app.py

import streamlit as st
from graphviz import Digraph 

st.markdown("""
# Top Five ML Libraries:
1. PyTorch: PyTorch is another popular open-source machine learning library that is widely used for building deep learning models. It is known for its ease of use and flexibility.
2. TensorFlow: Developed by Google, TensorFlow is an open-source library that is widely used for building and training deep learning models.
3. Scikit-learn: Scikit-learn is a popular Python library for machine learning. It provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction, along with tools for model selection and evaluation.
4. Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is designed to be easy to use and supports both convolutional and recurrent neural networks.
5. MXNet: MXNet is a deep learning framework that is known for its speed and scalability. It supports multiple programming languages and is used by several large companies for machine learning tasks.

# Pytorch demo with knowledge trees
""")

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import torch
def plot_knowledge_trees(knowledge_trees):
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    
    xs = torch.arange(len(knowledge_trees))
    ys = torch.arange(len(knowledge_trees[0]))
    xs, ys = torch.meshgrid(xs, ys)
    
    zs = []
    for i in range(len(knowledge_trees)):
        zs.append([kt[i] for kt in knowledge_trees])
    
    zs = torch.tensor(zs)
    ax.plot_surface(xs.numpy(), ys.numpy(), zs.numpy(), cmap='coolwarm')
    
    ax.set_xlabel('States')
    ax.set_ylabel('Knowledge Trees')
    ax.set_zlabel('Number of Nodes')
    
    return fig

def plot_population(states, populations):
    fig, ax = plt.subplots()
    ax.bar(states, populations)
    
    ax.set_xlabel('States')
    ax.set_ylabel('Population')
    
    return fig


def main():
    st.title('State Populations and Knowledge Trees')
    
    # Define the state populations
    states = ['California', 'Texas', 'Florida', 'New York']
    populations = [39538223, 29145505, 21538187, 19849399]
    
    # Define the knowledge trees for each state
    knowledge_trees = [
        [100, 200, 300, 400],
        [150, 250, 350, 450],
        [120, 220, 320, 420],
        [130, 230, 330, 430]
    ]
    
    # Generate the plots
    st.write('## State Populations')
    fig_population = plot_population(states, populations)
    st.pyplot(fig_population)
    
    st.write('## Descending Order Lists of Knowledge Trees')
    fig_knowledge_trees = plot_knowledge_trees(knowledge_trees)
    st.pyplot(fig_knowledge_trees)

if __name__ == '__main__':
    main()

st.markdown("""
# Health Related Examples:
1. Health Conditions By State:  https://huggingface.co/spaces/awacke1/HealthConditionsTest
2. Mental Health ICD10 and DSM Codes: https://huggingface.co/spaces/awacke1/Mental-Health-ICD10-to-DSM
3. Health Care and AI Datasets: https://huggingface.co/spaces/awacke1/Health-Care-AI-and-Datasets
4. Zero Shot Classifier Facebook: https://huggingface.co/spaces/awacke1/Zero-shot-classification-facebook-bart-large-mnli
5. Zero Shot Classifier Valhalla: https://huggingface.co/spaces/awacke1/Zero-Shot-Classification-valhalla-distilbart-mnli-12-1

""")

