Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import os
+import glob
+import re
+import pytz
+from datetime import datetime
+import base64
+
+# 🌳🤖 AI Knowledge Tree Builder - Growing smarter with every click!
+st.set_page_config(
+    page_title="AI Knowledge Tree Builder 📈🌿",
+    page_icon="🌳✨",
+    layout="wide",
+    initial_sidebar_state="auto",
+)
+
+# Predefined Knowledge Trees
+BiologyAndLevel36MagicUsers = """
+0. Biology Core Rules and Future Exceptions
+1. Central Dogma DNA RNA Protein
+- Current CRISPR RNA editing 🧪
+- Research Gene therapy siRNA 🔬
+- Future Programmable genetics 🚀
+2. Cell Origin
+- Current iPSCs organoids 🦠
+- Research Synthetic cells 🔬
+- Future De novo cell creation 🚀
+"""
+
+AITopicsToInnovate1 = """
+1. Major AI Industry Players 🌐
+   1. Research Leaders 🎯
+      - OpenAI: GPT-4 DALL-E Foundation Models 🔵
+      - Google: PaLM Gemini LLMs 🟦
+      - Anthropic: Claude Constitutional AI ⚡
+"""
+
+MultiplayerGames = """
+0. Fantasy Domain Introduction
+1. Setting the Scene
+- Current Create a high-fantasy realm 🏞️
+- Research Add domain-specific entities 🧝‍♂️
+- Future AI-generated worldbuilding 🚀
+"""
+
+# Root Node with URLs
+RootNode = """
+0. Research Hub 🌐
+1. Awacke1 Profile
+- Link: [Hugging Face Profile](https://huggingface.co/awacke1) 📚
+2. TeachingCV App
+- Link: [TeachingCV](https://huggingface.co/spaces/awacke1/TeachingCV) 🖥️
+3. DeepResearchEvaluator App
+- Link: [DeepResearchEvaluator](https://huggingface.co/spaces/awacke1/DeepResearchEvaluator) 🔍
+"""
+
+# Utility Functions
+def sanitize_filename(text):
+    safe_text = re.sub(r'[^\w\s-]', ' ', text)
+    safe_text = re.sub(r'\s+', ' ', safe_text)
+    return safe_text.strip()[:50]
+
+def generate_timestamp_filename(query):
+    central = pytz.timezone('US/Central')
+    current_time = datetime.now(central)
+    time_str = current_time.strftime("%I%M%p")
+    date_str = current_time.strftime("%m%d%Y")
+    safe_query = sanitize_filename(query)
+    return f"{time_str} {date_str} ({safe_query}).md"
+
+def parse_outline_to_mermaid(outline_text):
+    lines = outline_text.strip().split('\n')
+    nodes = []
+    edges = []
+    stack = []
+    for line in lines:
+        indent = len(line) - len(line.lstrip())
+        level = indent // 4  # 4 spaces per level
+        text = line.strip()
+        label = re.sub(r'^[#*\->\d\.\s]+', '', text).strip()
+        if label:
+            node_id = f"N{len(nodes)}"
+            nodes.append(f'{node_id}["{label}"]')
+            if stack:
+                parent_level = stack[-1][0]
+                if level > parent_level:
+                    parent_id = stack[-1][1]
+                    edges.append(f"{parent_id} --> {node_id}")
+                    stack.append((level, node_id))
+                else:
+                    while stack and stack[-1][0] >= level:
+                        stack.pop()
+                    if stack:
+                        parent_id = stack[-1][1]
+                        edges.append(f"{parent_id} --> {node_id}")
+                    stack.append((level, node_id))
+            else:
+                stack.append((level, node_id))
+    return "graph TD\n" + "\n".join(nodes + edges)
+
+def grow_tree(base_tree, new_node_name, parent_node):
+    lines = base_tree.strip().split('\n')
+    new_lines = []
+    added = False
+    for line in lines:
+        new_lines.append(line)
+        if parent_node in line and not added:
+            indent = len(line) - len(line.lstrip())
+            new_lines.append(f"{' ' * (indent + 4)}- {new_node_name} 🌱")
+            added = True
+    return "\n".join(new_lines)
+
