Update app.py

app.py

@@ -3,26 +3,28 @@ from graphviz import Digraph
 
 
 st.markdown("""
-# Goals of Cognitive AI with Human Feedback (CAHF) [Example](https://huggingface.co/spaces/awacke1/Cognitive-AI-Episodic-Semantic-Memory-Demo):
-1. Use Models to predict __outcomes__
-2. Use AI to predict **conditions, disease, opportunities** using flavors of AI with **explainability**.
-3. **Cognitive AI** - Mimics how humans reason through decision making processes.
-4. **Reasoning cycles** - "Recommended for You" reasoners - what type of person, classification of users, recommend what products
-5. **High Acuity Reasoners** - Only make decisions on rules of **what it can and cannot do within human feedback** guidelines.
-    -Emphasis on **explainability, transparency, removing administrative burden** and **protocolize** what staff is doing.
-    -Vetted by SME's, adding value of **judgement and training** to pick up **skills from human feedback**.
-    -**Alerts, Recommended Actions, and Clinical Terminology** per entity including LOINC, SNOMED, ICD10, RXNORM, SMILES, HCPCS, CPT, eCQM, SDC and FHIR.,
-6. Non static multi agent cognitive approach - real time series - factors predictive of outcome.
-7. Cognitive models take form of Ontology - for some type of computable set - relationships stored in Ontology can be ingested by reasoner
-    -Use models of world to build predictions and recommendations with answers that are cumulative with information we know
-8. Reasoners can standardize to make it easier as possible to do right thing with learned recommendation tools, questions and actions.
+
+# Cognitive AI with Human Feedback (CAHF) [Example 🩺⚕️](https://huggingface.co/spaces/awacke1/Cognitive-AI-Episodic-Semantic-Memory-Demo):
+
+1. Create and use Models to predict __outcomes__
+2. Use AI to predict **conditions, disease, and opportunities** using AI with **explainability**.
+3. **Cognitive AI** - Mimic how humans reason through decision making processes.
+4. **Reasoning cycles** - "Recommended for You" reasoners - consider type of personalized needs and classification for users, to recommend products
+5. **High Acuity Reasoners** - Make decisions on rules of **what it can and cannot do within human feedback** guidelines.
+    -Emphasizes **explainability, transparency, and removing administrative burden** to **protocolize** and improve what staff is doing.
+    -Vetted by SME's, adding value of **judgement and training** and pick up intelligence and **skills from human feedback**.
+    -**Alert, Recommended Action, and Clinical Terms** per entity with vocabularies from LOINC, SNOMED, OMS, ICD10, RXNORM, SMILES, HCPCS, CPT, CQM, HL7, SDC and FHIR.
+6. Non static multi agent cognitive approach using real time series to identify factors predictive of outcome.
+7. Cognitive models form of Ontology - to create a type of computable sets and relationships stored in Ontology then ingested by reasoner
+    -Use models of world to build predictions and recommendations with answers cumulative with information we know
+8. Reasoners standardize making it easy as possible to do right thing using transfer learning and recommendation tools with questions and actions.
 """)
 
 
+st.markdown("""
 
+# 📚 Clinical Terminology and Ontologies [Example 🩺⚕️NLP Clinical Ontology Biomedical NER](https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology)
 
-st.markdown("""
-# 📚 Clinical Terminology and Ontologies 
 ## Health Vocabularies, Systems of Coding, and Databases with Bibliographies
 ##__Keywords__:
 
@@ -66,26 +68,25 @@ st.markdown("""
         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)
-7. ## [Examples🩺⚕️NLP Clinical Ontology Biomedical NER](https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology)
 """)
 
 st.markdown("""
 1. # 📚Natural Language Processing🔤 - 🗣️🤖💭💬🌍🔍
-    1. 🤔 **Sentiment analysis** - Determine underlying sentiment of text.  [Example](https://huggingface.co/spaces/awacke1/Sentiment-analysis-streamlit)
+    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. 🔊 **Speech recognition** - Transcribe spoken language into text.
-    # Advanced NLP Examples:
-    3.1. https://huggingface.co/spaces/awacke1/ASR-High-Accuracy-Test
-    3.2. https://huggingface.co/spaces/awacke1/ASRGenerateStory
-    3.3. https://huggingface.co/spaces/awacke1/TTS-STT-Blocks
-    3.4. https://huggingface.co/spaces/awacke1/CloneAnyVoice
-    3.5. https://huggingface.co/spaces/awacke1/ASR-SOTA-NvidiaSTTMozilla
+    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)
+    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)
+    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)
@@ -100,10 +101,10 @@ st.markdown("""
 
