FermaCongress 1.2.5

A ZoomRx - Ferma Congress package for internal usage

FermaCongress: https://pypi.org/project/FermaCongress/

FermaCongress is a Python package for automating congress-level data extraction, management, and analysis at ZoomRx - Ferma Congress.

📦 Installation

pip install FermaCongress
pip install --upgrade FermaCongress

🔑 Setup

Create a .env file with your Ferma credentials:

FERMA_USERNAME=your_username
FERMA_PASSWORD=your_password

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📚 Modules Overview

Module	Purpose	Authentication
auth	Login to Admin/Support portals	N/A
extractferma	Extract congress data	Admin Portal
postferma	Post tweets, trigger buzz scores	Admin Portal
planner	Generate planning files	Admin Portal
annotate	AI keyword annotation	Support Portal
trials	Extract clinical trial data	None (Public APIs)
formatexcel	Professional Excel formatting	None

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🔐 auth - Authentication

A lightweight utility that handles login for both the Ferma Admin and Support portals, returning ready-to-use headers or sessions.

from FermaCongress.auth import adminlogin, supportlogin

# Admin Portal Authentication
adminclient = adminlogin(".env")

# Support Portal Authentication
supportclient = supportlogin(".env")

# With Encoded credentials for Admin Portal
adminclient = adminlogin(".env", format="ENCODED")

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📥 extractferma - Data Extraction

Extract various types of congress data from the Admin Portal.

from FermaCongress.auth import adminlogin
from FermaCongress.extractferma import (
    get_all_sessions,    # Session metadata
    get_skg,             # Session keywords
    get_fullabstracts,   # Full abstract texts
    get_summary,         # AI-generated summaries
    get_tweets,          # Tweet data with buzz scores
    get_priority         # Priority data from all planners
)

adminclient = adminlogin(".env")
CONGRESS_ID = "221"

# Extract data
sessions = get_all_sessions(adminclient, CONGRESS_ID)
keywords = get_skg(adminclient, CONGRESS_ID)
tweets = get_tweets(adminclient, CONGRESS_ID)
abstracts = get_fullabstracts(adminclient, CONGRESS_ID)
summaries = get_summary(adminclient, CONGRESS_ID)

# Get priorities with filtering
priority = get_priority(adminclient, CONGRESS_ID, include=["ClientA"], exclude=["ClientB"])

Note: include and exclude accept Tenant Names only (not Team Names).

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🔄 postferma - Post Data

Upload tweets and trigger buzz score calculations.

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

adminclient = adminlogin(".env")
CONGRESS_ID = "221"

# Add new tweets
addtweets(adminclient, tweets_add_df, CONGRESS_ID)

# Modify existing tweets
modifytweets(adminclient, tweets_modify_df, CONGRESS_ID)

# Recalculate buzz scores
populatebuzz(adminclient, CONGRESS_ID)

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🗂️ planner - Generate Planner Files

Create enriched planning files with session data, keywords, and priorities.

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

adminclient = adminlogin(".env")

# With priorities
planner_df = baseplanner(adminclient, "221", PLANNER_ID="4")

# Without priorities
planner_df = baseplanner(adminclient, "221")

Returns: DataFrame with session details, keywords, drugs, priorities, dates, locations

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🧠 annotate - Knowledge Base Annotation

Annotate data using Ferma's AI knowledge base.

Input Requirements:

• DataFrame must contain an id column

from FermaCongress.auth import supportlogin
from FermaCongress.annotate import annotate
import pandas as pd

supportclient = supportlogin(".env")

# Prepare input data
input_df = pd.read_excel("input.xlsx") / pd.read_csv("input.csv")

# Annotate
result = annotate(
    client=supportclient,
    input_df=input_df,
    needles=[1, 1],        # [kb_status, nct_status]: 1=enabled, 0=disabled
    long_table=True        # True=long format, False=pivot format
)

Parameters:

• needles: [kb_status, nct_status] - Enable/disable knowledge base and NCT annotation
• long_table: Output format (long or pivot)
• custom_needles_df: Optional custom keywords DataFrame

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🏥 trials - Clinical Trials

Extract trial data from ClinicalTrials.gov, EU CTIS, and EudraCT.

from FermaCongress.trials import extracttrials

df_wide, df_long = extracttrials(df, column='nct_ids', delimiter=';;')

Supported Formats:

• NCT: NCT12345678
• EU CTIS: 2022-500000-11-00
• EudraCT: 2022-500000-11

Returns: Study ID, Phase, Enrollment, Lead Sponsor, Collaborators

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⚙️ formatexcel - Excel Formatting

Apply Ferma-branded styling to Excel files.

from FermaCongress.formatexcel import format

# From DataFrame
format(dataframe=df, output_path="output.xlsx")

# From Excel file
format(input_path="raw.xlsx", output_path="formatted.xlsx", input_sheet="Sheet1")

# From CSV file
format(input_path="data.csv", output_path="output.xlsx", output_sheet="Sheet1")

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