st.markdown("""
# 👋 Two easy ways to turbo boost your AI learning journey! 💻
# 🌐 AI Pair Programming 
## Open 2 Browsers to:
1. __🌐 ChatGPT__ [URL](https://chat.openai.com/chat) or [URL2](https://platform.openai.com/playground) and 
2. __🌐 Huggingface__ [URL](https://huggingface.co/awacke1)  in separate browser windows.
1. 🤖 Use prompts to generate a streamlit program on Huggingface or locally to test it.
2. 🔧 For advanced work, add Python 3.10 and VSCode locally, and debug as gradio or streamlit apps.
3. 🚀 Use these two superpower processes to reduce the time it takes you to make a new AI program! ⏱️
# 🎥 YouTube University Method:
1. 🏋️‍♀️ Plan two hours each weekday to exercise your body and brain.
2. 🎬 Make a playlist of videos you want to learn from on YouTube. Save the links to edit later.
3. 🚀 Try watching the videos at a faster speed while exercising, and sample the first five minutes of each video.
4. 📜 Reorder the playlist so the most useful videos are at the front, and take breaks to exercise.
5. 📝 Practice note-taking in markdown to instantly save what you want to remember. Share your notes with others!
6. 👥 AI Pair Programming Using Long Answer Language Models with Human Feedback:
## 🎥 2023 AI/ML Advanced Learning Playlists:
1. [2023 QA Models and Long Form Question Answering NLP](https://www.youtube.com/playlist?list=PLHgX2IExbFovrkkx8HMTLNgYdjCMNYmX_)
2. [FHIR Bioinformatics Development Using AI/ML and Python, Streamlit, and Gradio - 2022](https://www.youtube.com/playlist?list=PLHgX2IExbFovoMUC3hYXeFegpk_Y0Lz0Q)
3. [2023 ChatGPT for Coding Assistant Streamlit, Gradio and Python Apps](https://www.youtube.com/playlist?list=PLHgX2IExbFouOEnppexiKZVdz_k5b0pvI)
4. [2023 BigScience Bloom - Large Language Model for AI Systems and NLP](https://www.youtube.com/playlist?list=PLHgX2IExbFouqnsIqziThlPCX_miiDq14)
5. [2023 Streamlit Pro Tips for AI UI UX for Data Science, Engineering, and Mathematics](https://www.youtube.com/playlist?list=PLHgX2IExbFou3cP19hHO9Xb-cN8uwr5RM)
6. [2023 Fun, New and Interesting AI, Videos, and AI/ML Techniques](https://www.youtube.com/playlist?list=PLHgX2IExbFotoMt32SrT3Xynt5BXTGnEP)
7. [2023 Best Minds in AGI AI Gamification and Large Language Models](https://www.youtube.com/playlist?list=PLHgX2IExbFotmFeBTpyje1uI22n0GAkXT)
8. [2023 State of the Art for Vision Image Classification, Text Classification and Regression, Extractive Question Answering and Tabular Classification](https://www.youtube.com/playlist?list=PLHgX2IExbFotPcPu6pauNHOoZTTbnAQ2F)
9. [2023 AutoML DataRobot and AI Platforms for Building Models, Features, Test, and Transparency](https://www.youtube.com/playlist?list=PLHgX2IExbFovsY2oGbDwdEhPrakkC8i3g)
""")


st.markdown("""
# 📚 Clinical Terminology and Ontologies [Example 🩺⚕️NLP Clinical Ontology Biomedical NER](https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology)
## Health Vocabularies, Systems of Coding, and Databases with Bibliographies
##__Keywords__:
1. __Clinical Terminology__: 💬 Words that doctors use to talk to each other about patients.
2. __Ontologies for Medications and Conditions__: 📚 A fancy way of organizing knowledge about medicine and health problems.
3. __Health Vocabularies__: 📝 A special list of words used in healthcare to talk about health issues.
4. __Systems of Coding__: 💻 A way of giving things like sicknesses and treatments special codes, so that doctors can remember them easily.
5. __Databases__: 🗄️ A computer system that stores information about patients, health research, and other healthcare things.
6. __Bibliographies__: 📖 A list of books or articles that doctors use to learn about new health information.
1. ## 1️⃣ National Library of Medicine's **RxNorm**:
    - Standardized nomenclature for clinical drugs developed by NLM
    - Provides links between drug names and related information such as ingredients, strengths, and dosages
    - **Data type: controlled vocabulary**
    - Access through **NLM's RxNorm website**: https://www.nlm.nih.gov/research/umls/rxnorm/index.html
2. ## 2️⃣ Centers for Medicare and Medicaid Services' Healthcare Common Procedure Coding System (HCPCS):
    - Coding system used to identify healthcare **services, procedures, and supplies**
    - Includes **codes for drugs, biologicals, and other items** used in medical care
    - **Data type: coding system**
    - Access through **CMS website**: https://www.cms.gov/Medicare/Coding/MedHCPCSGenInfo
3. ## 3️⃣ Unified Medical Language System (UMLS):
    - Set of files and software tools developed by NLM for integrating and mapping biomedical vocabularies
    - Includes RxNorm and other drug vocabularies, as well as other terminologies used in medicine
    - **Data type: controlled vocabulary**
    - Access through UMLS Metathesaurus: https://www.nlm.nih.gov/research/umls/index.html
4. ## 4️⃣ PubMed:
    - Database of **biomedical literature** maintained by the National Center for Biotechnology Information (NCBI)
    - Includes information about **drugs, including drug names, chemical structures, and pharmacological actions**
    - **Data type: bibliographic database**
    - Access through **PubMed website**: https://pubmed.ncbi.nlm.nih.gov/
5. ## 5️⃣ PubChem:
    - Database of chemical substances maintained by NCBI
    - Includes information about drugs, including **chemical structures, properties, and activities**
    - **Data type: chemical database**
    - Access through **PubChem website**: https://pubchem.ncbi.nlm.nih.gov/
6. ## 6️⃣ Behavioral Health Code Terminology Sets:
    - Code terminology sets specific to behavioral health
    - Includes **DSM** published by American Psychiatric Association, **ICD** published by World Health Organization, and **CPT** published by American Medical Association
    - **Data type: coding system**
    - Access through respective **organizations' websites**:
        1. [DSM](https://www.psychiatry.org/psychiatrists/practice/dsm)
        2. [ICD](https://www.who.int/standards/classifications/classification-of-diseases)
        3. [CPT](https://www.ama-assn.org/practice-management/cpt/current-procedural-terminology-cpt)
""")