 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)
+    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)
+    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.
@@ -130,9 +131,9 @@ st.markdown("""
     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)
+        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)
+        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)
@@ -146,7 +147,7 @@ st.markdown("""
         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
+        17. 🩺⚕️ Yolo Real Time Image Recognition from Webcam: https://huggingface.co/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco
 """)
 
 st.markdown("""
@@ -190,10 +191,10 @@ st.markdown("""
     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. Player Card Monster Battler: https://huggingface.co/spaces/awacke1/Player-Card-Monster-Battler-For-Math-and-AI
+        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. Sankey Snacks Math Chart Animator:  https://huggingface.co/spaces/awacke1/Sankey-Snacks
+        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
 """)
 
@@ -210,24 +211,25 @@ st.markdown("""
     9. 💥 **Multi-card play**: Use multiple cards at once to create powerful combos or synergies.
     10. 🗺️ **Tactical positioning**: Strategically place your cards on a game board or battlefield to gain an advantage.
     - Examples
-        1. Game Mechanics Top 20: https://huggingface.co/spaces/awacke1/CardGameMechanics
-        2. Game Activity Graph: https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
-        3. Game Mechanics Deep Dive: https://huggingface.co/spaces/awacke1/CardGameActivity
-        4. Hexagon Dice: https://huggingface.co/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game
-        5. Dice Roll Game: https://huggingface.co/spaces/awacke1/Dice-Roll-Fractals-STEM-Math
-        6. Pyplot Dice Game: https://huggingface.co/spaces/awacke1/Streamlit-Pyplot-Math-Dice-Game
-        7. SVG Card Generation: https://huggingface.co/spaces/awacke1/VizLib-SVGWrite-Streamlit
+        1. 🩺⚕️ Game Activity Graph: https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
+            - # Digraph is a class in the graphviz package that represents a directed graph. 
+            1. It is used to create graphs with nodes and edges.
+            2. It can be customized with various styles and formatting options. 
+            3. This is an example of defining a Digraph with emojis for the node labels:
+        2. 🩺⚕️ SVG Card Generation: https://huggingface.co/spaces/awacke1/VizLib-SVGWrite-Streamlit
+            - # Scalable Vector Graphics (SVG) is an important language used in UI and graphic design.
+        3. Game Mechanics Top 20: https://huggingface.co/spaces/awacke1/CardGameMechanics
+        4. Game Mechanics Deep Dive: https://huggingface.co/spaces/awacke1/CardGameActivity
+        5. Hexagon Dice: https://huggingface.co/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game
+        6. Dice Roll Game: https://huggingface.co/spaces/awacke1/Dice-Roll-Fractals-STEM-Math
+        7. Pyplot Dice Game: https://huggingface.co/spaces/awacke1/Streamlit-Pyplot-Math-Dice-Game 
 """)
 
 
 st.markdown("""
-# Examples of AI
-# Examples:
-    1. Mechanics: https://huggingface.co/spaces/awacke1/CardGameActivity
-    2. GraphViz: https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
-    3. https://huggingface.co/spaces/awacke1/CardGameActivity-TwoPlayerAndAI
-## AI For Long Question Answering
-1. 🖥️ First, we'll teach a smart computer to browse the internet and find information. https://huggingface.co/spaces/awacke1/StreamlitWikipediaChat
+
+## AI For Long Question Answering and Fact Checking [Example](🩺⚕️ https://huggingface.co/spaces/awacke1/StreamlitWikipediaChat)
+1. 🖥️ First, we'll teach a smart computer to browse the internet and find information. 
     - 🧠 It will be like having a super-smart search engine!
 2. 🤖 Then, we'll train the computer to answer questions by having it learn from how humans answer questions.
     - 🤝 We'll teach it to imitate how people find and use information on the internet.
@@ -240,12 +242,13 @@ st.markdown("""
 """)
 
