added logs for transcription

app/core/orchestrator.py

@@ -16,62 +16,75 @@ os.environ.setdefault("OMP_NUM_THREADS", "1")
 
 
 def _norm_snippet(s: str) -> str:
-	return re.sub(r"\s+", " ", (s or "").strip()).lower()
+    return re.sub(r"\s+", " ", (s or "").strip()).lower()
 
 
-def process_call(audio_path: Optional[str] = None, transcript: Optional[str] = None) -> CallReport:
-	print("[orchestrator] START")
+def process_call(
+    audio_path: Optional[str] = None, transcript: Optional[str] = None
+) -> CallReport:
+    print("[orchestrator] START")
 
-	segments = transcribe(audio_path) if audio_path else [
-		{"start":0.0,"end":0.0,"speaker":"A","text": transcript or ""}]
+    segments = (
+        transcribe(audio_path)
+        if audio_path
+        else [{"start": 0.0, "end": 0.0, "speaker": "A", "text": transcript or ""}]
+    )
 
-	# 2) Claim extraction (IBM)
-	claims: List[Claim] = extract_claims(segments)
-	print(f"[orchestrator] Claims extracted: {len(claims)}")
-	if not claims:
-		print("[orchestrator] No claims found; building minimal report.")
-		return make_report(segments, [], [], [], evidence_by_claim={})
+    print(f"[orchestrator] TRANSCRIPT: {segments}")
 
-	# 3) Evidence retrieval (IBM embeddings + optional rerank)
-	claims, evmap = retrieve_evidence_for_claims(claims, k=8)
-	ev_count = sum(len(v) for v in evmap.values())
+    # 2) Claim extraction (IBM)
+    claims: List[Claim] = extract_claims(segments)
+    print(f"[orchestrator] Claims extracted: {len(claims)}")
+    if not claims:
+        print("[orchestrator] No claims found; building minimal report.")
+        return make_report(segments, [], [], [], evidence_by_claim={})
 
-	# Print evidence per claim (detailed)
-	print("[orchestrator] Evidence per claim (top k):")
-	for c in claims:
-		evs = evmap.get(c.id, [])
-		print(f"  - Claim {c.id}: {c.text}")
-		for i, e in enumerate(evs, 1):
-			snippet_preview = _norm_snippet(e.snippet)[:200]
-			meta_preview = ""
-			try:
-				meta_preview = json.dumps(e.metadata, ensure_ascii=False)[:160]
-			except Exception:
-				meta_preview = str(e.metadata)[:160]
-			print(f"      {i:02d}. {e.source or e.doc_id}  id={e.doc_id}  score={e.score:.2f}")
-			print(f"          {snippet_preview}")
-			if e.metadata:
-				print(f"          meta: {meta_preview}")
+    # 3) Evidence retrieval (IBM embeddings + optional rerank)
+    claims, evmap = retrieve_evidence_for_claims(claims, k=8)
+    ev_count = sum(len(v) for v in evmap.values())
 
-	# Flatten and deduplicate global evidence by normalized snippet, keeping highest score
-	flat: List[Evidence] = [e for lst in evmap.values() for e in lst]
-	by_snippet: Dict[str, Evidence] = {}
-	for e in flat:
-		key = _norm_snippet(e.snippet)
-		best = by_snippet.get(key)
-		if not best or (e.score or 0.0) > (best.score or 0.0):
-			by_snippet[key] = e
-	evidence_flat = list(by_snippet.values())
+    # Print evidence per claim (detailed)
+    print("[orchestrator] Evidence per claim (top k):")
+    for c in claims:
+        evs = evmap.get(c.id, [])
+        print(f"  - Claim {c.id}: {c.text}")
+        for i, e in enumerate(evs, 1):
+            snippet_preview = _norm_snippet(e.snippet)[:200]
+            meta_preview = ""
+            try:
+                meta_preview = json.dumps(e.metadata, ensure_ascii=False)[:160]
+            except Exception:
+                meta_preview = str(e.metadata)[:160]
+            print(
+                f"      {i:02d}. {e.source or e.doc_id}  id={e.doc_id}  score={e.score:.2f}"
+            )
+            print(f"          {snippet_preview}")
+            if e.metadata:
+                print(f"          meta: {meta_preview}")
 
-	verdicts: List[Verdict] = verify(claims, evmap)
-	print(f"[orchestrator] Verifier produced {len(verdicts)} verdicts")
-	print("[orchestrator] Verdicts with citations:")
-	for v in verdicts:
-		cites = getattr(v, "citation_ids", [])
-		print(f"  - {v.claim_id}: {v.label}  conf={v.confidence:.2f}  best={v.best_evidence_id}  cites={cites}")
+    # Flatten and deduplicate global evidence by normalized snippet, keeping highest score
+    flat: List[Evidence] = [e for lst in evmap.values() for e in lst]
+    by_snippet: Dict[str, Evidence] = {}
+    for e in flat:
+        key = _norm_snippet(e.snippet)
+        best = by_snippet.get(key)
+        if not best or (e.score or 0.0) > (best.score or 0.0):
+            by_snippet[key] = e
+    evidence_flat = list(by_snippet.values())
 
-	# 5) Summarize
-	report = make_report(segments, claims, evidence_flat, verdicts, evidence_by_claim=evmap)
-	print(report.call_summary)
-	print("[orchestrator] DONE")
-	return report
+    verdicts: List[Verdict] = verify(claims, evmap)
+    print(f"[orchestrator] Verifier produced {len(verdicts)} verdicts")
+    print("[orchestrator] Verdicts with citations:")
+    for v in verdicts:
+        cites = getattr(v, "citation_ids", [])
+        print(
+            f"  - {v.claim_id}: {v.label}  conf={v.confidence:.2f}  best={v.best_evidence_id}  cites={cites}"
+        )
+
+    # 5) Summarize
+    report = make_report(
+        segments, claims, evidence_flat, verdicts, evidence_by_claim=evmap
+    )
+    print(report.call_summary)
+    print("[orchestrator] DONE")
+    return report