Celes Renata
34 lines
1.4 KiB
Python
34 lines
1.4 KiB
Python
from minio import Minio
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import os, json
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mc = Minio(os.environ["MINIO_ENDPOINT"], access_key=os.environ["MINIO_ACCESS_KEY"], secret_key=os.environ["MINIO_SECRET_KEY"], secure=False)
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# Get the most recent LLM results
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raw_objs = sorted(mc.list_objects("stonks-llm-results", recursive=True), key=lambda o: o.last_modified, reverse=True)
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for o in raw_objs[:3]:
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data = json.loads(mc.get_object("stonks-llm-results", o.object_name).read())
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attempts = data.get("attempts", [])
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if not attempts:
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continue
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last = attempts[-1]
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raw_out = last.get("raw_output", "")
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ticker = o.object_name.split("/")[1]
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doc_id = o.object_name.split("/")[-2]
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print(f"=== {ticker} / {doc_id[:8]} ===")
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print(f" success: {data.get('success')}")
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print(f" duration: {data.get('total_duration_ms')}ms")
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try:
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parsed = json.loads(raw_out)
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print(f" summary: {parsed.get('summary', '')[:120]}")
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print(f" confidence: {parsed.get('confidence')}")
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print(f" companies: {len(parsed.get('companies', []))}")
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print(f" macro_themes: {parsed.get('macro_themes', [])}")
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for c in parsed.get("companies", []):
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print(f" -> {c.get('ticker')} sent={c.get('sentiment')} impact={c.get('impact_score')} catalyst={c.get('catalyst_type')}")
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except:
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print(f" raw output: {raw_out[:200]}")
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print()
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