st.markdown("""
1. # 📚Natural Language Processing🔤 - 🗣️🤖💭💬🌍🔍
    1. 🤔 **🩺⚕️ Sentiment analysis** - Determine underlying sentiment of text.  [Example](https://huggingface.co/spaces/awacke1/Sentiment-analysis-streamlit)
    2. 📝 **Named Entity Recognition (NER)** - Identify and classify named entities in text.  [Example](https://huggingface.co/spaces/awacke1/Named-entity-resolution)
    3. 🔊 **🩺⚕️Automatic Speech Recognition (ASR)** - Transcribe spoken language into text.
    # Advanced NLP ASR Examples:
        1. 🩺⚕️ https://huggingface.co/spaces/awacke1/ASR-High-Accuracy-Test
        2. https://huggingface.co/spaces/awacke1/ASRGenerateStory
        3. 🩺⚕️ https://huggingface.co/spaces/awacke1/TTS-STT-Blocks
        4. 🩺⚕️ https://huggingface.co/spaces/awacke1/CloneAnyVoice
        5. https://huggingface.co/spaces/awacke1/ASR-SOTA-NvidiaSTTMozilla
    4. 🌐 **Machine translation** - Translate text between languages automatically.  [Example](https://huggingface.co/spaces/awacke1/Machine-translation)
    5. 📄 **Text summarization** - Automatically summarize large volumes of text.  [Example](https://huggingface.co/spaces/awacke1/Text-summarization)
    6. ❓ **🩺⚕️ Question answering** - Answer questions posed in natural language.  [Example](https://huggingface.co/spaces/awacke1/Question-answering)
    7. 🤖 **Sentiment-aware chatbots** - Use sentiment analysis to detect user emotions and respond appropriately.
    8. 📊 **🩺⚕️ Text classification** - Classify text into different categories.  [Example](https://huggingface.co/spaces/awacke1/sileod-deberta-v3-base-tasksource-nli)
    9. 💬 **🩺⚕️ Text generation** - Generate natural language text.  [Example](https://huggingface.co/spaces/awacke1/Sentence2Paragraph)
    10. 🔎 **Topic modeling** - Automatically identify topics in a large corpus of text.  [Example](https://huggingface.co/spaces/awacke1/Topic-modeling)
    - Examples
        1. [NLP Video Summary](https://huggingface.co/spaces/awacke1/Video-Summary)
        2. [TTS-STT ASR with Multiple Voices](https://huggingface.co/spaces/awacke1/TTS-STT-Blocks)
        3. [NLP Transcript with Video Player](https://huggingface.co/spaces/awacke1/Streamlit-ASR-Video)
        4. [NLP Clinical Ontology Biomedical NER](https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology)
        5. [Document Understanding and NLP](https://huggingface.co/spaces/awacke1/AIDocumentUnderstandingOCR)
        6. [NLP ASR Wav2Vec2 Multilingual](https://huggingface.co/spaces/awacke1/ASR-High-Accuracy-Test)
        7. [Live ASR](https://huggingface.co/spaces/awacke1/ASR-SOTA-NvidiaSTTMozilla)
        8. [NLP and Visualization](https://huggingface.co/spaces/awacke1/Visualization-Plotly-Sunbursts-Treemaps-and-WebGL)
""")





