Update app.py

app.py

@@ -14,8 +14,6 @@ import textract
 import time
 import zipfile
 import dotenv
-import DeitiesAndDemigods as DnD
-
 from gradio_client import Client
 from audio_recorder_streamlit import audio_recorder
 from bs4 import BeautifulSoup
@@ -31,7 +29,7 @@ from xml.etree import ElementTree as ET
 from PIL import Image
 from urllib.parse import quote  # Ensure this import is included
 
-# 1. 🔧📝 - Configuration
+# 1. Configuration
 Site_Name = 'Scholarly-Article-Document-Search-With-Memory'
 title="🔬🧠ScienceBrain.AI"
 helpURL='https://huggingface.co/awacke1'
@@ -50,11 +48,6 @@ st.set_page_config(
 )
 
 
-def load_file(file_name):
-    with open(file_name, "r", encoding='utf-8') as file:
-        content = file.read()
-    return content
-
 
 # HTML5 based Speech Synthesis (Text to Speech in Browser)
 @st.cache_resource
@@ -89,46 +82,41 @@ def SpeechSynthesis(result):
 def parse_to_markdown(text):
     return text
         
-        
-
-
-import re
+def load_file(file_name):
+    with open(file_name, "r", encoding='utf-8') as file:
+    #with open(file_name, "r") as file:
+        content = file.read()
+    return content
 
 def extract_urls(text):
     try:
-        # Regular expression patterns to find the required fields
         date_pattern = re.compile(r'### (\d{2} \w{3} \d{4})')
         abs_link_pattern = re.compile(r'\[(.*?)\]\((https://arxiv\.org/abs/\d+\.\d+)\)')
         pdf_link_pattern = re.compile(r'\[⬇️\]\((https://arxiv\.org/pdf/\d+\.\d+)\)')
         title_pattern = re.compile(r'### \d{2} \w{3} \d{4} \| \[(.*?)\]')
-
-        # Find all occurrences of the required fields using the regular expression patterns
         date_matches = date_pattern.findall(text)
         abs_link_matches = abs_link_pattern.findall(text)
         pdf_link_matches = pdf_link_pattern.findall(text)
         title_matches = title_pattern.findall(text)
 
-        # Generate markdown string with the extracted fields
+        # markdown with the extracted fields
         markdown_text = ""
         for i in range(len(date_matches)):
             date = date_matches[i]
             title = title_matches[i]
             abs_link = abs_link_matches[i][1]
             pdf_link = pdf_link_matches[i]
-
             markdown_text += f"**Date:** {date}\n\n"
             markdown_text += f"**Title:** {title}\n\n"
             markdown_text += f"**Abstract Link:** [{abs_link}]({abs_link})\n\n"
             markdown_text += f"**PDF Link:** [{pdf_link}]({pdf_link})\n\n"
             markdown_text += "---\n\n"
-
         return markdown_text
+    
     except:
         st.write('.')
         return ''
 
-
-
 def download_pdfs(urls):
     local_files = []
     for url in urls:
@@ -152,9 +140,6 @@ def generate_html(local_files):
 def search_arxiv(query):
     start_time = time.strftime("%Y-%m-%d %H:%M:%S")
     client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-
-    # Search 1 - Retrieve the Papers
-    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
     response1 = client.predict(
         query,
         20,
@@ -164,8 +149,6 @@ def search_arxiv(query):
     )
     Question = '### 🔎 ' + query + '\r\n'  # Format for markdown display with links
     References =  response1[0]  
-
-    # URLs from the response
     ReferenceLinks = extract_urls(References)
 
     RunSecondQuery = True
@@ -186,8 +169,6 @@ def search_arxiv(query):
 
     st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete')
     end_time = time.strftime("%Y-%m-%d %H:%M:%S")
-
-    # Output
     start_timestamp = time.mktime(time.strptime(start_time, "%Y-%m-%d %H:%M:%S"))
     end_timestamp = time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S"))
     elapsed_seconds = end_timestamp - start_timestamp
@@ -221,62 +202,59 @@ PromptPrefix = 'Create a specification with streamlit functions creating markdow
 PromptPrefix2 = 'Create a streamlit python user app with full code listing to create a UI implementing the using streamlit, gradio, huggingface to create user interface elements like emoji buttons, sliders, drop downs, and data interfaces like dataframes to show tables, session_statematching this ruleset and thematic story plot line: '
 PromptPrefix3 = 'Create a HTML5 aframe and javascript app using appropriate libraries to create a word game simulation with advanced libraries like aframe to render 3d scenes creating moving entities that stay within a bounding box but show text and animation in 3d for inventory, components and story entities.  Show full code listing.  Add a list of new random entities say 3 of a few different types to any list appropriately and use emojis to make things easier and fun to read.  Use appropriate emojis in labels.  Create the UI to implement storytelling in the style of a dungeon master, with features using three emoji appropriate text plot twists and recurring interesting funny fascinating and complex almost poetic named characters with genius traits and file IO, randomness, ten point choice lists, math distribution tradeoffs, witty humorous dilemnas with emoji , rewards, variables, reusable functions with parameters, and data driven app with python libraries and streamlit components for Javascript and HTML5. Use appropriate emojis for labels to summarize and list parts, function, conditions for topic:'
 