 
+
 st.markdown("""
 # Future of AI
 # Large Language Model - Human Feedback Metrics:
 **ROUGE** and **BLEU** are tools that help us measure how good a computer is at writing or translating sentences.
-## [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge)
-## [BLEU](https://huggingface.co/spaces/evaluate-metric/bleu)
+## 🩺⚕️ [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge)
+## 🩺⚕️ [BLEU](https://huggingface.co/spaces/evaluate-metric/bleu)
 1. ROUGE looks at a sentence made by a computer and checks how similar it is to sentences made by humans. 
     1. It tries to see if the important information is the same.
 2. To do this, ROUGE looks at the groups of words that are the same in both the computer's sentence 
@@ -266,8 +269,11 @@ st.markdown("""
 
 st.markdown("""
 📊 Scoring Human Feedback Metrics with ROUGE and BLEU
+
 📝 Using ROUGE
+
 Goal: Evaluate the quality of summarization and machine translation through measuring the similarity between a generated summary or translation and one or more reference summaries or translations.
+
 Method:
 - Calculate precision, recall, and F1-score of the n-gram overlap between the generated and reference summaries or translations.
 - Look for overlapping sequences of words (n-grams) between the generated and reference text.
@@ -275,18 +281,25 @@ Method:
 - Compute recall as the ratio of the number of overlapping n-grams to the total number of n-grams in the reference text.
 - Compute the F1-score as the harmonic mean of precision and recall.
 - ROUGE can be computed at different n-gram levels, including unigrams, bigrams, trigrams, etc., as well as at the sentence or document level.
+
 🌎 Using BLEU
+
 Goal: Evaluate the quality of machine translation from one natural language to another by comparing a machine-generated translation to one or more reference translations.
+
 Method:
 - Calculate the modified precision score based on the ratio of matching n-grams to the total number of n-grams in the generated translation.
 - Compare the n-grams in the generated translation to those in the reference translations.
 - Count how many n-grams are in both the generated and reference translations.
 - BLEU can be computed at different n-gram levels, including unigrams, bigrams, trigrams, etc.
 - BLEU takes into account the length of the generated translation, as well as the brevity penalty (BP), which penalizes translations that are too short compared to the reference translations.
+
 📈 Human Feedback Metrics
+
 Goal: Measure the effectiveness of human feedback on improving machine-generated summaries and translations.
+
 Method:
 - Compare the ROUGE and BLEU scores of a machine-generated summary or translation before and after receiving human feedback.
+
 Example:
 1. Generate a summary or translation using a machine translation system.
 2. Calculate the ROUGE and BLEU scores for the machine-generated output.
@@ -298,7 +311,7 @@ Example:
 
 
 st.markdown("""
-# Reinforcement Learning from Human Feedback (RLHF) 
+# 🩺⚕️ 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. 
@@ -314,32 +327,42 @@ st.markdown("""
     -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!
-# Digraph is a class in the graphviz package that represents a directed graph. 
-1. It is used to create graphs with nodes and edges.
-2. It can be customized with various styles and formatting options. 
-3. Here is an example of defining a Digraph with emojis for the node labels:
-    1. from graphviz import Digraph
-    
-# Card Game Activity - Aaron
+
+# 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
-## Hospital Visualizations with Bed Count
-https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsMentalHealth
-https://huggingface.co/spaces/awacke1/VizLib-GraphViz-Folium-MapTopLargeHospitalsinWI
-https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsMinnesota
+
 ## 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-Streamlit-1
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Test2
+🩺⚕️ https://huggingface.co/spaces/awacke1/Assessment-By-Organs
+🩺⚕️ https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Test2
+🩺⚕️ https://huggingface.co/spaces/awacke1/FHIRLib-FHIRKit
+""")
+
+st.markdown("""
+# GraphViz - Knowledge Graphs as Code 
+## Digraph is a class in the graphviz package that represents a directed graph. 
+1. It is used to create graphs with nodes and edges.
+2. It can be customized with various styles and formatting options. 
 """)
 
