The complete AI toolkit that turns complex workflows into simple Python commands. From medical diagnosis to compliance checking, document analysis to face recognition - NewberryAI brings enterprise-grade AI capabilities to your fingertips.
pip install newberryaibashsudo apt-get install -y portaudio19-dev
pip install pyaudiobrew install portaudio then pip install pyaudioDownload pre-compiled wheels from "https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyaudio" and
pip install PyAudio‑0.2.11‑cp312‑cp312‑win_amd64.whlreplace the filename with the one you downloaded
- HealthScribe: Medical transcription with AWS HealthScribe
- Differential Diagnosis Assistant(DDX): Clinical diagnosis support
- Medical Bill Extractor: Automated medical billing analysis
- Compliance Checker: Video analysis for regulatory compliance
- PII Redactor: Remove personally identifiable information
- PII Extractor: Extract and categorize sensitive data
- EDA Assistant: Automated exploratory data analysis
- NL2SQL: Natural language to SQL query conversion
- Excel Formula Generator: AI-powered Excel formula creation
- PDF Summarizer: Intelligent document summarization
- PDF Extractor: Semantic search and content extraction
- Video Generator: Text-to-video with Amazon Bedrock Nova
- Image Generator: Text-to-image with Titan Image Generator
- Virtual Try-On: AI-powered fashion visualization
- Face Recognition: Identity management with AWS Rekognition
- Face Detection: Video face detection and tracking
- Coding Assistant: Code review and debugging support
- Speech-to-Speech: Real-time voice interaction
# AWS Credentials
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_DEFAULT_REGION=us-east-1#Analyze videos for regulatory compliance. Requires AWS credentials.
from newberryai import ComplianceChecker
checker = ComplianceChecker()
video_file = 'YOUR.mp4'
compliance_question = 'Is the video compliant with safety regulations such as mask?'
result, status_code = checker.check_compliance(
video_file=video_file,
prompt=compliance_question
)
if status_code:
print(f'Error: {result.get("error", "Unknown error")})')
else:
print(f'Compliant: {"Yes" if result["compliant"] else "No"}')
print(f'Analysis: {result["analysis"]}')newberryai compliance --video_file YOUR.mp4 --question "Is the video compliant with safety regulations such as mask?"
newberryai compliance --gradio# AWS Credentials
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_DEFAULT_REGION=us-east-1
HEALTHSCRIBE_INPUT_BUCKET=your-healthscribe-input-bucket
HEALTHSCRIBE_OUTPUT_BUCKET=your-healthscribe-output-bucket
HEALTHSCRIBE_DATA_ACCESS_ROLE=arn:aws:iam::account:role/your-role# Medical transcription using AWS HealthScribe. Requires AWS credentials.
from newberryai import HealthScribe
scribe = HealthScribe(
input_s3_bucket='your-input-bucket',
data_access_role_arn='arn:aws:iam::992382417943:role/YOUR-role'
)
result = scribe.process(
file_path=r'YOUR_AUDIO.mp3',
job_name='JOB-NAME',
output_s3_bucket='your-output-bucket'
)
print(result["summary"])newberryai healthscribe --file_path YOUR_AUDIO.mp3 --job_name sdktest --output_s3_bucket dax-healthscribe-v2
newberryai healthscribe --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Get assistance with clinical diagnosis.
from newberryai import DDxChat
ddx_chat = DDxChat()
response = ddx_chat.ask('Patient presents with fever, cough, and fatigue for 5 days')
print(response)newberryai ddx --question "Patient presents with fever, cough, and fatigue for 5 days"
newberryai ddx --interactive
newberryai ddx --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Get assistance with Excel formulas.
from newberryai import ExcelExp
excel_expert = ExcelExp()
response = excel_expert.ask(
"Calculate average sales for products that meet specific criteria E.g: give me excel formula to calculate average of my sale for year 2010,2011 sales is in col A, Year in Col B and Months in Col C"
)
print(response)newberryai excel --question "Calculate average sales for products that meet specific criteria E.g: give me excel formula to calculate average of my sale for year 2010,2011 sales is in col A, Year in Col B and Months in Col C"
newberryai excel --interactive
newberryai excel --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key
# AWS Credentials
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key#Extract and analyze data from medical bills.
from newberryai import Bill_extractor
extractor = Bill_extractor()
analysis = extractor.analyze_document('Billimg.jpg')
print(analysis)newberryai bill --file_path Billimg.jpg
newberryai bill --interactive
newberryai bill --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Analyze code and help you with coding as debugger.
