#!/usr/bin/env python3 import os import re import streamlit as st import streamlit.components.v1 as components from urllib.parse import quote import pandas as pd import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset import base64 import glob import time # Page Configuration st.set_page_config( page_title="AI Knowledge Tree Builder 📈🌿", page_icon="🌳✨", layout="wide", initial_sidebar_state="auto", ) # Predefined Knowledge Trees trees = { "ML Engineering": """ 0. ML Engineering 🌐 1. Data Preparation - Load Data 📊 - Preprocess Data 🛠️ 2. Model Building - Train Model 🤖 - Evaluate Model 📈 3. Deployment - Deploy Model 🚀 """, "Health": """ 0. Health and Wellness 🌿 1. Physical Health - Exercise 🏋️ - Nutrition 🍎 2. Mental Health - Meditation 🧘 - Therapy 🛋️ """, } # Project Seeds project_seeds = { "Code Project": """ 0. Code Project 📂 1. app.py 🐍 2. requirements.txt 📦 3. README.md 📄 """, "Papers Project": """ 0. Papers Project 📚 1. markdown 📝 2. mermaid 🖼️ 3. huggingface.co 🤗 """, "AI Project": """ 0. AI Project 🤖 1. Streamlit Torch Transformers - Streamlit 🌐 - Torch 🔥 - Transformers 🤖 2. DistillKit MergeKit Spectrum - DistillKit 🧪 - MergeKit 🔄 - Spectrum 📊 3. Transformers Diffusers Datasets - Transformers 🤖 - Diffusers 🎨 - Datasets 📊 """, } # Utility Functions def sanitize_label(label): """Remove invalid characters for Mermaid labels.""" return re.sub(r'[^\w\s-]', '', label).replace(' ', '_') def sanitize_filename(label): """Make a valid filename from a label.""" return re.sub(r'[^\w\s-]', '', label).replace(' ', '_') def parse_outline_to_mermaid(outline_text, search_agent): """Convert tree outline to Mermaid syntax with clickable nodes.""" lines = outline_text.strip().split('\n') nodes, edges, clicks, stack = [], [], [], [] for line in lines: indent = len(line) - len(line.lstrip()) level = indent // 4 label = re.sub(r'^[#*\->\d\.\s]+', '', line.strip()) if label: node_id = f"N{len(nodes)}" sanitized_label = sanitize_label(label) nodes.append(f'{node_id}["{label}"]') search_url = search_urls[search_agent](label) clicks.append(f'click {node_id} "{search_url}" _blank') if stack: parent_level = stack[-1][0] if level > parent_level: edges.append(f"{stack[-1][1]} --> {node_id}") stack.append((level, node_id)) else: while stack and stack[-1][0] >= level: stack.pop() if stack: edges.append(f"{stack[-1][1]} --> {node_id}") stack.append((level, node_id)) else: stack.append((level, node_id)) return "%%{init: {'themeVariables': {'fontSize': '18px'}}}%%\nflowchart LR\n" + "\n".join(nodes + edges + clicks) def generate_mermaid_html(mermaid_code): """Generate HTML to display Mermaid diagram.""" return f"""

{mermaid_code}

""" def grow_tree(base_tree, new_node_name, parent_node): """Add a new node to the tree under a specified parent.""" 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 get_download_link(file_path, mime_type="text/plain"): """Generate a download link for a file.""" with open(file_path, 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() return f'Download {file_path}' def save_tree_to_file(tree_text, parent_node, new_node): """Save tree to a markdown file with name based on nodes.""" root_node = tree_text.strip().split('\n')[0].split('.')[1].strip() if tree_text.strip() else "Knowledge_Tree" filename = f"{sanitize_filename(root_node)}_{sanitize_filename(parent_node)}_{sanitize_filename(new_node)}_{int(time.time())}.md" mermaid_code = parse_outline_to_mermaid(tree_text, "🔮Google") # Default search engine for saved trees export_md = f"# Knowledge Tree: {root_node}\n\n## Outline\n{tree_text}\n\n## Mermaid Diagram\n```mermaid\n{mermaid_code}\n```" with open(filename, "w") as f: f.write(export_md) return filename def load_trees_from_files(): """Load all saved tree markdown files.""" tree_files = glob.glob("*.md") trees_dict = {} for file in tree_files: if file != "README.md" and file != "knowledge_tree.md": # Skip project README and temp export try: with open(file, 'r') as f: content = f.read() # Extract the tree name from the first line match = re.search(r'# Knowledge Tree: (.*)', content) if match: tree_name = match.group(1) else: tree_name = os.path.splitext(file)[0] # Extract the outline section outline_match = re.search(r'## Outline\n(.*?)(?