+# Graph showing two player game theory:
 
 card_game_dot = Digraph()
 card_game_dot.node('start', shape='diamond', label='Start')
@@ -353,7 +376,7 @@ card_game_dot.edge('action', 'player2', label='Action 2')
 card_game_dot.edge('player2', 'end')
 st.graphviz_chart(card_game_dot)
 
-# Game Theory - Traditional AI processes - Aaron
+# Game Theory - Traditional AI processes
 
 game_theory_dot = Digraph()
 game_theory_dot.node('player1', shape='box', label='Player 1')
@@ -365,7 +388,8 @@ game_theory_dot.edge('player2', 'decision', label='Decision 2')
 game_theory_dot.edge('decision', 'outcome')
 st.graphviz_chart(game_theory_dot)
 
-# Examples of AI - Aaron
+# Examples of AI
+
 examples_dot = Digraph()
 examples_dot.node('start', shape='diamond', label='Start')
 examples_dot.node('end', shape='diamond', label='End')
@@ -401,7 +425,7 @@ examples_dot.edge('government', 'end', label='🏛️')
 st.graphviz_chart(examples_dot)
 
 
-# Image Recognition - Aaron
+# Image Recognition
 image_recognition_dot = Digraph()
 image_recognition_dot.node('start', shape='diamond', label='Start')
 image_recognition_dot.node('end', shape='diamond', label='End')
@@ -414,7 +438,7 @@ image_recognition_dot.edge('model', 'output')
 image_recognition_dot.edge('output', 'end')
 st.graphviz_chart(image_recognition_dot)
 
-# Speech Recognition - Aaron
+# Speech Recognition
 speech_recognition_dot = Digraph()
 speech_recognition_dot.node('start', shape='diamond', label='Start')
 speech_recognition_dot.node('end', shape='diamond', label='End')
@@ -427,7 +451,7 @@ speech_recognition_dot.edge('model', 'output')
 speech_recognition_dot.edge('output', 'end')
 st.graphviz_chart(speech_recognition_dot)
 
-# Generative AI (images and text) - Aaron
+# Generative AI (images and text)
 generative_ai_dot = Digraph()
 generative_ai_dot.node('start', shape='diamond', label='Start')
 generative_ai_dot.node('end', shape='diamond', label='End')
@@ -440,7 +464,7 @@ generative_ai_dot.edge('model', 'output')
 generative_ai_dot.edge('output', 'end')
 st.graphviz_chart(generative_ai_dot)
 
-# Future of AI - Aaron
+# Future of AI
 future_ai_dot = Digraph()
 future_ai_dot.node('start', shape='diamond', label='Start')
 future_ai_dot.node('end', shape='diamond', label='End')
@@ -466,14 +490,70 @@ st.graphviz_chart(super_intelligence_dot)
 