st.markdown("""
2. # 🔮Generative AI💭 (🎨Images and 📝Text)  -  🎵🧩🔄📊🌌
    1. 🆕 **🩺⚕️ Generation of new data**: Create new data that resembles existing data. [Example](https://huggingface.co/spaces/awacke1/GenAI-Generate-New-Data-Resembling-Example)
    2. 🎨 **Creative potential**: Generate music, art, or literature. [Example](https://huggingface.co/spaces/awacke1/Creative-Potential-Music-Art-Lit)
    3. 📊 **Data synthesis**: Synthesize data from multiple sources to create new datasets.  [Example](https://huggingface.co/spaces/awacke1/Data-Synthesizer-Synthesize-From-Multiple-Sources)
    4. 📈 **🩺⚕️ Data augmentation**: Augment existing datasets to make them larger and more diverse.  [Example](https://huggingface.co/spaces/awacke1/Data-Augmentation)
    5. 🔀 **Domain transfer**: Transfer knowledge learned from one domain to another.
    6. 🔍 **Unsupervised learning**: Learn patterns without labeled training data.
    7. 🔄 **Adaptive learning**: Adapt to changes in data over time.
    8. 🔊 **Noise injection**: Introduce noise to explore a wider range of possibilities.
    9. 🕶️ **Latent space manipulation**: Control output by manipulating a model's latent space.
    10. 🖼️ **Realistic output**: Produce output that is difficult to distinguish from human-created data.
    - Examples
        1. Quantum AI Circuits: https://huggingface.co/spaces/awacke1/AI-Quantum?option=Circuit
        2. Generate Story and Video: https://huggingface.co/spaces/awacke1/ASRGenerateStoryandVideo
        3. ASR Generate Story: https://huggingface.co/spaces/awacke1/ASRGenerateStory
        4. Music Generation: https://huggingface.co/spaces/awacke1/MusicMaker
""")




st.markdown("""
3. # 📷Image Recognition🏞️ 
    1. 📷 **Object detection**: Detect and identify multiple objects in an image for detailed analysis and classification.
    2. 🏞️ **Scene recognition**: Recognize and classify entire scenes based on objects, colors, and shapes.
    3. 😃 **Facial recognition**: Analyze facial features for accurate identification.
    4. 😊 **Emotion recognition**: Identify emotions on a subject's face, including happiness, sadness, and anger.
    5. 🔤 **Text recognition**: Identify and translate text in images for analysis.
    6. 🎨 **Color recognition**: Detect colors and provide information on hue, saturation, and brightness.
    7. 🔍 **Image segmentation**: Divide an image into multiple regions for individual analysis and classification.
    8. 🌅 **Image restoration**: Remove noise and blur, restoring images to original clarity and quality.
    9. 🔖 **Image classification**: Classify images into categories like animals, buildings, or landscapes.
    10. 🎨 **Style transfer**: Apply the style of one image to another for unique and innovative results.
    - Examples
        1. 🩺⚕️ Text-to-Image : [Image Classification](https://huggingface.co/spaces/awacke1/Prompt-Refinery-Text-to-Image-Generation)
        2. Image Captions from 5 SOTA Generators: [URL](https://huggingface.co/spaces/awacke1/ImageCaptionPromptGenerator)
        3. 🩺⚕️ Image to Multilingual OCR: [URL](https://huggingface.co/spaces/awacke1/Image-to-Multilingual-OCR)
        4. WRN - Wide Residual Networks: [URL](https://huggingface.co/spaces/awacke1/ResnetPytorchImageRecognition)
        5. AI Document Understanding: [URL](https://huggingface.co/spaces/awacke1/AIDocumentUnderstandingOCR)
        6. Elixir Docker Bumblebee: [URL](https://huggingface.co/spaces/awacke1/DockerImageRecognitionToText)
        7. Speech to Text to Story to Images to Video: [URL](https://huggingface.co/spaces/awacke1/Speeech2Text2Story2Images2Video)
        8. Image to Line Drawings: [URL](https://huggingface.co/spaces/awacke1/Image-to-Line-Drawings)
        9. Semantic Image Search: [URL](https://huggingface.co/spaces/awacke1/Image-Semantic-Search)
        10. Zoom Clip Toon: [URL](https://huggingface.co/spaces/awacke1/Zoom-Clip-Toon-Image-to-Image)
        11. Image to Reading Labels: [URL](https://huggingface.co/spaces/awacke1/ImageOCRMultilingual)
        12. A Game For That - Gamification Using Snapshot Images: [URL](https://huggingface.co/spaces/awacke1/AGameForThat)
        13. AI Visually Plays QBert, Pong, Seaquest and more: [URL](https://huggingface.co/spaces/awacke1/AI-Atari-Live-Streamlit)
        14. AI Creates Generator Style Mix Art from Encyclopedia: [URL](https://huggingface.co/spaces/awacke1/Art-Generator-and-Style-Mixer)
        15. BigGAN Image Gen and Search: [URL](https://huggingface.co/spaces/awacke1/AI-BigGAN-Image-Gen)
        16. Art Style Line Drawings: [URL](https://huggingface.co/spaces/awacke1/ArtStyleFoodsandNutrition)
        17. 🩺⚕️ Yolo Real Time Image Recognition from Webcam: https://huggingface.co/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco
""")