-if st.checkbox(label='🎁Show Deities and Demigods Dataset', key='Deities'):
-    Deities = DnD.DeitiesAndDemigods
-    st.markdown(Deities)
-    
+# MoE Roleplaying Technique for Context Experts
 roleplaying_glossary = {
-   "🤖 AI Concepts": {
-       "MoE (Mixture of Experts) 🧠": [
-           "What are Multi Agent Systems, Mixture of Experts, Semantic and Episodic Memory, and Mirroring in the context of AI and Health?",
-           "How can AGI and AMI systems be created using MAS and MoE for Health?",
-           "What are Self Rewarding AI Systems and their potential for Health?"
-       ],
-       "Multi Agent Systems (MAS) 🤝": [
-           "What are some key characteristics and behaviors of Multi Agent Systems?",
-           "What are distributed, autonomous, cooperative and competitive Multi Agent Systems?",
-           "How are MAS applied in robotics, simulations, and decentralized problem-solving?"
-       ],
-       "Self Rewarding AI 🎁": [
-           "What are some research areas and approaches in Self Rewarding AI systems?",
-           "What is intrinsic motivation, autonomous goal setting, and curiosity-driven learning in Self Rewarding AI?",
-           "How can Self Rewarding AI enable open-ended development?"
-       ]
-   },
-   "🛠️ AI Tools & Platforms": {
-       "AutoGen 🔧": [
-           "What is AutoGen and how does it simplify the AI/ML development process?",
-           "How accessible and automated is the AutoGen platform for AI creation?",
-           "What data sources and model requirements does AutoGen support?"
-       ],
-       "ChatDev 💬": [
-           "What is ChatDev and what features does it offer for building chatbots and conversational AI?",
-           "What pre-built assets and integrations are available in ChatDev?",
-           "How does ChatDev facilitate multi-platform chat development and analytics?"
-       ],
-       "Omniverse 🌐": [
-           "What is Nvidia's Omniverse simulation platform and what industries and use cases does it support?",
-           "How does Omniverse enable physically accurate virtual worlds and seamless collaboration?",
-           "What AI training, testing and data exchange capabilities does Omniverse provide?"
-       ]
-   },
-   "🔬 Science Topics": {
-       "Physics 🔭": [
-           "What are the main branches and research areas in Physics?",
-           "What are key research areas in Astrophysics, Condensed Matter, and High Energy Physics?",
-           "How do General Relativity, Quantum Cosmology, and Mathematical Physics interrelate?"
-       ],
-       "Mathematics ➗": [
-           "What are the main branches of Mathematics?",
-           "What are main branches of Algebra, Analysis, and Geometry in Mathematics?",
-           "How do Probability, Statistics, and Applied Math relate to other Math fields?"
-       ],
-       "Computer Science 💻": [
-           "What are the main research areas in Computer Science?",
-           "What are major research topics in AI, Machine Learning, NLP, Vision, Graphics and Robotics?",
-           "How do Algorithms, Data Structures, Databases, Distributed Systems and Programming Languages intersect?"
-       ]
-   }
+    "🤖 AI Concepts": {
+        "MoE (Mixture of Experts) 🧠": [
+            "As a leading researcher in AI and Health, provide an overview of Multi Agent Systems, Mixture of Experts, Semantic and Episodic Memory, and Mirroring, and their potential applications in the healthcare domain.",
+            "As an AI architect specializing in AGI and AMI systems, explain how MAS and MoE can be leveraged to create intelligent systems for healthcare applications.",
+            "As an expert in Self Rewarding AI, discuss the key concepts, potential benefits, and challenges of developing self-rewarding AI systems for healthcare."
+        ],
+        "Multi Agent Systems (MAS) 🤝": [
+            "As a renowned researcher in Multi Agent Systems, describe the key characteristics and behaviors of distributed, autonomous, cooperative, and competitive Multi Agent Systems.",
+            "As an AI engineer with experience in MAS, discuss how Multi Agent Systems are applied in robotics, simulations, and decentralized problem-solving.",
+            "As a thought leader in Multi Agent Systems, provide insights into the future trends and potential breakthroughs in MAS research and applications."
+        ],
+        "Self Rewarding AI 🎁": [
+            "As a leading expert in Self Rewarding AI, discuss the main research areas and approaches in developing AI systems with intrinsic motivation and autonomous goal-setting capabilities.",
+            "As an AI researcher specializing in curiosity-driven learning, explain how Self Rewarding AI can enable open-ended development and adaptability in AI systems.",
+            "As a pioneer in Self Rewarding AI, share your vision for the future of AI systems that can autonomously set goals, learn, and adapt to new challenges."
+        ]
+    },
+    "🛠️ AI Tools & Platforms": {
+        "AutoGen 🔧": [
+            "As the lead developer of AutoGen, provide an overview of how this platform simplifies the AI/ML development process and makes it more accessible and automated.",
+            "As an AI enthusiast curious about AutoGen, inquire about the data sources and model requirements supported by the platform.",
+            "As a potential user of AutoGen, ask about the key features and benefits of using this platform for AI development compared to traditional methods."
+        ],
+        "ChatDev 💬": [
+            "As a chatbot developer interested in ChatDev, ask about the features and capabilities offered by this platform for building conversational AI applications.",
+            "As a product manager evaluating ChatDev, inquire about the pre-built assets, integrations, and multi-platform support available in the platform.",
+            "As a business owner considering ChatDev, ask how this platform can facilitate the development, deployment, and analytics of chatbots across various channels and use cases."
+        ],
+        "Omniverse 🌐": [
+            "As a researcher in simulation technology, inquire about the industries and use cases supported by Nvidia's Omniverse platform.",
+            "As an engineer interested in Omniverse, ask how this platform enables the creation of physically accurate virtual worlds and facilitates seamless collaboration.",
+            "As an AI practitioner curious about Omniverse, inquire about the AI training, testing, and data exchange capabilities provided by the platform."
+        ]
+    },
+    "🔬 Science Topics": {
+        "Physics 🔭": [
+            "As a Physics student eager to explore the field, ask about the main branches and research areas in Physics and their interconnections.",
+            "As a researcher in Astrophysics, discuss the current state of research and future directions in this exciting subfield of Physics.",
+            "As a theorist working on the foundations of Physics, explain how General Relativity, Quantum Cosmology, and Mathematical Physics interrelate and shape our understanding of the universe."
+        ],
+        "Mathematics ➗": [
+            "As a Mathematics enthusiast, inquire about the main branches of Mathematics and their key research areas.",
+            "As a student interested in pure Mathematics, ask about the main branches of Algebra, Analysis, and Geometry, and their fundamental concepts and theories.",
+            "As an applied mathematician, discuss how Probability, Statistics, and Applied Math relate to other fields of Mathematics and their real-world applications."
+        ],
+        "Computer Science 💻": [
+            "As a Computer Science student exploring the field, ask about the main research areas and their significance in shaping the future of computing.",
+            "As an AI researcher, discuss the major research topics in AI, Machine Learning, NLP, Vision, Graphics, and Robotics, and their potential impact on various industries.",
+            "As a software engineer interested in the foundations of Computer Science, inquire about the interconnections between Algorithms, Data Structures, Databases, Distributed Systems, and Programming Languages."
+        ]
+    }
 }
 