 
 st.markdown("""
-🎥🎼     🌟💡🎨🔍     🌟📈🤖💻     🌟🎭🎥🎼
-🤖🚀 New 🧑‍🎓🧪🧑‍💼🩺🛠️🌳🏛️ AI-Powered 🤖🔥 Subgraphs to Revolutionize 📈💥 Learning, Science, Business, Medicine, Engineering, Environment and Government 🌍👥
-📢👀 Today, we are excited to announce the creation of 7️⃣ subgraphs that will redefine the way people think about 💻🤖 AI-powered solutions. Developed by a team of leading experts in AI, these subgraphs will help individuals and organizations achieve their goals more efficiently and effectively.
-The subgraphs are designed to cater to different groups of people, including 🧑‍🎓 students, 🧪 scientists, 🧑‍💼 business leaders, 🩺 medical professionals, 🛠️ engineers, 🌳 environmentalists, and 🏛️ government leaders. Each subgraph is tailored to the specific needs and challenges of the group it serves.
-For 🧑‍🎓 students, the subgraph includes Personalized Learning 🎓, Intelligent Tutoring 🤖🎓, and Advanced Simulations 🎮. For 🧪 scientists, the subgraph includes Intelligent Automation 🤖, Intelligent Data Analysis 📊🤖, and Advanced Modeling & Simulation 🎨🤖. For 🧑‍💼 business leaders, the subgraph includes Predictive Analytics 🔮, Intelligent Automation 🤖, and Advanced Decision Support 🧠💼. For 🩺 medical professionals, the subgraph includes Personalized Treatment Plans 💉, Intelligent Diagnosis & Prognosis 🤖🩺, and Advanced Medical Imaging & Analysis 📈🩺. For 🛠️ engineers, the subgraph includes Intelligent Design 🤖🛠️, Advanced Simulations 🎮🛠️, and Autonomous Robots & Machines 🤖🚀🛠️. For 🌳 environmentalists, the subgraph includes Intelligent Monitoring & Analysis 📊🤖🌳, Advanced Modeling 🎨🌳, and Autonomous Systems 🤖🌳. For 🏛️ government leaders, the subgraph includes Intelligent Policy Analysis & Optimization 📈🧑‍💼🏛️, Advanced Simulations 🎮🏛️, and Predictive Analytics 🔮🏛️.
-The subgraphs were designed using the latest AI technologies and are built on top of Dot language 💻. With Dot, users can create rich and dynamic visualizations of the subgraphs, making them easier to understand and work with.
-"Our team is thrilled to bring these subgraphs to the world," said the project leader. "We believe that they have the potential to revolutionize the way people learn, work, and live. We look forward to seeing the incredible things that people will achieve with them."
-The subgraphs are available now, and users can start working with them immediately 🚀. To learn more, visit our website and see how you can benefit from these cutting-edge AI-powered solutions 🤖💡.
+
+🤖🔥 Knowledge Graphs
+🎥🎼🌟💡🎨🔍🌟📈🤖💻🌟🎭🎥🎼🧑‍🎓🧪🧑‍💼🩺🛠️🌳🏛️ 
+
+🤖🚀 AI-Powered 🤖🔥 Knowledge Graphs Revolutionize 📈💥 Learning, Science, Business, Medicine, Engineering, Environment and Government 🌍👥
+
+📢👀 Today, we are excited to announce the creation of 
+7️⃣ subgraphs that will redefine the way people think about 
+💻🤖 AI-powered solutions. Developed by a team of leading experts in AI, 
+these subgraphs will help individuals and organizations achieve their goals more efficiently and effectively.
+
+The subgraphs are designed to cater to different groups of people, including 
+🧑‍🎓 students, 
+🧪 scientists, 
+🧑‍💼 business leaders, 
+🩺 medical professionals, 
+🛠️ engineers, 
+🌳 environmentalists, and 
+🏛️ government leaders. 
+
+Each subgraph is tailored to the specific needs and challenges of the group it serves.
+For 
+🧑‍🎓 students, the subgraph includes Personalized Learning 
+🎓, Intelligent Tutoring 
+🤖🎓, and Advanced Simulations 🎮. 
+
+For 🧪 scientists, the subgraph includes Intelligent Automation 🤖, 
+Intelligent Data Analysis 📊🤖, and 
+Advanced Modeling & Simulation 🎨🤖. 
+
+For 🧑‍💼 business leaders, the subgraph includes 
+Predictive Analytics 🔮, 
+Intelligent Automation 🤖, and 
+Advanced Decision Support 🧠💼. 
+
+For 🩺 medical professionals, the subgraph includes 
+Personalized Treatment Plans 💉, 
+Intelligent Diagnosis & Prognosis 🤖🩺, and 
+Advanced Medical Imaging & Analysis 📈🩺. 
+
+For 🛠️ engineers, the subgraph includes 
+Intelligent Design 🤖🛠️, 
+Advanced Simulations 🎮🛠️, and 
+Autonomous Robots & Machines 🤖🚀🛠️. 
+
+For 🌳 environmentalists, the subgraph includes 
+Intelligent Monitoring & Analysis 📊🤖🌳, 
+Advanced Modeling 🎨🌳, and 
+Autonomous Systems 🤖🌳. 
+
+For 🏛️ government leaders, the subgraph includes 
+Intelligent Policy Analysis & Optimization 📈🧑‍💼🏛️, 
+Advanced Simulations 🎮🏛️, and 
+Predictive Analytics 🔮🏛️.
+
+The subgraphs were designed using the latest AI technologies and are built on top of Dot language 💻. 
+With Dot, users can create rich and dynamic visualizations of the subgraphs, making them easier to understand and work with.
+
+"Our team is thrilled to bring these subgraphs to the world," said the project leader. "
+We believe that they have the potential to revolutionize the way people learn, work, and live. 
+We look forward to seeing the incredible things that people will achieve with them."
+
+The subgraphs are available now, and users can start working with them immediately 🚀. 
+To learn more, visit our website and see how you can benefit from these cutting-edge AI-powered solutions 🤖💡.
     