st.markdown("""
4. # 🗣️Speech Recognition💬
    1. 🔊 **Continuous Speech Recognition**: Transcribe spoken words in real-time without pausing.
    2. 🗣️ **Speaker Identification**: Identify individual speakers through unique features in their speech.
    3. 🧠 **Contextual Awareness**: Understand conversation context and interpret word meaning.
    4. 🌎 **Multilingual Support**: Recognize and transcribe multiple languages for translation.
    5. 🔇 **Noise Reduction**: Filter out background noise to improve transcription quality.
    6. 🔒 **Voice Biometrics**: Verify speaker identity and provide secure access to personal data.
    7. 🎛️ **Command and Control**: Interpret voice commands to automate tasks and interact with software.
    8. 💬 **Natural Language Processing**: Understand complex human speech patterns.
    9. 🧠 **Adaptive Learning**: Learn and adapt to improve accuracy over time.
    10. ☁️ **Cloud-Based Deployment**: Real-time processing of large amounts of data, even on mobile devices.
""")

st.markdown("""
5. # Reinforcement Learning
    1. 🏆 **Reward-driven**: RL uses rewards or punishments to drive its learning process.
    2. 🧪 **Trial-and-error learning**: RL is a trial-and-error learning method, where an agent tries different actions to find the best action that will maximize the cumulative reward.
    3. 🤔 **Exploration-exploitation trade-off**: RL agents need to balance exploration and exploitation to find new possibilities while also exploiting successful actions.
    4. 📈 **Markov Decision Processes**: RL uses MDPs to model decision-making processes.
    5. 📊 **Policy optimization**: RL uses policy optimization techniques to find the best policy for a given task or learn the optimal policy from scratch.
    6. 💰 **Value-based methods**: RL uses value-based methods to estimate the value of each state or action.
    7. 🧠 **Model-based methods**: RL can use model-based methods to predict the outcomes of different actions.
    8. 🤖 **Deep Reinforcement Learning**: DRL combines RL with deep learning techniques to learn complex decision-making tasks.
    9. 🔄 **Transfer learning**: RL can use transfer learning techniques to transfer knowledge learned in one task to another task.
    10. 🤝 **Multi-agent RL**: RL can handle multiple agents that interact with each other.
""")