from newberryai import CodeReviewAssistant
code_debugger = CodeReviewAssistant()
response = code_debugger.ask('''Explain and correct below code
def calculate_average(nums):
sum = 0
for num in nums:
sum += num
average = sum / len(nums)
return average
numbers = [10, 20, 30, 40, 50]
result = calculate_average(numbers)
print("The average is:", results)''')
print(response)newberryai code --question "Explain and correct below code
def calculate_average(nums):
sum = 0
for num in nums:
sum += num
average = sum / len(nums)
return average
numbers = [10, 20, 30, 40, 50]
result = calculate_average(numbers)
print(\"The average is:\", results)"
newberryai code --interactive
newberryai code --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Analyze text and remove PII (personally identifiable information) from the text.
from newberryai import PII_Redaction
pii_red = PII_Redaction()
response = pii_red.ask("Patient name is John Doe with fever. he is from Austin,Texas.His email id is john.doe14@email.com")
print(response)newberryai pii --text "Patient name is John Doe with fever. he is from Austin,Texas.His email id is john.doe14@email.com"
newberryai pii --interactive
newberryai pii --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Analyze text and extract PII (personally identifiable information) from the text.
from newberryai import PII_extraction
pii_extract = PII_extraction()
response = pii_extract.ask("Patient name is John Doe with fever. he is from Austin,Texas.His email id is john.doe14@email.com")
print(response)newberryai pii --text "Patient name is John Doe with fever. he is from Austin,Texas.His email id is john.doe14@email.com"
newberryai pii --interactive
newberryai pii --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Perform detailed data exploration with real statistics, hypothesis testing, and actionable insights—no code, just direct analysis
from newberryai import EDA
import pandas as pd
eda = EDA()
eda.current_data = pd.read_csv(r'your_csv.csv')
response = eda.ask("What is the average value of column 'xyz'?")
print(response)newberryai eda --file_path your_csv.csv --question "What is the average value of column 'target'?"
newberryai eda --interactive
newberryai eda --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Extract and summarize content from PDF documents.
from newberryai import DocSummarizer
summarizer = DocSummarizer()
response = summarizer.ask(r'YOUR-pdf.pdf')
print(response)newberryai doc --file_path YOUR-pdf.pdf --question "Extract and summarize content from PDF documents."
newberryai doc --interactive
newberryai doc --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key#Extract and query content from PDF documents using embeddings and semantic search. (Async usage)
import asyncio
from newberryai import PDFExtractor
async def pdf_extract_demo():
extractor = PDFExtractor()
pdf_id = await extractor.process_pdf(r'YOUR-pdf.pdf')
response = await extractor.ask_question(pdf_id, 'What is the mode of review in the document?')
print(response['answer'])
print("\nSource Chunks:")
for chunk in response['source_chunks']:
print(f"\n---\n{chunk}")
#To run the async demo in a notebook cell:
await pdf_extract_demo()newberryai pdf --file_path YOUR-pdf.pdf --question "What is the mode of review in the document?"
newberryai pdf --interactive
newberryai pdf --gradio# AWS Credentials
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
BEDROCK_REGION=us-east-1
BEDROCK_MODEL_ID=amazon.nova-canvas-v1:0#Generate videos from text using Amazon Bedrock's Nova model. Requires AWS credentials.
# Example usage
from newberryai import VideoGenerator
generator = VideoGenerator()
prompt = "A cat dancing on a wall"
async def run_video():
response = await generator.generate(
text=prompt,
duration_seconds=6,
fps=24,
dimension="1280x720",
seed=42
)
print(response["message"])
print("Waiting for video to complete...")