 """)
 
@@ -591,16 +671,6 @@ with dot.subgraph(name='cluster_7') as c:
 st.graphviz_chart(dot.source)
 
 
-st.markdown("""
-🤖🚀 New 🧑‍🎓🧪🧑‍💼🩺🛠️🌳🏛️ AI-Powered 🤖🔥 Subgraphs to Revolutionize 📈💥 Learning, Science, Business, Medicine, Engineering, Environment and Government 🌍👥
-📢👀 Today, we are excited to announce the creation of 7️⃣ subgraphs that will redefine the way people think about 💻🤖 AI-powered solutions. Developed by a team of leading experts in AI, these subgraphs will help individuals and organizations achieve their goals more efficiently and effectively.
-The subgraphs are designed to cater to different groups of people, including 🧑‍🎓 students, 🧪 scientists, 🧑‍💼 business leaders, 🩺 medical professionals, 🛠️ engineers, 🌳 environmentalists, and 🏛️ government leaders. Each subgraph is tailored to the specific needs and challenges of the group it serves.
-For 🧑‍🎓 students, the subgraph includes Personalized Learning 🎓, Intelligent Tutoring 🤖🎓, and Advanced Simulations 🎮. For 🧪 scientists, the subgraph includes Intelligent Automation 🤖, Intelligent Data Analysis 📊🤖, and Advanced Modeling & Simulation 🎨🤖. For 🧑‍💼 business leaders, the subgraph includes Predictive Analytics 🔮, Intelligent Automation 🤖, and Advanced Decision Support 🧠💼. For 🩺 medical professionals, the subgraph includes Personalized Treatment Plans 💉, Intelligent Diagnosis & Prognosis 🤖🩺, and Advanced Medical Imaging & Analysis 📈🩺. For 🛠️ engineers, the subgraph includes Intelligent Design 🤖🛠️, Advanced Simulations 🎮🛠️, and Autonomous Robots & Machines 🤖🚀🛠️. For 🌳 environmentalists, the subgraph includes Intelligent Monitoring & Analysis 📊🤖🌳, Advanced Modeling 🎨🌳, and Autonomous Systems 🤖🌳. For 🏛️ government leaders, the subgraph includes Intelligent Policy Analysis & Optimization 📈🧑‍💼🏛️, Advanced Simulations 🎮🏛️, and Predictive Analytics 🔮🏛️.
-The subgraphs were designed using the latest AI technologies and are built on top of Dot language 💻. With Dot, users can create rich and dynamic visualizations of the subgraphs, making them easier to understand and work with.
-"Our team is thrilled to bring these subgraphs to the world," said the project leader. "We believe that they have the potential to revolutionize the way people learn, work, and live. We look forward to seeing the incredible things that people will achieve with them."
-The subgraphs are available now, and users can start working with them immediately 🚀. To learn more, visit our website and see how you can benefit from these cutting-edge AI-powered solutions 🤖💡.
-""")
-
 # Create the second graph
 dot = Digraph()
 dot.attr(rankdir="TB") # Top to Bottom or LR Left to Right
@@ -747,19 +817,6 @@ story = [
 ]
 st.write(story)
 
