Upload 2 files

app.py

@@ -1,19 +1,15 @@
 \
 import os
 import json
+import uuid  # <-- FIX: added
 from typing import Dict, Any, List, Tuple
 import gradio as gr
 
-# Network rendering
 from pyvis.network import Network
 
-DEFAULT_JSON = "job_skill_network.json"  # Place this file in the Space repo root
+DEFAULT_JSON = "job_skill_network.json"
 
 def _load_graph(file_obj) -> Dict[str, Any]:
-    """
-    Load JSON from uploaded file or from DEFAULT_JSON if present.
-    Expect keys: 'nodes': [{'id','label','type',...}], 'edges': [{'source','target','type','weight',...}]
-    """
     if file_obj is not None:
         with open(file_obj.name if hasattr(file_obj, "name") else file_obj, "r", encoding="utf-8") as f:
             return json.load(f)
@@ -37,12 +33,11 @@ def _filter_graph(graph: Dict[str, Any],
                   top_n_jobs: int,
                   keep_outside_similar: bool,
                   include_job_nodes: bool,
-                  include_skill_nodes: bool) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+                  include_skill_nodes: bool):
     nodes = graph.get("nodes", [])
     edges = graph.get("edges", [])
     jobs, skills = _split_nodes(nodes)
 
-    # Sort jobs by postings desc (fallback to degree if missing)
     def _postings(n): 
         try:
             return int(n.get("postings", 0))
@@ -52,29 +47,24 @@ def _filter_graph(graph: Dict[str, Any],
     jobs_sorted = sorted(jobs, key=_postings, reverse=True)
     selected_job_ids = set([n["id"] for n in jobs_sorted[:max(1, int(top_n_jobs))]]) if include_job_nodes else set()
 
-    # Start with required edge types
     selected_edges = []
     for e in edges:
         et = str(e.get("type","")).lower()
         if et == "requires" and include_requires and int(e.get("weight", 1)) >= int(min_weight):
-            # only keep if we include job nodes and skill nodes
             if include_job_nodes or include_skill_nodes:
                 selected_edges.append(e)
         elif et == "similar" and include_similar and int(e.get("weight", 1)) >= int(min_weight):
             selected_edges.append(e)
 
-    # Build node id set from edges, but restricted to selected jobs for "requires" if we have a top_n constraint
     node_ids = set()
     for e in selected_edges:
         s, t, et = e.get("source"), e.get("target"), str(e.get("type","")).lower()
         if et == "requires" and selected_job_ids:
-            # retain requires edges only if job is in top N (either as source or target, depending direction)
             if (s in selected_job_ids) or (t in selected_job_ids):
                 node_ids.update([s, t])
         else:
             node_ids.update([s, t])
 
-    # If include_similar is True and keep_outside_similar is True, add jobs similar to selected jobs even if outside top N
     if include_similar and keep_outside_similar and selected_job_ids:
         for e in selected_edges:
             if str(e.get("type","")).lower() != "similar": 
@@ -83,7 +73,6 @@ def _filter_graph(graph: Dict[str, Any],
             if (s in selected_job_ids) or (t in selected_job_ids):
                 node_ids.update([s, t])
 
-    # Build final node list according to include flags
     node_map = _index_nodes(nodes)
     final_nodes = []
     for nid in list(node_ids):
@@ -94,18 +83,15 @@ def _filter_graph(graph: Dict[str, Any],
         if (ntype == "job" and include_job_nodes) or (ntype == "skill" and include_skill_nodes):
             final_nodes.append(n)
 
-    # Filter edges to keep only those whose endpoints remain
     final_ids = set(n["id"] for n in final_nodes)
     final_edges = [e for e in selected_edges if e.get("source") in final_ids and e.get("target") in final_ids]
 
     return final_nodes, final_edges
 
-def _build_pyvis_html(nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]], physics: bool, hierarchical: bool):
-    # Create network
+def _build_pyvis_html(nodes, edges, physics: bool, hierarchical: bool):
     net = Network(height="720px", width="100%", directed=False, notebook=False)
-    net.barnes_hut()  # default physics model
+    net.barnes_hut()
 
-    # Add nodes
     for n in nodes:
         nid = n["id"]
         label = str(n.get("label", nid))
@@ -114,14 +100,12 @@ def _build_pyvis_html(nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]],
         size = 12
         shape = "dot"
         if ntype == "job":
-            size = 18 + int(n.get("postings", 0)) * 0.1  # scale by postings
+            size = 18 + int(n.get("postings", 0)) * 0.1
             shape = "ellipse"
         elif ntype == "skill":
             size = 8
-
         net.add_node(nid, label=label, title=title, group=ntype, shape=shape, value=size)
 
-    # Add edges
     for e in edges:
         s, t = e.get("source"), e.get("target")
         et = str(e.get("type",""))
@@ -129,7 +113,6 @@ def _build_pyvis_html(nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]],
         title = f"{et} (w={weight})"
         net.add_edge(s, t, title=title, value=weight)
 
-    # Options
     options = {
       "physics": {"enabled": bool(physics)},
       "interaction": {"hover": True, "multiselect": True, "dragNodes": True},
@@ -137,7 +120,6 @@ def _build_pyvis_html(nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]],
       "edges": {"smooth": {"type": "dynamic"}}
     }
     if hierarchical:
-        # Simple hierarchical layout (works better when dominated by job->skill edges)
         options["layout"] = {
             "hierarchical": {
                 "enabled": True,
@@ -148,12 +130,10 @@ def _build_pyvis_html(nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]],
                 "sortMethod": "hubsize"
             }
         }
-        # When hierarchical, physics should usually be off to avoid jitter
         options["physics"]["enabled"] = False
 
-    net.set_options(json.dumps(options))
-
-    # Return html string (include vis.js assets inline)
+    import json as _json
+    net.set_options(_json.dumps(options))
     return net.generate_html()
 
 def build_network(
@@ -177,7 +157,6 @@ def build_network(
 
     html = _build_pyvis_html(nodes, edges, physics, hierarchical)
 
-    # Save to a temp file so users can download
     out_name = f"network_{uuid.uuid4().hex[:8]}.html"
     with open(out_name, "w", encoding="utf-8") as f:
         f.write(html)
@@ -185,8 +164,7 @@ def build_network(
     return gr.update(value=html), out_name
 
 with gr.Blocks(title="Job ↔ Hard Skill Network") as demo:
-    gr.Markdown("# Job ↔ Hard Skill Network Diagram\n"
-                "Upload `job_skill_network.json` or place it at repo root.")
+    gr.Markdown("# Job ↔ Hard Skill Network Diagram\nUpload `job_skill_network.json` or place it at repo root.")
 
     with gr.Row():
         with gr.Column(scale=1):