
 
     """
-        
+
     response = requests.get(url)
     soup = BeautifulSoup(response.text, "lxml")
 
@@ -33,9 +32,9 @@ def get_taxon_ids(url):
 
 def get_taxon_tree(taxon_ids, db_engine):
     """
-    The function connects with ensembl My sql database
+    The function connects with ensembl MySQL database
     and runs a sql query to retrieve taxon tree data needed to load
-    into django models as fixtures. 
+    into django models as fixtures.
 
     Parameters:
     taxon_ids (List): list of taxonomy ids for which entire tree structures
@@ -43,7 +42,7 @@ def get_taxon_tree(taxon_ids, db_engine):
     db_conn (sqlalchemy.create_engine): A sqlalchemy engine.
 
     Returns:
-    pandas dataframe (pd.DataFrame): tabluar data.
+    pandas dataframe (pd.DataFrame): tabular data.
 
     """
 
diff --git a/scripts/get_phrases.py b/scripts/get_phrases.py
index a6ccb51..5885136 100644
--- a/scripts/get_phrases.py
+++ b/scripts/get_phrases.py
@@ -20,7 +20,7 @@ def get_taxon_ids(url):
     that belong to metazoa.
 
     """
-        
+
     response = requests.get(url)
     soup = BeautifulSoup(response.text, "lxml")
 
@@ -34,7 +34,7 @@ def get_taxon_ids(url):
 
 def preprocess_name(text):
     """
-    Does some basic text preprocessing like 
+    Does some basic text preprocessing like
     removing special characters, extra spaces, etc.
     """
     name = text["name"]
@@ -53,9 +53,9 @@ def preprocess_name(text):
 
 def get_taxon_names(taxon_ids, db_conn):
     """
-    The function connects with ensembl My sql database
-    and runs a sql query to retrieve taxon names like synonyms, common names, 
-    scientif names and converts into elastic search synonym file format. 
+    The function connects with ensembl MySQL database
+    and runs a sql query to retrieve taxon names like synonyms, common names,
+    scientific names and converts into elastic search synonym file format.
 
     Parameters:
     taxon_ids (List): list of taxonomy ids for which entire tree structures
@@ -63,7 +63,7 @@ def get_taxon_names(taxon_ids, db_conn):
     db_conn (sqlalchemy.create_engine): A sqlalchemy engine.
 
     Returns:
-    pandas dataframe (pd.DataFrame): tabluar data.
+    pandas dataframe (pd.DataFrame): tabular data.
 
     """
 
diff --git a/scripts/get_synonyms.py b/scripts/get_synonyms.py
index b60aff6..ce1a047 100644
--- a/scripts/get_synonyms.py
+++ b/scripts/get_synonyms.py
@@ -20,7 +20,7 @@ def get_taxon_ids(url):
     that belong to metazoa.
 
     """
-        
+
     response = requests.get(url)
     soup = BeautifulSoup(response.text, "lxml")
 
@@ -34,10 +34,10 @@ def get_taxon_ids(url):
 
 def preprocess_name(text):
     """
-    Does some basic text preprocessing like 
+    Does some basic text preprocessing like
     removing special characters, extra spaces, etc.
     """
-      
+
     name = text["name"]
     if text["name_class"] != "scientific name":
         name = re.sub(r"[,.;@#?!&$\(\)]+\ *", " ", name)
@@ -49,9 +49,9 @@ def preprocess_name(text):
 
 def get_taxon_names(taxon_ids, db_conn):
     """
-    The function connects with ensembl My sql database
-    and runs a sql query to retrieve taxon names like synonyms, common names, 
-    scientif names and converts into elastic search synonym file format. 
+    The function connects with ensembl MySQL database
+    and runs a sql query to retrieve taxon names like synonyms, common names,
+    scientific names and converts into elastic search synonym file format.
 
     Parameters:
     taxon_ids (List): list of taxonomy ids for which entire tree structures
@@ -59,10 +59,10 @@ def get_taxon_names(taxon_ids, db_conn):
     db_conn (sqlalchemy.create_engine): A sqlalchemy engine.
 
     Returns:
-    pandas dataframe (pd.DataFrame): tabluar data.
+    pandas dataframe (pd.DataFrame): tabular data.
 
     """
-    
+
     query_df = pd.DataFrame()
     for i in range(len(taxon_ids[:])):
         taxon_id = taxon_ids[i]
diff --git a/scripts/get_taxon.py b/scripts/get_taxon.py
index 32e9e6f..0c50d24 100644
--- a/scripts/get_taxon.py
+++ b/scripts/get_taxon.py
@@ -18,7 +18,7 @@ def get_taxon_ids(url):
     that belong to metazoa.
 
