DeepResearchEvaluator

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awacke1 commited on Dec 30, 2024

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+#!/usr/bin/env python3
+import os
+import re
+import glob
+import json
+import base64
+import zipfile
+import random
+import requests
+import openai
+from PIL import Image
+from urllib.parse import quote
+import streamlit as st
+import streamlit.components.v1 as components
+# Example base snippet: your normal Mermaid code
+DEFAULT_MERMAID = r"""
+flowchart LR
+    U((User 😎)) -- "Talk 🗣️" --> LLM[LLM Agent 🤖\nExtract Info]
+    click U "/?q=User%20😎" "Open 'User 😎'" "_blank"
+    click LLM "/?q=LLM%20Agent%20Extract%20Info" "Open LLM Agent" "_blank"
+    LLM -- "Query 🔍" --> HS[Hybrid Search 🔎\nVector+NER+Lexical]
+    click HS "/?q=Hybrid%20Search%20Vector+NER+Lexical" "Open Hybrid Search" "_blank"
+    HS -- "Reason 🤔" --> RE[Reasoning Engine 🛠️\nNeuralNetwork+Medical]
+    click RE "/?q=Reasoning%20Engine%20NeuralNetwork+Medical" "Open Reasoning" "_blank"
+    RE -- "Link 📡" --> KG((Knowledge Graph 📚\nOntology+GAR+RAG))
+    click KG "/?q=Knowledge%20Graph%20Ontology+GAR+RAG" "Open Knowledge Graph" "_blank"
+"""
+def parse_mermaid_edges(mermaid_text: str):
+    """
+    🍿 parse_mermaid_edges:
+      - Find lines like `A -- "Label" --> B`.
+      - Return adjacency dict: edges[A] = [(label, B), ...].
+    """
+    adjacency = {}
+    # Regex to match lines like:  A -- "Label" --> B
+    edge_pattern = re.compile(r'(\S+)\s*--\s*"([^"]*)"\s*-->\s*(\S+)')
+    # We split the text into lines and search for edges
+    for line in mermaid_text.split('\n'):
+        match = edge_pattern.search(line.strip())
+        if match:
+            nodeA, label, nodeB = match.groups()
+            if nodeA not in adjacency:
+                adjacency[nodeA] = []
+            adjacency[nodeA].append((label, nodeB))
+    return adjacency
+def build_subgraph(adjacency, start_node):
+    """
+    🍎 build_subgraph:
+      - BFS or DFS from start_node to gather edges.
+      - For simplicity, we only gather direct edges from this node.
+      - If you want a multi-level downstream search, do a BFS/DFS deeper.
+    """
+    sub_edges = []
+    # If start_node has no adjacency, return empty
+    if start_node not in adjacency:
+        return sub_edges
+    # For each edge out of start_node, store it in sub_edges
+    for label, child in adjacency[start_node]:
+        sub_edges.append((start_node, label, child))
+    return sub_edges
+def create_subgraph_mermaid(sub_edges, start_node):
+    """
+    🍄 create_subgraph_mermaid:
+      - Given a list of edges in form (A, label, B),
+      - Return a smaller flowchart snippet that includes them.
+    """
+    # Start with the flowchart directive
+    sub_mermaid = "flowchart LR\n"
+    sub_mermaid += f"    %% Subgraph for {start_node}\n"
+    # For each edge, build a line:   NodeA -- "Label" --> NodeB
+    for (A, label, B) in sub_edges:
+        # Potentially you can keep the original styles or shapes (like U((User 😎))).
+        # If your original code has shapes, you can store them in a dict so A-> "U((User 😎))" etc.
+        sub_mermaid += f'    {A} -- "{label}" --> {B}\n'
+    # Optionally add a comment to show the subgraph ends
+    sub_mermaid += f"    %% End of subgraph for {start_node}\n"
+    return sub_mermaid
+def generate_mermaid_html(mermaid_code: str) -> str:
+    """Tiny function to embed Mermaid code in HTML with a CDN."""
+    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;
+            }}
+            .mermaid {{
+                max-width: 800px;
+            }}
+        </style>
+    </head>
+    <body>
+        <div class="mermaid centered-mermaid">
+            {mermaid_code}
+        </div>
+        <script>
+            mermaid.initialize({{ startOnLoad: true }});
+        </script>
+    </body>
+    </html>
+    """
+def main():
+    st.set_page_config(page_title="Partial Subgraph Demo", layout="wide")
+    st.title("Partial Mermaid Subgraph from a Clicked Node")
+    # 1) Show the main diagram
+    st.subheader("Full Diagram:")
+    full_html = generate_mermaid_html(DEFAULT_MERMAID)
+    components.html(full_html, height=400, scrolling=True)
+    # 2) Build adjacency from the original code
+    adjacency = parse_mermaid_edges(DEFAULT_MERMAID)
+    # 3) See if user clicked a shape, e.g. ?q=LLM%20Agent%20Extract%20Info
+    query_params = st.query_params
+    clicked = (query_params.get('q') or [""])[0]  # If present
+    if clicked:
+        # The "clicked" node might contain spaces or special chars.
+        # We typically match the "nodeId" from the code.
+        # But your code might have the real node name "LLM" or "RE", etc.
+        # If your node is "LLM" and the label is "LLM Agent Extract Info",
+        # you might need to map them.
+        # For simplicity, let's assume your node ID is the same as the label
+        # after removing spaces.
+        # Or if your original code uses node IDs like 'LLM' or 'U'.
+        # We'll show an example:
+        # We can guess your node ID might be "LLM" if the text includes "LLM".
+        # Let's try a simpler approach: you store an internal mapping
+        # from node ID -> label. We'll do a brute force approach:
+        st.info(f"User clicked shape: {clicked}")
+        # Suppose we want to find the adjacency key that best matches the clicked string:
+        # This is a naive approach:
+        # We'll see if 'clicked' is a substring of the adjacency's node key.
+        possible_keys = []
+        for nodeKey in adjacency.keys():
+            if clicked.lower().replace("%20", " ").replace("extract info", "") in nodeKey.lower():
+                possible_keys.append(nodeKey)
+        # If we found one or more possible matches, take the first
+        if possible_keys:
+            chosen_node = possible_keys[0]
+            # Build subgraph from adjacency
+            sub_edges = build_subgraph(adjacency, chosen_node)
+            if sub_edges:
+                sub_mermaid = create_subgraph_mermaid(sub_edges, chosen_node)
+                # Display top-centered subgraph
+                st.subheader(f"SearchResult Subgraph for Node: {chosen_node}")
+                partial_html = generate_mermaid_html(sub_mermaid)
+                components.html(partial_html, height=300, scrolling=False)
+            else:
+                st.warning(f"No outgoing edges from node '{chosen_node}'.")
+        else:
+            st.warning("No matching node found in adjacency for that query param.")
+    else:
+        st.info("No shape clicked, or no ?q= in the query parameters.")
+if __name__ == "__main__":
+    main()