st.markdown("""
6. 🎲Game Theory🎲 – Traditional AI processes
    1. 🤝 **Interdependence**: Game Theory considers decision-making among multiple agents, unlike traditional AI processes which focus on a single agent.
    2. 🎯 **Strategic Behavior**: Game Theory assumes that agents aim to maximize their payoffs based on the actions of other agents. Traditional AI may not consider this strategic element.
    3. 💰 **Payoffs**: Game Theory calculates payoffs for each agent based on their actions and the actions of other agents, unlike traditional AI which may focus on a single objective.
    4. ⚖️ **Equilibrium**: Game Theory seeks to identify stable states in the game where no agent has an incentive to deviate from their current strategy. Traditional AI may not seek to find an equilibrium.
    5. 🎲 **Game Formulation**: Game Theory formulates a game, including rules, players, and possible actions, unlike traditional AI which may not require such formulation.
    6. 💡 **Solution Concepts**: Game Theory has various solution concepts, such as Nash Equilibrium and Pareto Efficiency, to identify the most desirable outcomes. Traditional AI may not have such concepts.
    7. 📊 **Information**: Game Theory considers the information available to each agent in the game. Traditional AI may not consider information explicitly.
    8. ⚔️ **Adversarial**: Game Theory models adversarial scenarios where agents have conflicting goals. Traditional AI may assume cooperation among agents.
    9. ❓ **Uncertainty**: Game Theory deals with uncertainty and incomplete information in the game. Traditional AI may not consider uncertainty.
    10. 🌐 **Complexity**: Game Theory deals with complex multi-agent interactions. Traditional AI may focus on single-agent optimization.
    - Examples
        1. 🩺⚕️ Health Care Game: https://huggingface.co/spaces/awacke1/AI-RPG-Self-Play-RLML-Health-Battler-Game
        2. 🩺⚕️ Sankey Snacks Math Chart Animator:  https://huggingface.co/spaces/awacke1/Sankey-Snacks
        3. Blackjack 21 : https://huggingface.co/spaces/awacke1/BlackjackSimulatorCardGameAI
        4. Player Card Monster Battler: https://huggingface.co/spaces/awacke1/Player-Card-Monster-Battler-For-Math-and-AI
        5. Emojitrition: https://huggingface.co/spaces/awacke1/Emojitrition-Fun-and-Easy-Nutrition
""")




st.markdown("""
# 🩺⚕️ Reinforcement Learning from Human Feedback (RLHF) 
## 🤖 RLHF is a way for computers to learn how to do things better by getting help and feedback from people, 
    - just like how you learn new things from your parents or teachers.
🎮 Let's say the computer wants to learn how to play a video game. 
    - It might start by trying different things and seeing what happens.
👍 If it does something good, like getting a high score, it gets a reward.
👎 If it does something bad, like losing a life, it gets a punishment.
👩‍💻 Now, imagine that a person is watching the computer play the game and giving it feedback. 
    -The person might say things like "Good job!" when the computer gets a high score 
    - or "Oops, try again!" when it loses a life.
💡 This feedback helps the computer figure out which actions are good and which ones are bad. 
    -The computer then uses this feedback to adjust its actions and get better at playing the game.
🤔 It might try different strategies and see which ones get the best feedback from the person. 
    -Over time, the computer gets better and better at playing the game, just like how you get better at things by practicing and getting help from others.
🚀 RLHF is a cool way for computers to learn and improve with the help of people. 
    -Who knows, maybe one day you can teach a computer to do something amazing!
# Examples
## 🩺⚕️ Hospital Visualizations
🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsMinnesota
🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsNewJersey
🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsMentalHealth
🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-GraphViz-Folium-MapTopLargeHospitalsinWI
# Card Game Activity
https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
https://huggingface.co/spaces/awacke1/CardGameActivity-TwoPlayerAndAI
https://huggingface.co/spaces/awacke1/CardGameActivity
https://huggingface.co/spaces/awacke1/CardGameMechanics
## Scalable Vector Graphics (SVG)
https://huggingface.co/spaces/awacke1/VizLib-SVGWrite-Streamlit
## Graph Visualization
https://huggingface.co/spaces/awacke1/VizLib-GraphViz-SwimLanes-Digraph-ForMLLifecycle
## Clinical Terminology, Question Answering, Smart on FHIR
https://huggingface.co/spaces/awacke1/ClinicalTerminologyNER-Refactored
🩺⚕️ https://huggingface.co/spaces/awacke1/Assessment-By-Organs
🩺⚕️ https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Test2
🩺⚕️ https://huggingface.co/spaces/awacke1/FHIRLib-FHIRKit
""")