     """
-        
+
     response = requests.get(url)
     soup = BeautifulSoup(response.text, "lxml")
 
@@ -32,9 +32,9 @@ def get_taxon_ids(url):
 
 def get_taxon_tree(taxon_ids, db_engine):
     """
-    The function connects with ensembl My sql database
+    The function connects with ensembl MySQL database
     and runs a sql query to retrieve taxon tree data needed to load
-    into django models as fixtures. 
+    into django models as fixtures.
 
     Parameters:
     taxon_ids (List): list of taxonomy ids for which entire tree structures
@@ -42,11 +42,10 @@ def get_taxon_tree(taxon_ids, db_engine):
     db_engine (sqlalchemy.create_engine): A sqlalchemy engine.
 
     Returns:
-    pandas dataframe (pd.DataFrame): tabluar data.
+    pandas dataframe (pd.DataFrame): tabular data.
 
     """
 
-
     tree_df = pd.DataFrame()
     for i in range(len(taxon_ids[:])):
         taxon_id = taxon_ids[i]
@@ -76,7 +75,14 @@ def get_taxon_tree(taxon_ids, db_engine):
 
     # get data json for taxon_search.NCBITaxaNode model
     pk_col = ["taxon_id"]
-    field_col = ["parent_id", "rank", "genbank_hidden_flag", "left_index", "right_index", "root_id"]
+    field_col = [
+        "parent_id",
+        "rank",
+        "genbank_hidden_flag",
+        "left_index",
+        "right_index",
+        "root_id",
+    ]
     m1_df = metazoa_df[pk_col + field_col].drop_duplicates()
 
     m1_df["model"] = "taxon_search.NCBITaxaNode"
diff --git a/scripts/get_taxon_flat.py b/scripts/get_taxon_flat.py
index 7df6ea4..fb4e975 100644
--- a/scripts/get_taxon_flat.py
+++ b/scripts/get_taxon_flat.py
@@ -33,9 +33,9 @@ def get_taxon_ids(url):
 
 def get_taxon_tree_flat(taxon_ids, db_conn):
     """
-    The function connects with ensembl My sql database
+    The function connects with ensembl MySQL database
     and runs a sql query to retrieve taxon tree data needed to load
-    into django models as fixtures. 
+    into django models as fixtures.
 
     Parameters:
     taxon_ids (List): list of taxonomy ids for which entire tree structures
@@ -43,10 +43,10 @@ def get_taxon_tree_flat(taxon_ids, db_conn):
     db_conn (sqlalchemy.create_engine): A sqlalchemy engine.
 
     Returns:
-    pandas dataframe (pd.DataFrame): tabluar data.
+    pandas dataframe (pd.DataFrame): tabular data.
 
     """
-        
+
     unique_taxons = list(set(taxon_ids))
 
     tree_df = pd.DataFrame()
@@ -87,7 +87,6 @@ def get_taxon_tree_flat(taxon_ids, db_conn):
     all_ids = metazoa_ids + add_ids
     metazoa_df = get_taxon_tree_flat(all_ids, db_conn)
 
-
     # the below code converts the dataframe into json format
     # required by django to load as fixtures.
     pk_col = []
diff --git a/src/index_documents.py b/src/index_documents.py
index 3d83073..0f29e5c 100644
--- a/src/index_documents.py
+++ b/src/index_documents.py
@@ -22,7 +22,11 @@ def __init__(self):
 
     def add_arguments(self, parser):
         parser.add_argument(
-            "-b", "--batch-size", dest="batch_size", type=int, help="Number of items to index at once."
+            "-b",
+            "--batch-size",
+            dest="batch_size",
+            type=int,
+            help="Number of items to index at once.",
         )
         parser.add_argument(
             "-r",
@@ -32,12 +36,26 @@ def add_arguments(self, parser):
             help="Remove objects from the index that are no longer present in \
                   the database.",
         )
-        parser.add_argument("-i", "--index", dest="index", type=str, help="Specify which index to update.")
         parser.add_argument(
-            "-c", "--clear_index", action="store_true", default=False, help="Clear and rebuild index."
+            "-i",
+            "--index",
+            dest="index",
+            type=str,
+            help="Specify which index to update.",
         )
         parser.add_argument(
-            "-a", "--age", dest="age", default=0, help="Number of hours back to consider objects new."
+            "-c",
+            "--clear_index",
+            action="store_true",
+            default=False,
+            help="Clear and rebuild index.",
+        )
+        parser.add_argument(
+            "-a",
+            "--age",
+            dest="age",
+            default=0,
+            help="Number of hours back to consider objects new.",
         )
 
     def handle(self, *args, **options):
diff --git a/src/taxon_search/documents.py b/src/taxon_search/documents.py
index eb06103..df68390 100644
--- a/src/taxon_search/documents.py
+++ b/src/taxon_search/documents.py
@@ -27,13 +27,11 @@
     synonyms=load_synonym_file(ph_file_path),
 )
 