=\n## |$)', content, re.DOTALL) if outline_match: tree_outline = outline_match.group(1).strip() trees_dict[f"{tree_name} ({file})"] = tree_outline except Exception as e: print(f"Error loading {file}: {e}") return trees_dict # Search Agents (Highest resolution social network default: X) search_urls = { "📚📖ArXiv": lambda k: f"/?q={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)}", "💡Truth": lambda k: f"https://truthsocial.com/search?q={quote(k)}", "📱X": lambda k: f"https://twitter.com/search?q={quote(k)}", } # Main App st.title("🌳 AI Knowledge Tree Builder 🌱") # Sidebar with saved trees st.sidebar.title("Saved Trees") saved_trees = load_trees_from_files() selected_saved_tree = st.sidebar.selectbox("Select a saved tree", ["None"] + list(saved_trees.keys())) # Select Project Type project_type = st.selectbox("Select Project Type", ["Code Project", "Papers Project", "AI Project"]) # Initialize or load tree if 'current_tree' not in st.session_state: if selected_saved_tree != "None" and selected_saved_tree in saved_trees: st.session_state['current_tree'] = saved_trees[selected_saved_tree] else: st.session_state['current_tree'] = trees.get("ML Engineering", project_seeds[project_type]) elif selected_saved_tree != "None" and selected_saved_tree in saved_trees: st.session_state['current_tree'] = saved_trees[selected_saved_tree] # Select Search Agent for Node Links search_agent = st.selectbox("Select Search Agent for Node Links", list(search_urls.keys()), index=5) # Default to X # Tree Growth new_node = st.text_input("Add New Node") parent_node = st.text_input("Parent Node") if st.button("Grow Tree 🌱") and new_node and parent_node: st.session_state['current_tree'] = grow_tree(st.session_state['current_tree'], new_node, parent_node) # Save to a new file with the node names saved_file = save_tree_to_file(st.session_state['current_tree'], parent_node, new_node) st.success(f"Added '{new_node}' under '{parent_node}' and saved to {saved_file}!") # Also update the temporary current_tree.md for compatibility with open("current_tree.md", "w") as f: f.write(st.session_state['current_tree']) # Display Mermaid Diagram st.markdown("### Knowledge Tree Visualization") mermaid_code = parse_outline_to_mermaid(st.session_state['current_tree'], search_agent) components.html(generate_mermaid_html(mermaid_code), height=600) # Export Tree if st.button("Export Tree as Markdown"): export_md = f"# Knowledge Tree\n\n## Outline\n{st.session_state['current_tree']}\n\n## Mermaid Diagram\n```mermaid\n{mermaid_code}\n```" with open("knowledge_tree.md", "w") as f: f.write(export_md) st.markdown(get_download_link("knowledge_tree.md", "text/markdown"), unsafe_allow_html=True) # AI Project: Minimal ML Model Building if project_type == "AI Project": st.subheader("Build Minimal ML Model from CSV") uploaded_file = st.file_uploader("Upload CSV", type="csv") if uploaded_file: df = pd.read_csv(uploaded_file) st.write("Columns:", df.columns.tolist()) feature_cols = st.multiselect("Select feature columns", df.columns) target_col = st.selectbox("Select target column", df.columns) if st.button("Train Model"): X = df[feature_cols].values y = df[target_col].values X_tensor = torch.tensor(X, dtype=torch.float32) y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1) dataset = TensorDataset(X_tensor, y_tensor) loader = DataLoader(dataset, batch_size=32, shuffle=True) model = nn.Linear(X.shape[1], 1) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.01) for epoch in range(10): for batch_X, batch_y in loader: optimizer.zero_grad() outputs = model(batch_X) loss = criterion(outputs, batch_y) loss.backward() optimizer.step() torch.save(model.state_dict(), "model.pth") app_code = f""" import streamlit as st import torch import torch.nn as nn model = nn.Linear({len(feature_cols)}, 1) model.load_state_dict(torch.load("model.pth")) model.eval() st.title("ML Model Demo") inputs = [] for col in {feature_cols}: inputs.append(st.number_input(col)) if st.button("Predict"): input_tensor = torch.tensor([inputs], dtype=torch.float32) prediction = model(input_tensor).item() st.write(f"Predicted {target_col}: {{prediction}}") """ with open("app.py", "w") as f: f.write(app_code) reqs = "streamlit\ntorch\npandas\n" with open("requirements.txt", "w") as f: f.write(reqs) readme = """ # ML Model Demo ## How to run 1. Install requirements: `pip install -r requirements.txt` 2. Run the app: `streamlit run app.py` 3. Input feature values and click "Predict". """ with open("README.md", "w") as f: f.write(readme) st.markdown(get_download_link("model.pth", "application/octet-stream"), unsafe_allow_html=True) st.markdown(get_download_link("app.py", "text/plain"), unsafe_allow_html=True) st.markdown(get_download_link("requirements.txt", "text/plain"), unsafe_allow_html=True) st.markdown(get_download_link("README.md", "text/markdown"), unsafe_allow_html=True)