final_response = await generator.wait_for_completion(response["job_id"])
print(final_response["message"])
print(f"Video URL: {final_response['video_url']}")
await run_video()newberryai video --text "A cat dancing on a wall" --duration_seconds 6 --fps 24 --dimension 1280x720 --seed 42
newberryai video --interactive
newberryai video --gradio# AWS Credentials
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
BEDROCK_REGION=us-east-1
BEDROCK_MODEL_ID=amazon.titan-image-generator-v1from newberryai.image_generator import ImageGenerator
import asyncio
generator = ImageGenerator()
prompt = "A lotus in a pond"
result = await generator.generate(
text= prompt,
width=512,
height=512,
number_of_images=1,
cfg_scale=8,
seed=42,
quality="standard"
)
print(result["message"])
for path in result["images"]:
print(f"Generated image path: {path}")newberryai image --text "A lotus in a pond" --width 512 --height 512 --number_of_images 1 --cfg_scale 8 --seed 42 --quality standard
newberryai image --interactive
newberryai image --gradio# AWS Credentials
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
REKOGNITION_COLLECTION_ID=your-collection-id
REKOGNITION_REGION=us-east-1# Import the FaceRecognition class
from newberryai import FaceRecognition
# Initialize face recognition
face_recognition = FaceRecognition()
# Add a face to the collection and recognize a face in one go
add_response = face_recognition.add_to_collect("yourimg.jpeg", "Name")
print(add_response["message"], f"Face ID: {add_response.get('face_id', 'N/A')}")
# Recognize a face from another image
recognize_response = face_recognition.recognize_image("yourimg2.jpeg")
print(recognize_response["message"])
if recognize_response["success"]:
print(f"Recognized: {recognize_response['name']} (Confidence: {recognize_response['confidence']:.2f}%)")newberryai face --image_path yourimg.jpeg --name Name
newberryai face --image_path yourimg2.jpeg --recognize
newberryai face --interactive
newberryai face --gradio# AWS Credentials
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
REKOGNITION_COLLECTION_ID=your-collection-id
REKOGNITION_REGION=us-east-1# Import and initialize FaceDetection class
from newberryai.face_detection import FaceDetection
face_detector = FaceDetection()
# Add face to collection
add_response = face_detector.add_face_to_collection("yourimg.jpeg", "kirti")
print(add_response["message"])
if add_response["success"]:
print(f"Face ID: {add_response['face_id']}")
# Process video
results = face_detector.process_video("yourvideo.mp4", max_frames=20)
for detection in results:
print(f"\nTimestamp: {detection['timestamp']}s")
if detection.get('external_image_id'):
print(f"Matched Face: {detection['external_image_id']}")
print(f"Face ID: {detection['face_id']}")
print(f"Confidence: {detection['confidence']:.2f}%")
else:
print("No match found in collection")newberryai face --image_path yourimg.jpeg --add_to_collection
newberryai face --video_path yourvideo.mp4 --max_frames 20
newberryai face --interactive
newberryai face --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key
# Database Configuration
DB_HOST=localhost
DB_USER=your_db_username
DB_PASSWORD=your_db_password
DB_NAME=your_database_name
DB_PORT=3306#Natural Language to SQL Query Assistant
# Import the NL2SQL class
from newberryai import NL2SQL
import json
# Initialize NL2SQL processor
nl2sql = NL2SQL()
# Set up the database connection parameters (adjust these accordingly)
host = "127.0.0.1"
user = "user-name"
password = "passward"
database = "your-database-name"
port = 3306
# Connect to the database
nl2sql.connect_to_database(host, user, password, database, port)
# Test a natural language question to SQL conversion
question = "Show all tables"
response = nl2sql.process_query(question)
# Print the results: SQL query, data, and summary
if response["success"]:
print(f"Generated SQL Query: {response['sql_query']}")
print(f"Data: {json.dumps(response['data'], indent=2)}")
print(f"Summary: {response['summary']}")
else:
print(f"Error: {response['message']}")newberryai sql --question "Show all tables"
newberryai sql --interactive
newberryai sql --gradio# Fashn API Configuration
FASHN_API_KEY=your_fashn_api_key
FASHN_API_URL=https://api.fashn.ai/v1# Generate virtual try-on images using AI. Requires Fashn API credentials.
import base64
import asyncio
from newberryai import VirtualTryOn
try_on = VirtualTryOn()
request = await try_on.process(
model_image='model.jpg',
garment_image='image.png',
category='tops'
)
async def tryon_demo():
job_id = request["job_id"]
while True:
status = await try_on.get_status(job_id)
if status["status"] in ['completed', 'failed']:
break
await asyncio.sleep(3)
if status["status"] == "completed" and status["output"]:
print('Generated images:')
for url in status["output"]:
print(url)
# Run the demo
await tryon_demo()newberryai tryon --model_image model.jpg --garment_image image.png --category tops
newberryai tryon --interactive
newberryai tryon --gradio# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key# Real-time voice interaction with an AI assistant.
from newberryai.speechtospeech import RealtimeApp
# Ensure you have set your OPENAI_API_KEY environment variable
# and installed necessary audio drivers and dependencies (`pip install "openai[realtime]"`).
app = RealtimeApp()
app.run()newberryai speech_to_speech- Python 3.8+
- AWS Account (for AWS-powered features)
- OpenAI API key
- Additional API keys for specific features:
- Fashn API credentials (for Virtual Try-On)
- Database access (for NL2SQL)
- SSL errors:
pip install --upgrade certifi export SSL_CERT_FILE=$(python -c "import certifi; print(certifi.where())")
MIT License