-# Elastic search index time analyzer to be used while indexing documents.  
+# Elastic search index time analyzer to be used while indexing documents.
 index_analyzer = analyzer(
     "index_analyzer",
     tokenizer="standard",
-    filter=["lowercase", "stop", 
-            autophrase_syn_filter, 
-            synonym_token_filter],
+    filter=["lowercase", "stop", autophrase_syn_filter, synonym_token_filter],
 )
 
 #### Define Ensembl Taxonomy Flat on elastic search with appropiate settings.
@@ -45,15 +43,16 @@
 @taxon_flat_index.document
 class TaxonFlatDocument(Document):
     """
-    Elastic Search Document Model for Index data from the 
+    Elastic Search Document Model for Index data from the
     NCBITaxonFlat Django Model.
 
-    Auto Indexing signals are disabled. For re-indexing or updating the 
+    Auto Indexing signals are disabled. For re-indexing or updating the
     index, run the below command in `src` directory.
 
     python3 manage.py search_index --rebuild
 
     """
+
     taxon_id = fields.IntegerField(attr="taxon_id")
     parent_id = fields.IntegerField(attr="parent_id")
     left_index = fields.IntegerField(attr="left_index")
diff --git a/src/taxon_search/migrations/0001_initial.py b/src/taxon_search/migrations/0001_initial.py
index 9614df1..8952c04 100644
--- a/src/taxon_search/migrations/0001_initial.py
+++ b/src/taxon_search/migrations/0001_initial.py
@@ -22,7 +22,8 @@ class Migration(migrations.Migration):
                 (
                     "parent_id",
                     models.ForeignKey(
-                        on_delete=django.db.models.deletion.CASCADE, to="taxon_search.ncbitaxanode"
+                        on_delete=django.db.models.deletion.CASCADE,
+                        to="taxon_search.ncbitaxanode",
                     ),
                 ),
             ],
@@ -36,7 +37,10 @@ class Migration(migrations.Migration):
                 (
                     "id",
                     models.BigAutoField(
-                        auto_created=True, primary_key=True, serialize=False, verbose_name="ID"
+                        auto_created=True,
+                        primary_key=True,
+                        serialize=False,
+                        verbose_name="ID",
                     ),
                 ),
                 ("name", models.CharField(db_index=True, max_length=500)),
diff --git a/src/taxon_search/migrations/0003_alter_ncbitaxanode_parent_id.py b/src/taxon_search/migrations/0003_alter_ncbitaxanode_parent_id.py
index 33eaa30..9fdb446 100644
--- a/src/taxon_search/migrations/0003_alter_ncbitaxanode_parent_id.py
+++ b/src/taxon_search/migrations/0003_alter_ncbitaxanode_parent_id.py
@@ -6,7 +6,10 @@
 
 class Migration(migrations.Migration):
     dependencies = [
-        ("taxon_search", "0002_rename_taxaname_taxon_id_ncbitaxaname_taxon_id_and_more"),
+        (
+            "taxon_search",
+            "0002_rename_taxaname_taxon_id_ncbitaxaname_taxon_id_and_more",
+        ),
     ]
 
