Merge pull request #2 from cashfree/update_readme_1

updating the read me

README.md

@@ -5,9 +5,9 @@ FileGenie SDK is a Python library designed to simplify parsing files from AWS S3
 - **Multi-format Support:** Effortlessly parse files in formats such as TEXT, CSV, EXCEL, ZIP, XML, and PDF directly from AWS S3.
 - **Flexible Response Types:** Generate responses tailored to user needs, including DATAFRAME, JSON, or FILE outputs.
 - **Password-Protected Files:** Seamlessly parse files secured with passwords.
-- **Custom Edge Case Handling:** Apply user-defined custom functions to manage specific parsing and transformation needs, including data sanitization, value conversions, or reformatting date fields for consistency.
-AWS S3 Integration: Fetch files directly from AWS S3 buckets using IAM roles for secure access.
-Streamlined Configuration: Set up easily with minimal configuration, eliminating the need of writing parser for specific file type.
+- **Custom Edge Case Handling:** Apply user-defined custom functions to address specific data massaging and transformation requirements, such as sanitizing data, converting values, reformatting date fields etc.
+- **AWS S3 Integration:** Fetch files directly from AWS S3 buckets using IAM roles for secure access.
+- **Streamlined Configuration:** Set up easily with minimal configuration, eliminating the need of writing parser for specific file type.
 
 ### Installation
 Install the SDK using pip:
@@ -21,7 +21,7 @@ pip install file_genie
 - **Pandas:** '2.0.0'
 
 ### Getting Started
-- **Define Custom Edge Cases:**
+**Define Custom Edge Cases:**
 Let's say you need to sanitize columns (e.g., standardise column values to a common format before applying custom logic) during file parsing, you can define custom functions for the SDK to use.
 
 To implement this:
@@ -37,11 +37,11 @@ from edgeCases import user_edge_cases
 self.edge_cases = user_edge_cases
 ```
 
-- **Define the configuration required for file parsing logic and S3 bucket names**
+**Define the configuration required for file parsing logic and S3 bucket names**
 ```
     s3_config: {
-        upload_bucket: reconciliation-live
-        download_bucket: reconciliation-live
+        upload_bucket: s3_bucket_name
+        download_bucket: s3_bucket_name
     }
     file_config: {
         "file_source_1": {
@@ -75,17 +75,26 @@ self.edge_cases = user_edge_cases
         },
     }
 ```
+**read_from_s3_func:** This filed in FileGenie configuration specifies the function to be used for parsing a specific file type from AWS S3. Depending on the file format, you can choose from the following available functions:
 
-- **Define a ParsedDataResponseType enum**
-```
-import enum
-class ParsedDataResponseType(enum.Enum):
-    DATAFRAME="DATAFRAME"
-    FILE="FILE"
-    JSON="JSON"
-```
+- **readFromS3** - parse the TXT, EXCEL, CSV, XML, PDF files
+- **readZipFromS3** - parse the zip files
+- **read_complete_excel_file** - Use this function when working with EXCEL files containing multiple sheets.
+
+**parameters_for_read_s3:** This field in FileGenie configuration specifies the additional parameters required for reading the file such as password_protected, password, sep etc. you can choose from the following available params:
+- password_protected: If file is password protected or not
+- passowrd_secret_key: Secret key name for password.
+- skiprows: Rows to skip at the start.
+- sep: Delimiter for CSV parsing.
+- header: Row number(s) to use as column names.
+- has_header: Specify if the file has a header.
+- skip_header: Skip the header row during processing.
+- sheet_name: Target sheet in an Excel file.
+- parser_func: Custom parser function.
+- chunksize: Number of rows to read per chunk.
+- skip_footer: Rows to skip at the end.
 
-- **Import and initialise the file genie**
+**Import and initialise the file genie**
 ```
 from file_genie import FileGenie
 

setup.py

@@ -3,7 +3,7 @@
 
 setup(
     name="file_genie",
-    version="0.0.2",
+    version="0.0.3",
     description="File Genie is designed to parse various file types and transform them according to provided configuration",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",