#!/usr/bin/env python3
import os
import re
import glob
import streamlit as st
import streamlit.components.v1 as components
from transformers import pipeline
from urllib.parse import quote
from datetime import datetime
import pytz
import base64
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
st.set_page_config(page_title="AI Knowledge Tree Builder ๐๐ฟ", page_icon="๐ณโจ", layout="wide")
trees = {
"Biology": """
0. Biology Core Rules and Future Exceptions
1. Central Dogma DNA RNA Protein
- Current CRISPR RNA editing ๐งช
- Research Gene therapy siRNA ๐ฌ
- Future Programmable genetics ๐
""",
"AI Topics": """
1. Major AI Industry Players ๐
1. Research Leaders ๐ฏ
- OpenAI: GPT-4 DALL-E Foundation Models ๐ต
"""
}
def parse_outline_to_mermaid(outline_text):
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)}"
nodes.append(f'{node_id}["{label}"]')
clicks.append(f'click {node_id} "?q={quote(label)}" _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):
return f"""
{mermaid_code}
"""
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} ๐ฑ")
added = True
return "\n".join(new_lines)
def get_download_link(file_path, mime_type="text/plain"):
with open(file_path, 'rb') as f:
data = f.read()
b64 = base64.b64encode(data).decode()
return f'Download {file_path}'
@st.cache_resource
def load_generator():
return pipeline("text-generation", model="distilgpt2")
# Main App
st.title("๐ณ AI Knowledge Tree Builder ๐ฑ")
if 'current_tree' not in st.session_state:
if os.path.exists("current_tree.md"):
with open("current_tree.md", "r") as f:
st.session_state['current_tree'] = f.read()
else:
st.session_state['current_tree'] = trees["Biology"]
selected_tree = st.selectbox("Select Knowledge Tree", list(trees.keys()))
if selected_tree != st.session_state.get('selected_tree_name', 'Biology'):
st.session_state['current_tree'] = trees[selected_tree]
st.session_state['selected_tree_name'] = selected_tree
with open("current_tree.md", "w") as f:
f.write(st.session_state['current_tree'])
new_node = st.text_input("Add New Node")
parent_node = st.text_input("Parent Node")
if st.button("Grow Tree ๐ฑ") and new_node and parent_node:
st.session_state['current_tree'] = grow_tree(st.session_state['current_tree'], new_node, parent_node)
with open("current_tree.md", "w") as f:
f.write(st.session_state['current_tree'])
st.success(f"Added '{new_node}' under '{parent_node}'!")
st.markdown("### Knowledge Tree Visualization")
mermaid_code = parse_outline_to_mermaid(st.session_state['current_tree'])
components.html(generate_mermaid_html(mermaid_code), height=600)
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)
st.subheader("Build 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)