Add some preliminary analysis

agent/package.json

@@ -8,7 +8,9 @@
   "main": "agent.ts",
   "scripts": {
     "agent": "npx @langchain/langgraph-cli dev",
-    "ragas_service": "cd ../supporting/RAGAS_Service && .venv/bin/uvicorn ragas_service:app --port 8001"
+    "ragas_service": "cd ../supporting/RAGAS_Service && .venv/bin/uvicorn ragas_service:app --port 8001",
+    "frontend": "cd ../supporting/scorer && .venv/bin/streamlit run display.py",
+    "fetch": "cd ../supporting/dbkf & python fetch.py"
   },
   "dependencies": {
     "@huggingface/transformers": "^3.8.1",

supporting/scorer/analyse.py

@@ -0,0 +1,81 @@
+import json
+from statistics import mean
+
+# ------------------------------------------------------------
+# Load JSONL file
+# ------------------------------------------------------------
+DATA_FILE = "../Wrapper/results.jsonl"
+
+data = []
+with open(DATA_FILE, "r", encoding="utf-8") as f:
+    for line in f:
+        line = line.strip()
+        if line:
+            data.append(json.loads(line))
+
+# ------------------------------------------------------------
+# Extract events
+# ------------------------------------------------------------
+all_events = []
+claims = []
+
+for item in data:
+    if item.get("status") != "success":
+        continue
+
+    claim_text = item.get("text", "")
+    outputs = item.get("output", [])
+
+    for out in outputs:
+        if "content_parsed" in out:
+            events = out["content_parsed"]
+
+            claims.append({
+                "claim": claim_text,
+                "events": events
+            })
+
+            for ev in events:
+                score = ev["score"]
+                human = ev["human_score"]
+
+                all_events.append({
+                    "claim": claim_text,
+                    "event": ev["event"],
+                    "reason": ev["reasoningWhyRelevant"],
+                    "score": score,
+                    "human_score": human,
+                    "gap": abs(score - human),
+                })
+
+# ------------------------------------------------------------
+# Compute metrics
+# ------------------------------------------------------------
+if not all_events:
+    raise ValueError("No events found in file.")
+
+avg_score = mean(e["score"] for e in all_events)
+avg_diff = mean(e["gap"] for e in all_events)
+
+largest_gap_event = max(all_events, key=lambda x: x["gap"])
+worst_event = largest_gap_event
+
+worst_claim_data = next(
+    c for c in claims if c["claim"] == worst_event["claim"]
+)
+
+# ------------------------------------------------------------
+# Output results
+# ------------------------------------------------------------
+print(f"Average score: {avg_score:.4f}")
+print(f"Average |human_score - score|: {avg_diff:.4f}")
+
+print("\nLargest gap event:")
+print(f"Event: {largest_gap_event['event']}")
+print(f"Score: {largest_gap_event['score']}")
+print(f"Human score: {largest_gap_event['human_score']}")
+print(f"Gap: {largest_gap_event['gap']:.4f}")
+
+print("\nWorst performing event and its claims:")
+print(f"Claim: {worst_event['claim']}")
+print(f"Worst Event: {worst_event['event']}")