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awacke1 commited on 4 days ago

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Create app.py

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+#!/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 📈🌿", page_icon="🌳✨", layout="wide")
+trees = {
+    "Biology": """
+0. Biology Core Rules and Future Exceptions
+1. Central Dogma DNA RNA Protein
+- Current CRISPR RNA editing 🧪
+- Research Gene therapy siRNA 🔬
+- Future Programmable genetics 🚀
+    """,
+    "AI Topics": """
+1. Major AI Industry Players 🌐
+   1. Research Leaders 🎯
+      - OpenAI: GPT-4 DALL-E Foundation Models 🔵
+    """
+}
+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"""
+    <html><head><script src="https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js"></script>
+    <style>.centered-mermaid{{display:flex;justify-content:center;margin:20px auto;}}</style></head>
+    <body><div class="mermaid centered-mermaid">{mermaid_code}</div>
+    <script>mermaid.initialize({{startOnLoad:true}});</script></body></html>
+    """
+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 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'<a href="data:{mime_type};base64,{b64}" download="{file_path}">Download {file_path}</a>'
+@st.cache_resource
+def load_generator():
+    return pipeline("text-generation", model="distilgpt2")
+# Main App
+st.title("🌳 AI Knowledge Tree Builder 🌱")
+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 🌱") 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)