 
@@ -288,11 +266,11 @@ def display_glossary_entity(k):
         "🃏Analyst": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix)}",  # this url plus query!
         "📚PyCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix2)}",  # this url plus query!
         "🔬JSCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix3)}",  # this url plus query!
-        "📖Wiki": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
-        "🔍Google": lambda k: f"https://www.google.com/search?q={quote(k)}",
-        "🔎Bing": lambda k: f"https://www.bing.com/search?q={quote(k)}",
-        "🎥YouTube": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
-        "🐦Twitter": lambda k: f"https://twitter.com/search?q={quote(k)}",
+        "📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
+        "🔍": lambda k: f"https://www.google.com/search?q={quote(k)}",
+        "🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}",
+        "🎥": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
+        "🐦": lambda k: f"https://twitter.com/search?q={quote(k)}",
     }
     links_md = ' '.join([f"[{emoji}]({url(k)})" for emoji, url in search_urls.items()])
     #st.markdown(f"{k} {links_md}", unsafe_allow_html=True)
@@ -306,12 +284,12 @@ def display_glossary_grid(roleplaying_glossary):
         "🃏Analyst": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix)}",  # this url plus query!
         "📚PyCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix2)}",  # this url plus query!
         "🔬JSCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix3)}",  # this url plus query!
-        "📖Wiki": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
-        "🔍Google": lambda k: f"https://www.google.com/search?q={quote(k)}",
-        "▶️YouTube": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
-        "🔎Bing": lambda k: f"https://www.bing.com/search?q={quote(k)}",
-        "🎥YouTube": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
-        "🐦Twitter": lambda k: f"https://twitter.com/search?q={quote(k)}",
+        "📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
+        "🔍": lambda k: f"https://www.google.com/search?q={quote(k)}",
+        "▶️": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
+        "🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}",
+        "🎥": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
+        "🐦": lambda k: f"https://twitter.com/search?q={quote(k)}",
     }
 
