Add option for specifying the CSV delimiter char

README.md

@@ -4,10 +4,10 @@
 
 **Create APIs from CSV files within seconds, using fastapi.**
 
-This is a Python package to create APIs from CSV files, using a lightweight & 
-fully customizable wrapper around [fastapi](https://fastapi.tiangolo.com/). Endpoints 
-and query parameters are auto-generated based on the column names and data types in the 
-CSV file. Its data is written to a (temporary) sqlite database, so the API is blazing 
+This is a Python package to create APIs from CSV files, using a lightweight &
+fully customizable wrapper around [fastapi](https://fastapi.tiangolo.com/). Endpoints
+and query parameters are auto-generated based on the column names and data types in the
+CSV file. Its data is written to a (temporary) sqlite database, so the API is blazing
 fast even for huge files.
 
 ![](images/visual-demo.png)
@@ -33,18 +33,22 @@ fastapi-csv people.csv
 
 # directly from URL
 fastapi-csv https://raw.githubusercontent.com/jrieke/fastapi-csv/main/people.csv
+
+# With options : specify the delimiter character in the CSV, the host and port for the server
+fastapi-csv people.csv --delimiter "|" --host "127.0.0.1" --port 1234
+
 ```
 
-Either command should start a fastapi instance, which has auto-generated endpoints and 
-query parameters based on the CSV file. Here, the API has an endpoint `/people` 
-(same name as the file), which can be queried using the CSV's column names, e.g. you can 
+Either command should start a fastapi instance, which has auto-generated endpoints and
+query parameters based on the CSV file. Here, the API has an endpoint `/people`
+(same name as the file), which can be queried using the CSV's column names, e.g. you can
 do:
 
 - `/people?first_name=Rachel`
 - `/people?last_name=Johnson&age=48`
-   
-Additionally, fastapi-csv creates some convenience query parameters for specific data 
-types, e.g. 
+
+Additionally, fastapi-csv creates some convenience query parameters for specific data
+types, e.g.
 
 - `/people&age_greaterThan=18` (for int/float)
 - `/people&age_lessThanEqual=18` (for int/float)
@@ -72,7 +76,7 @@ uvicorn my_file:app
 
 **Extending the API**
 
-The cool thing: `FastAPI_CSV` is just a wrapper around `FastAPI`. Therefore, you can do 
+The cool thing: `FastAPI_CSV` is just a wrapper around `FastAPI`. Therefore, you can do
 all the stuff you can do with a normal fastapi instance, e.g. add a new endpoint:
 
 ```python
@@ -82,7 +86,7 @@ def hello(self):
     return {"Hello:", "World"}
 ```
 
-In the future, you will also be able to easily modify existing endpoints that were 
+In the future, you will also be able to easily modify existing endpoints that were
 generated from the CSV file.
 
 
@@ -94,5 +98,5 @@ If your CSV file changes, you can update the API data with:
 app.update_database()
 ```
 
-Note that this will only update the data, not the API endpoints or query parameters. 
+Note that this will only update the data, not the API endpoints or query parameters.
 To do that, you need to create a new `FastAPI_CSV` instance or re-start uvicorn.

fastapi_csv/applications.py

@@ -59,7 +59,7 @@ class FastAPI_CSV(FastAPI):
     #     # Return modified results so they get passed to the user.
     #     return results
 
-    def __init__(self, csv_path: Union[str, Path]) -> None:
+    def __init__(self, csv_path: Union[str, Path], delimiter: Union[str, str]) -> None:
         """
         Initializes a FastAPI instance that serves data from a CSV file.
 
@@ -70,6 +70,7 @@ def __init__(self, csv_path: Union[str, Path]) -> None:
 
         # Read CSV file to pandas dataframe and create sqlite3 database from it.
         self.csv_path = csv_path
+        self.delimiter = delimiter
         self.table_name = Path(self.csv_path).stem
         self.con = None
         df = self.update_database()
@@ -134,8 +135,8 @@ def query_database(self, sql_query):
 
     def delete_database(self):
         """
-        Deletes the database with all data read from the CSV. 
-            
+        Deletes the database with all data read from the CSV.
+
         The CSV file is not deleted of course. The API endpoints are also not affected,
         so you can use `update_data` to read in new data.
         """
@@ -149,9 +150,9 @@ def delete_database(self):
     def update_database(self):
         """
         Updates the database with the current data from the CSV file.
-        
+
         Note that this only affects the database, not the endpoints. If the column names
-        and/or data types in the CSV change (and you want that to update in the 
+        and/or data types in the CSV change (and you want that to update in the
         endpoints as well), you need to create a new FastAPI_CSV object.
         """
         self.delete_database()
@@ -164,7 +165,7 @@ def update_database(self):
 
         # Download excel file from Google Sheets, read it with pandas and write to
         # database.
-        df = pd.read_csv(self.csv_path)
+        df = pd.read_csv(self.csv_path, delimiter=self.delimiter)
         self.con = sqlite3.connect(":memory:", check_same_thread=False)
         df.to_sql(self.table_name, self.con)
 

fastapi_csv/cli.py

@@ -14,17 +14,18 @@
 @typer_app.command()
 def main(
     csv_path: str = typer.Argument(..., help="Path to the CSV file"),
+    delimiter: str = typer.Option(",", help="Delimiter character in the CSV file"),
     host: str = typer.Option("127.0.0.1", help="IP to run the API on"),
     port: int = typer.Option(8000, help="Port to run the API on"),
 ):
     """
     🏗️ Create APIs from CSV files within seconds, using fastapi.
-    
+
     Just pass along a CSV file and this command will start a fastapi
     instance with auto-generated endpoints & query parameters to access the data.
     """
     typer.echo(f"🏗️ Creating API from CSV file: {csv_path}")
-    app = FastAPI_CSV(csv_path)
+    app = FastAPI_CSV(csv_path, delimiter)
     typer.echo("🦄 Starting with uvicorn...")
     typer.echo(
         "💡 Check out the API docs at "