     operations = [
@@ -14,7 +17,9 @@ class Migration(migrations.Migration):
             model_name="ncbitaxanode",
             name="parent_id",
             field=models.ForeignKey(
-                default=0, on_delete=django.db.models.deletion.CASCADE, to="taxon_search.ncbitaxanode"
+                default=0,
+                on_delete=django.db.models.deletion.CASCADE,
+                to="taxon_search.ncbitaxanode",
             ),
         ),
     ]
diff --git a/src/taxon_search/migrations/0006_ensemblmetadata.py b/src/taxon_search/migrations/0006_ensemblmetadata.py
index 0750489..7ba0f64 100644
--- a/src/taxon_search/migrations/0006_ensemblmetadata.py
+++ b/src/taxon_search/migrations/0006_ensemblmetadata.py
@@ -15,7 +15,10 @@ class Migration(migrations.Migration):
                 (
                     "id",
                     models.BigAutoField(
-                        auto_created=True, primary_key=True, serialize=False, verbose_name="ID"
+                        auto_created=True,
+                        primary_key=True,
+                        serialize=False,
+                        verbose_name="ID",
                     ),
                 ),
                 ("taxonomy_id", models.IntegerField()),
diff --git a/src/taxon_search/migrations/0008_ncbitaxaflat.py b/src/taxon_search/migrations/0008_ncbitaxaflat.py
index 13b9c31..a017a34 100644
--- a/src/taxon_search/migrations/0008_ncbitaxaflat.py
+++ b/src/taxon_search/migrations/0008_ncbitaxaflat.py
@@ -15,7 +15,10 @@ class Migration(migrations.Migration):
                 (
                     "id",
                     models.BigAutoField(
-                        auto_created=True, primary_key=True, serialize=False, verbose_name="ID"
+                        auto_created=True,
+                        primary_key=True,
+                        serialize=False,
+                        verbose_name="ID",
                     ),
                 ),
                 ("taxon_id", models.IntegerField()),
diff --git a/src/taxon_search/migrations/0010_taxonflat.py b/src/taxon_search/migrations/0010_taxonflat.py
index c866296..51bf053 100644
--- a/src/taxon_search/migrations/0010_taxonflat.py
+++ b/src/taxon_search/migrations/0010_taxonflat.py
@@ -15,7 +15,10 @@ class Migration(migrations.Migration):
                 (
                     "id",
                     models.BigAutoField(
-                        auto_created=True, primary_key=True, serialize=False, verbose_name="ID"
+                        auto_created=True,
+                        primary_key=True,
+                        serialize=False,
+                        verbose_name="ID",
                     ),
                 ),
                 ("taxon_id", models.IntegerField()),
diff --git a/src/taxon_search/migrations/0012_ncbitaxonflat.py b/src/taxon_search/migrations/0012_ncbitaxonflat.py
index 2e41e01..12c2e22 100644
--- a/src/taxon_search/migrations/0012_ncbitaxonflat.py
+++ b/src/taxon_search/migrations/0012_ncbitaxonflat.py
@@ -15,7 +15,10 @@ class Migration(migrations.Migration):
                 (
                     "id",
                     models.BigAutoField(
-                        auto_created=True, primary_key=True, serialize=False, verbose_name="ID"
+                        auto_created=True,
+                        primary_key=True,
+                        serialize=False,
+                        verbose_name="ID",
                     ),
                 ),
                 ("taxon_id", models.IntegerField()),
diff --git a/src/taxon_search/models.py b/src/taxon_search/models.py
index c4a9897..bc09d82 100644
--- a/src/taxon_search/models.py
+++ b/src/taxon_search/models.py
@@ -7,10 +7,10 @@ class EnsemblMetadata(models.Model):
     """
     EnsemblMetadata Django model class.
 
-    All fields defined are self explainatory and 
+    All fields defined are self explainatory and
     picked up from the ensembl MySQL database
     aftering joining 'organism' , 'genome', 'division'
-    and 'data_release' tables present 
+    and 'data_release' tables present
     in the 'ensmebl_metadata_109' schema/database.
 
     refer the scripts/get_ensembl_metadata.py for SQL query used.
@@ -32,11 +32,11 @@ class NCBITaxonFlat(models.Model):
     """
     NCBI Taxonomy Django model class.
 
-    All fields defined are self explainatory and 
+    All fields defined are self explainatory and
     picked up from the ensembl MySQL database
     aftering joining 'ncbi_taxa_node' and 'ncbi_taxa_name'
     tables present in the 'ncbi_taxonomy_109' schema/database.
-    
+
     refer the scripts/get_taxon_flat.py for SQL query used.
 
     """
diff --git a/src/taxon_search/search.py b/src/taxon_search/search.py
index 6eb1433..1c41f71 100644
--- a/src/taxon_search/search.py
+++ b/src/taxon_search/search.py
@@ -1,15 +1,16 @@
 from .documents import TaxonFlatDocument
 
+
 def search_species(query):
     """
-    The function calls the Elastic Search Document Model using 
-    a query string and returns the results of the elastic search. 
+    The function calls the Elastic Search Document Model using
+    a query string and returns the results of the elasticsearch.
 
     Parameters:
     query (str): Query string provided by the user as Input
 
     Returns:
-    return_type (List[Dict]): List of Dictonaries containing the 
+    return_type (List[Dict]): List of Dictionaries containing the
             search results from the elastic search server.
 
