Prepare for mass data collection. Reduce concurrency as to not overwhelm scraper on long sessions. Remode duplicates from fetch script. Removing naming wierdness on scorer frontend.

supporting/Wrapper/run.ts

@@ -9,7 +9,7 @@ const INPUT_FILE = "../../data/claims.json";
 const OUTPUT_FILE = "../../data/results.jsonl";
 const API_URL = "http://localhost:2024";
 const AGENT_NAME = "agent";
-const MAX_CONCURRENCY = 50;
+const MAX_CONCURRENCY = 5;
 
 const client = new Client({ apiUrl: API_URL });
 

supporting/dbkf/fetch.py

@@ -26,8 +26,32 @@ DEFAULT_PARAMS = [
 
 NUM_RANDOM_CLAIMS = 20
 
+INPUT_FILE = "../../data/input.jsonl"
 OUTPUT_FILE = "../../data/claims.json"
 
+def load_existing_urls(input_file):
+    existing_urls = set()
+
+    try:
+        with open(input_file, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                obj = json.loads(line)
+                url = obj.get("documentUrl")
+                if url:
+                    existing_urls.add(url)
+
+        print(f"Loaded {len(existing_urls)} existing document URLs.")
+    except FileNotFoundError:
+        print(f"No existing file found at {input_file}")
+    except Exception as e:
+        print(f"Error reading JSONL file: {e}")
+
+    return existing_urls
+
+
 def fetch_claims(params=None):
     if params is None:
         params = DEFAULT_PARAMS
@@ -47,11 +71,25 @@ def fetch_claims(params=None):
         print(f"Error fetching data: {e}")
         return []
 
-def save_random_claims(documents, output_file, num_claims=20):
+def save_random_claims(documents, output_file, excluded_urls=None, num_claims=20):
     if not documents:
         print("No documents to save.")
         return
 
+    excluded_urls = excluded_urls or set()
+
+    # remove already-used documents
+    filtered_docs = [
+        d for d in documents
+        if d.get("documentUrl") not in excluded_urls
+    ]
+
+    print(f"{len(filtered_docs)} documents remain after filtering.")
+
+    if not filtered_docs:
+        print("No new documents available after filtering.")
+        return
+
     sample_size = min(num_claims, len(documents))
     selected = random.sample(documents, sample_size)
 
@@ -61,5 +99,13 @@ def save_random_claims(documents, output_file, num_claims=20):
     print(f"Saved {sample_size} random claims to {output_file}")
 
 if __name__ == "__main__":
+    existing_urls = load_existing_urls(INPUT_FILE)
+
     docs = fetch_claims()
-    save_random_claims(docs, OUTPUT_FILE, NUM_RANDOM_CLAIMS)
+
+    save_random_claims(
+        docs,
+        OUTPUT_FILE,
+        excluded_urls=existing_urls,
+        num_claims=NUM_RANDOM_CLAIMS
+    )

supporting/scorer/views/rank.py

@@ -10,7 +10,7 @@ def page_title() -> str:
     return "Rank"
 
 def render():
-    st.header("Rank PERFECT Events")
+    st.header("Rank Events")
     candidates = []
 
     for entry in st.session_state.data:
@@ -25,7 +25,7 @@ def render():
             candidates.append({"entry": entry, "claims": perfect})
 
     if not candidates:
-        st.info("No PERFECT events available.")
+        st.info("No events available.")
         st.stop()
 
     if "current_bundle" not in st.session_state: