Add multi claim runner. Add dbkf fetcher for automated testing. Add visualisation tool plus human score enterer.

supporting/dbkf/fetch.py

@@ -0,0 +1,61 @@
+import requests
+import json
+import random
+from urllib.parse import urlencode
+
+BASE_URL = "https://dbkf.ontotext.com/rest-api/search/documents"
+
+# search parameters 
+# Ukraine: http://weverify.eu/resource/Concept/Q212
+# COVID: http://weverify.eu/resource/Concept/Q84263196
+
+DEFAULT_PARAMS = {
+    "concept": "http://weverify.eu/resource/Concept/Q212",
+    "documentTypes": "http://schema.org/Claim",
+    "from": "2000-01-01",
+    "to": "2023-10-17",
+    "lang": "en",
+    "limit": 50,  # Max per page
+    "page": 1,
+    "orderBy": "date"
+}
+
+NUM_RANDOM_CLAIMS = 10
+
+OUTPUT_FILE = "../Wrapper/claims.json"
+
+def fetch_claims(params=None):
+    if params is None:
+        params = DEFAULT_PARAMS
+
+    query_string = urlencode(params)
+    url = f"{BASE_URL}?{query_string}"
+    print(f"Fetching data from: {url}\n")
+
+    try:
+        response = requests.get(url, timeout=15)
+        response.raise_for_status()
+        data = response.json()
+        documents = data.get("documents", [])
+        print(f"Fetched {len(documents)} documents.")
+        return documents
+    except Exception as e:
+        print(f"Error fetching data: {e}")
+        return []
+
+def save_random_claims(documents, output_file, num_claims=20):
+    if not documents:
+        print("No documents to save.")
+        return
+
+    sample_size = min(num_claims, len(documents))
+    selected = random.sample(documents, sample_size)
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        json.dump(selected, f, indent=4, ensure_ascii=False)
+
+    print(f"Saved {sample_size} random claims to {output_file}")
+
+if __name__ == "__main__":
+    docs = fetch_claims()
+    save_random_claims(docs, OUTPUT_FILE, NUM_RANDOM_CLAIMS)