     """
@@ -21,9 +22,6 @@ def search_species(query):
     query_results = []
 
     for hit in hits:
-        # print(hit.name, hit.name_class, hit.taxon_id, hit.species_taxon_id, hit.meta.score)
-        # print(hit.parent_id)
-
         data = {
             "taxon_id": hit.taxon_id,
             "name": hit.name,
diff --git a/src/taxon_search/utils.py b/src/taxon_search/utils.py
index efd0fa5..bdad4bc 100644
--- a/src/taxon_search/utils.py
+++ b/src/taxon_search/utils.py
@@ -1,20 +1,23 @@
 from sqlalchemy import create_engine, text as sql_text
-from .models import EnsemblMetadata, NCBITaxonFlat
+
+from .models import EnsemblMetadata
+
 
 TREE_TRAVERSAL_LIMIT = 5
 
+
 def load_synonym_file(path):
     """
-    Utility function to load 
+    Utility function to load
     Elastic search synonym files.
 
     Parameters:
     path (str): Path to the file.
 
     Returns:
-    return_type (List[str]): List of string for elastic search server 
+    return_type (List[str]): List of string for elastic search server
     to retrieve synonyms/phrases.
-    
+
     """
     syn_list = []
     with open(path, "r") as f:
@@ -24,11 +27,11 @@ def load_synonym_file(path):
     print(f"{path} file loaded...")
 
     return syn_list
-    
+
 
 def run_custom_sql(engine, query):
     """
-    Utility function to execute 
+    Utility function to execute
     custom sql directly on Django models.
 
     Parameters:
@@ -36,9 +39,9 @@ def run_custom_sql(engine, query):
     query (str): A SQL in multi-lined string format.
 
     Returns:
-    return_type (List[List]): List of Lists containing the 
+    return_type (List[List]): List of Lists containing the
     retrieved query results.
-    
+
     """
     with engine.connect() as cursor:
         rows = cursor.execute(sql_text(query))
@@ -48,19 +51,19 @@ def run_custom_sql(engine, query):
 
 def get_relevant_species(species_dict):
     """
-    Given a species dictonary with taxonomy id, the function
-    retrieves species from its parent id's and matches them 
+    Given a species dictionary with taxonomy id, the function
+    retrieves species from its parent id's and matches them
     with ensembl database.
 
     Parameters:
-    species_dict (Dict): A dictonary with species details like 
+    species_dict (Dict): A dictionary with species details like
     taxonomy id, parent id, name, etc.
 
     Returns:
-    species_dict (List[Dict]): List of Dictonaries containing the 
+    species_dict (List[Dict]): List of Dictionaries containing the
             matched relevant species.
 
-    parent_name (str): Name of parent taxonomy under 
+    parent_name (str): Name of parent taxonomy under
     which species match is found.
 
 
@@ -125,15 +128,15 @@ def get_all_parents(taxon_id):
 def get_species_from_parent(parent_id):
     """
     The function is called inside the `get_relevant_species`
-    function to get all species childs present under given a parent taxonomy id.
+    function to get all species children present under given a parent taxonomy id.
 
     Parameters:
     parent_id (str): Parent Taxonomy Id
 
     Returns:
-    return_type (List): List containing all species ids present under 
+    return_type (List): List containing all species ids present under
     given parent id tree.
-    
+
     """
 
     ncbi_engine = create_engine("mysql://anonymous@ensembldb.ensembl.org:3306/ncbi_taxonomy_109")
@@ -151,4 +154,4 @@ def get_species_from_parent(parent_id):
     with ncbi_engine.connect() as cursor:
         rows = cursor.execute(sql_text(query))
 
-    return [r[0] for r in rows]
\ No newline at end of file
+    return [r[0] for r in rows]
diff --git a/src/taxon_search/views.py b/src/taxon_search/views.py
index 70f5e5d..6847488 100644
--- a/src/taxon_search/views.py
+++ b/src/taxon_search/views.py
@@ -5,7 +5,7 @@
 from sqlalchemy import create_engine, text as sql_text
 
 from .search import search_species
-from .models import EnsemblMetadata, NCBITaxonFlat
+from .models import EnsemblMetadata
 from .utils import get_relevant_species
 
 pymysql.install_as_MySQLdb()
@@ -14,8 +14,8 @@
 # Create your views here.
 def index(request):
     """
-    View function for the index/search page of the 
-    taxonomy search django app. 
+    View function for the index/search page of the
+    taxonomy search django app.
 
     Parameters:
     request : GET request from the front-end containing the query string.
@@ -82,8 +82,8 @@ def index(request):
 
 def taxon_tree(request, taxon_id):
     """
-    View function (GET) for the taxon tree page of the 
-    taxonomy search django app. 
+    View function (GET) for the taxon tree page of the
+    taxonomy search django app.
 
     Parameters:
     request : GET request from the front-end containing the query string.