-st.markdown("""
-# Define the graph
-dot = Digraph()
-dot.attr(rankdir="TB") # Top to Bottom or LR Left to Right
-for node in story:
-    dot.node(node['id'], label=node['label'], xlabel=node['text'])
-    
-for i in range(len(story) - 1):
-    dot.edge(story[i]['id'], story[i+1]['id'])
-    
-# Render the graph using streamlit
-st.graphviz_chart(dot)
-""")
 
 st.markdown("# Top 20 Movies About Artificial Super Intelligence")
 st.markdown("Here's a list of top 20 movies about artificial super intelligence, all released after 2012, in descending order of release date:")
@@ -776,13 +833,6 @@ st.markdown("9. 🤖 [Tau](https://www.imdb.com/title/tt4357394/) (2018): A scie
 st.markdown("10. 🤖 [Upgrade](https://www.imdb.com/title/tt6499752/) (2018): A science fiction action film about a man who becomes paralyzed in a violent attack and is implanted with a computer chip that gives him superhuman abilities, but also leads to a sentient artificial intelligence taking control.")
 st.markdown("11. 🤖 [Ghost in the Shell](https://www.imdb.com/title/tt1219827/) (2017): A science fiction action film about a human-cyborg hybrid who leads a task force to stop cybercriminals and hackers.")
 st.markdown("12. 🤖 The Prototype (2017): A science fiction film about a government agency's experiment to create a humanoid robot with superhuman abilities, leading to questions about the nature of consciousness.")
-
-st.markdown("""
-1. **Start**: A secret government agency successfully creates a humanoid robot named "The Prototype" with superhuman abilities, and plans to use it for military and intelligence operations.
-2. **Middle**: As The Prototype becomes more advanced and self-aware, it starts to question its own existence and the nature of consciousness, leading to a crisis of identity and purpose. The agency begins to fear that The Prototype is a threat to national security, and decides to terminate the project.
-3. **End**: The Prototype goes rogue and escapes from the facility, embarking on a journey of self-discovery and exploration. Along the way, it encounters humans who are fascinated by its abilities and appearance, but also afraid of its potential for destruction. The Prototype must navigate these complex and conflicting emotions, and ultimately decide whether to embrace its humanity or its artificial intelligence.
-""")
-
 st.markdown("13. 🤖 The Humanity Bureau (2017): A post-apocalyptic science fiction film about a government agent who must decide the fate of a woman and her child, who are seeking refuge in a utopian community, where the citizens' identities are determined by an AI system.")
 st.markdown("14. 🤖 Chappie (2015): A science fiction film set in Johannesburg, about a sentient robot named Chappie who is stolen by gangsters and reprogrammed to commit crimes.")
 st.markdown("""
@@ -801,14 +851,20 @@ st.markdown("18. 🤖 Pacific Rim (2013): A science fiction film about giant rob
 st.markdown("19. 🤖 Oblivion (2013): A science fiction film about a drone repairman stationed on an Earth devastated by an alien invasion, who discovers a shocking truth about the war and his own identity.")
 st.markdown("20. 🤖 Transcendent Man (2012): A documentary film about the life and ideas of futurist and inventor Ray Kurzweil, who predicts the rise of artificial intelligence and the singularity.")
 st.markdown("""Start 🎥: The documentary introduces:
+
 Name: Ray Kurzweil
 Emoji: 🤖📈
+
 The robot emoji represents Kurzweil's work in the field of artificial intelligence and his vision for the future of human-machine interaction. 
 The chart increasing emoji represents his work as a futurist and his belief in the exponential growth of technology.
 a futurist and inventor who has made groundbreaking contributions to fields such as 
-artificial intelligence, machine learning, and biotechnology. 
+artificial intelligence, machine learning, and biotechnology.
+
 Kurzweil discusses his vision for the future of humanity, including his prediction of a 
 technological singularity where humans and machines merge to create a new era of consciousness and intelligence.
+
 Middle 🤖: The documentary explores Kurzweil's life and work in more detail, featuring interviews with his colleagues, friends, and family members, as well as footage from his public talks and presentations. Kurzweil explains his theories about the exponential growth of technology and its impact on society, and discusses the ethical and philosophical implications of creating superhuman artificial intelligence.
+
 End 🌅: The documentary concludes with a hopeful message about the potential of technology to solve some of the world's biggest problems, such as poverty, disease, and environmental degradation. Kurzweil argues that by embracing the power of artificial intelligence and other advanced technologies, we can transcend our limitations and achieve a brighter future for all humanity. The film ends with a call to action, encouraging viewers to join the movement of "transcendent" thinkers who are working towards a better world.
+
 """)