+def breed_trees(tree1, tree2, intersect_node):
+    lines1 = tree1.strip().split('\n')
+    lines2 = tree2.strip().split('\n')
+    new_lines = lines1.copy()
+    for line in lines2:
+        if intersect_node not in line and not any(line.strip() in l for l in lines1):
+            new_lines.append(line)
+    return "\n".join(new_lines)
+
+# Model Building Process
+def generate_model_pipeline():
+    return """
+    graph TD
+    A[Load Data 📊] --> B[Preprocess Data 🛠️]
+    B --> C[Train Model 🤖]
+    C --> D[Evaluate Model 📈]
+    D --> E[Deploy Model 🚀]
+    """
+
+# AI Lookup
+@st.cache_resource
+def load_generator():
+    return pipeline("text-generation", model="distilgpt2")
+
+# Sidebar: File Management
+if 'selected_file' not in st.session_state:
+    st.session_state.selected_file = None
+
+st.sidebar.title("📁 Saved Interactions")
+md_files = glob.glob("*.md")
+for file in md_files:
+    if st.sidebar.button(file):
+        st.session_state.selected_file = file
+if st.sidebar.button("Create New Note"):
+    filename = generate_timestamp_filename("New Note")
+    with open(filename, 'w') as f:
+        f.write("# New Note\n")
+    st.sidebar.success(f"Created {filename}")
+    st.session_state.selected_file = filename
+
+# Main App
+st.title("🌳 AI Knowledge Tree Builder 🌱")
+st.markdown("Grow and visualize knowledge trees, build ML pipelines, and explore research!")
+
+if st.session_state.selected_file:
+    with open(st.session_state.selected_file, 'r') as f:
+        content = f.read()
+    st.markdown(content)
+else:
+    # Knowledge Tree Selection and Growth
+    trees = {
+        "Research Hub": RootNode,
+        "Biology": BiologyAndLevel36MagicUsers,
+        "AI Topics": AITopicsToInnovate1,
+        "Multiplayer Games": MultiplayerGames
+    }
+    selected_tree = st.selectbox("Select Knowledge Tree", list(trees.keys()))
+    current_tree = trees[selected_tree]
+
+    # Tree Growth
+    new_node = st.text_input("Add New Node (e.g., 'ML Pipeline')")
+    parent_node = st.text_input("Parent Node to Attach To (e.g., 'Research Leaders')")
+    if st.button("Grow Tree 🌱") and new_node and parent_node:
+        current_tree = grow_tree(current_tree, new_node, parent_node)
+        trees[selected_tree] = current_tree
+        st.success(f"Added '{new_node}' under '{parent_node}'!")
+
+    # Tree Breeding
+    breed_with = st.selectbox("Breed With Another Tree", [t for t in trees.keys() if t != selected_tree])
+    intersect_node = st.text_input("Common Node for Breeding (e.g., 'Research')")
+    if st.button("Breed Trees 🌳"):
+        new_tree = breed_trees(current_tree, trees[breed_with], intersect_node)
+        trees[f"{selected_tree} + {breed_with}"] = new_tree
+        st.success(f"Created new tree: {selected_tree} + {breed_with}")
+
+    # Display Tree
+    mermaid_code = parse_outline_to_mermaid(current_tree)
+    st.markdown("### Knowledge Tree Visualization")
+    st.mermaid(mermaid_code)
+
+    # Model Building Pipeline
+    st.markdown("### ML Model Building Pipeline")
+    st.mermaid(generate_model_pipeline())
+
+    # AI Lookup
+    query = st.text_input("Enter Query for AI Lookup")
+    if st.button("Perform AI Lookup 🤖") and query:
+        generator = load_generator()
+        response = generator(query, max_length=50)[0]['generated_text']
+        st.write(f"**AI Response:** {response}")
+        filename = generate_timestamp_filename(query)
+        with open(filename, 'w') as f:
+            f.write(f"# Query: {query}\n\n## AI Response\n{response}")
+        st.success(f"Saved to {filename}")
+
+if __name__ == "__main__":
+    st.sidebar.markdown("Explore, grow, and innovate!")