FermaCongress 1.2.1

A ZoomRx - Ferma Congress package for internal usage

Ferma Congress

FermaCongress is a private Python package used internally by the Planners team at ZoomRx - Ferma Congress to automate extraction and management of congress-level data. The package provides utilities for Authentication, Session & Tweet Management, Annotation & Social Workflows

Installation

pip install FermaCongress

# upgrade to the latest version
pip install --upgrade FermaCongress

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🔐 FermaCongress.auth

To access Ferma, you must provide a .env file containing your authorized Ferma credentials.

Function	Description
adminlogin()	Authenticates with the Ferma Admin Portal and enables access to its APIs
supportlogin()	Authenticates with the Ferma Support Portal and maintains an active session

Both functions expect the following variables in your .env file:

FERMA_USERNAME=<your-username>
FERMA_PASSWORD=<your-password>

Quick Start

from FermaCongress.auth import adminlogin, supportlogin

# Authenticate to the Admin portal
admin_client = adminlogin(".env")

# Authenticate to the Support portal
support_client = supportlogin(".env")

Environment Variables (.env)

FERMA_USERNAME=your_username
FERMA_PASSWORD=your_password

Encoded credentials: If your .env file contains Base64-encoded values, simply add the format="ENCODED" argument:

admin_client = adminlogin(".env", format="ENCODED")

🧠 FermaCongress.annotate

The annotate() function allows you to send a DataFrame to the Ferma Support Portal for keyword annotation and download the processed results.

Requires an authenticated Ferma Support Portal session (via supportlogin() from FermaCongress.auth)

Function

Parameter	Description
client	Authenticated Session instance from supportlogin()
input_df	Pandas DataFrame with an id column
custom_needles_df	Optional DataFrame of user-defined needles
needles	List of two ints: [kb_status, nct_status]
entities	Optional entity configuration (dict)
long_table	Use long table format (True) or pivot table format (False)

Quick Example

from FermaCongress.auth import supportlogin
from FermaCongress.annotate import annotate

# Authenticate to the Support portal
support_client = supportlogin(".env")

# Annotate
annotated_df = annotate(support_client, input_df=input_df, needles=[1, 1], long_table=True)

📥 FermaCongress.extractferma

This module provides a set of helper functions to extract congress-level data from the Ferma Admin Portal. All functions require an authenticated Admin client (via adminlogin() from FermaCongress.auth).

Functions

Function	Description
get_all_sessions()	Download all session-level metadata for a given congress ID
get_skg()	Download grouped session keyword data
get_fullabstracts()	Download full abstracts for all sessions
get_summary()	Download session summaries
get_tweets()	Download tweet-level data and merge with session metadata and priorities
get_priority()	Download and merge planner priority files into a consolidated DataFrame

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Example

from FermaCongress.auth import adminlogin
from FermaCongress.extractferma import (
    get_all_sessions, get_skg, get_fullabstracts,
    get_summary, get_tweets, get_priority
)

# 1) Authenticate to the Admin portal
admin_client = adminlogin(".env")

# 2) Fetch Sessions / Keywords / Tweets
CONGRESS_ID = "221"

sessions_df = get_all_sessions(admin_client, CONGRESS_ID)
skg_df      = get_skg(admin_client, CONGRESS_ID)
abstracts_df = get_fullabstracts(admin_client, CONGRESS_ID)

summary_df  = get_summary(admin_client, CONGRESS_ID)
tweets_df   = get_tweets(admin_client, CONGRESS_ID)
priority_df = get_priority(admin_client, CONGRESS_ID)

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🔄 FermaCongress.postferma

This module contains helper functions for posting tweet-level updates back to the Ferma Admin Portal and for triggering server-side buzz score recalculation.

Functions

Function	Description
addtweets()	Uploads new tweets for a specific congress ID
modifytweets()	Modifies existing tweets for a specific congress ID
populatebuzz()	Triggers the server to populate or update buzz scores for all sessions

All functions require an authenticated Admin client (adminlogin() from FermaCongress.auth).

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Example

from FermaCongress.auth import adminlogin
from FermaCongress.postferma import addtweets, modifytweets, populatebuzz

client = adminlogin(".env")

CONGRESS_ID = "221"

# Add new tweets
addtweets(client, df, CONGRESS_ID)

# Modify tweets (if needed)
modifytweets(client, df, CONGRESS_ID)

# Recalculate buzz scores
populatebuzz(client, CONGRESS_ID)

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🗂️ FermaCongress.planner

The FermaCongress.planner module provides utilities for generating congress planning files by combining session metadata, keyword information, and planner-level priorities.

Functions

Function	Description
baseplanner()	Builds a fully enriched planning DataFrame for a given Congress and Planner ID

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Example

from FermaCongress.auth import adminlogin
from FermaCongress.planner import baseplanner

# 1) Authenticate to the Admin portal
client = adminlogin(".env")

# 2) Build a planner file
CONGRESS_ID = "221"
PLANNER_ID  = "4"

planner_df = baseplanner(client, CONGRESS_ID, PLANNER_ID)
print(planner_df.head())

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⚙️ FermaCongress.formatexcel

The FormatExcel utility is used to apply styling and export your Ferma data (from a DataFrame or input file) into a clean, Ferma-styled Excel format.

from FermaCongress.formatexcel import format

format(dataframe=df, output_path="priority_report.xlsx")  # Format from a DataFrame

format(input_path="raw_sessions.xlsx", output_path="formatted_sessions.xlsx")  # Format from Excel file

format(input_path="raw_data.csv", output_path="formatted_output.xlsx")  # Format from CSV file

Parameter	Type	Description
input_path	str	Path to an input Excel or CSV file.
dataframe	pandas.DataFrame	DataFrame to format.
output_path	str	File path to save the formatted Excel output.
headers	bool	True to convert headers to proper casing (e.g., buzz_score → Buzz Score).
input_sheet	str	Name of the sheet to read from (Excel only). Optional if only one sheet.
output_sheet	str	Name of the sheet to write into in the output Excel file.

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