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awacke1 commited on 4 days ago

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ac04dd1

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1 Parent(s): 912ec24

Update app.py

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app.py +131 -73

app.py CHANGED Viewed

@@ -1,38 +1,86 @@
 #!/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:
@@ -41,8 +89,10 @@ def parse_outline_to_mermaid(outline_text):
         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:
@@ -59,6 +109,7 @@ def parse_outline_to_mermaid(outline_text):
     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>
@@ -67,6 +118,7 @@ def generate_mermaid_html(mermaid_code):
     """
 def grow_tree(base_tree, new_node_name, parent_node):
     lines = base_tree.strip().split('\n')
     new_lines = []
     added = False
@@ -79,32 +131,34 @@ def grow_tree(base_tree, new_node_name, parent_node):
     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:
@@ -113,42 +167,46 @@ if st.button("Grow Tree 🌱") and new_node and parent_node:
         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
@@ -166,12 +224,12 @@ if st.button("Predict"):
     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
@@ -179,9 +237,9 @@ if st.button("Predict"):
 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)

 #!/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
+# 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 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:
         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:
     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"""
     <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>
     """
 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
     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'<a href="data:{mime_type};base64,{b64}" download="{file_path}">Download {file_path}</a>'
+# 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 🌱")
+# Select Project Type
+project_type = st.selectbox("Select Project Type", ["Code Project", "Papers Project", "AI Project"])
 if 'current_tree' not in st.session_state:
+    st.session_state['current_tree'] = trees.get("ML Engineering", project_seeds[project_type])
+# 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:
         f.write(st.session_state['current_tree'])
     st.success(f"Added '{new_node}' under '{parent_node}'!")
+# 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
     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
 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)