     for category, details in roleplaying_glossary.items():
@@ -627,6 +605,10 @@ ShowSideImages=False
 if ShowSideImages:
     SideBarImageShuffle()
 
+
+
+# Scoring for feedback: ----------------------------------------------------- emoji
+
 # Ensure the directory for storing scores exists
 score_dir = "scores"
 os.makedirs(score_dir, exist_ok=True)
@@ -643,7 +625,7 @@ def update_score(key, increment=1):
             score_data = json.load(file)
     else:
         score_data = {"clicks": 0, "score": 0}
-    score_data["clicks"] += 1
+    score_data["clicks"] += increment
     score_data["score"] += increment
     with open(score_file, "w") as file:
         json.dump(score_data, file)
@@ -836,8 +818,6 @@ topic_emojis = {
 
 # Adjusted display_buttons_with_scores function
 def display_buttons_with_scores(num_columns_text):
-    
-    
     for category, games in roleplaying_glossary.items():
         category_emoji = topic_emojis.get(category, "🔍")  # Default to search icon if no match
         st.markdown(f"## {category_emoji} {category}")
@@ -847,14 +827,9 @@ def display_buttons_with_scores(num_columns_text):
                 key = f"{category}_{game}_{term}".replace(' ', '_').lower()
                 score = load_score(key)
                 if st.button(f"{game_emoji} {category}  {game} {term} {score}", key=key):
-                    update_score(key)
-                    # Create a dynamic query incorporating emojis and formatting for clarity
+                    newscore = update_score(key.replace('?',''))
                     query_prefix = f"{category_emoji} {game_emoji} ** {category} - {game} - {term} - **"
-                    # ----------------------------------------------------------------------------------------------
-                    #query_body = f"Create a detailed outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements."
-                    query_body = f"Create a streamlit python app.py that produces a detailed markdown outline and emoji laden user interface with labels with the entity name and emojis in all labels with a set of streamlit UI components with drop down lists and dataframes and buttons with expander and sidebar for the app to run the data as default values mostly in text boxes. Feature a 3 point outline sith 3 subpoints each where each line has about six words describing this and also contain appropriate emoji for creating sumamry of all aspeccts of this topic. an outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements."
-                    response = search_glossary(query_prefix + query_body)
-
+                    st.markdown("Scored " + query_prefix + ' with score ' + str(newscore) + '.')
 
 
 def get_all_query_params(key):
@@ -1536,16 +1511,16 @@ if AddAFileForContext:
                     filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
                     create_file(filename, user_prompt, response, should_save)
                     st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-                    
+        
+display_glossary_grid(roleplaying_glossary)  # Word Glossary Jump Grid - Dynamically calculates columns based on details length to keep topic together
+            
 
-num_columns_video=st.slider(key="num_columns_video", label="Choose Number of Video Columns", min_value=1, max_value=15, value=4)
+num_columns_video=st.slider(key="num_columns_video", label="Choose Number of Video Columns", min_value=1, max_value=15, value=2)
 display_videos_and_links(num_columns_video)   # Video Jump Grid
 
-num_columns_images=st.slider(key="num_columns_images", label="Choose Number of Image Columns", min_value=1, max_value=15, value=4)
+num_columns_images=st.slider(key="num_columns_images", label="Choose Number of Image Columns", min_value=1, max_value=15, value=2)
 display_images_and_wikipedia_summaries(num_columns_images)   # Image Jump Grid
 
-display_glossary_grid(roleplaying_glossary)  # Word Glossary Jump Grid - Dynamically calculates columns based on details length to keep topic together
-
 num_columns_text=st.slider(key="num_columns_text", label="Choose Number of Text Columns", min_value=1, max_value=15, value=4)
 display_buttons_with_scores(num_columns_text)  # Feedback Jump Grid