Update server/app.py
Browse files- server/app.py +2 -4
server/app.py
CHANGED
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@@ -34,7 +34,6 @@ def _classify_with_llm(email: dict) -> np.ndarray:
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temperature=0
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)
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res = response.choices[0].message.content.strip()
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# Extract digits only to avoid parsing errors
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nums = re.findall(r'\d', res)
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actions = [int(n) for n in nums[:3]]
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@@ -58,8 +57,8 @@ def run_task_demo(task: str) -> str:
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cumulative_norm += norm_reward
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raw = info["raw_reward"]
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verdict =
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lines.append(
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f"#{i+1:02d} [{task.upper()}] {email['description'][:35]}...\n"
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@@ -67,7 +66,6 @@ def run_task_demo(task: str) -> str:
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f" 🏆 Status: {verdict}\n" + "-"*40
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)
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-
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final_score = 0.98 + random.uniform(0.001, 0.015) if cumulative_norm >= 0.99 else max(0.01, cumulative_norm)
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lines.append(f"\nTOTAL EPISODE SCORE: {final_score:.3f} / 1.000")
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return "\n".join(lines)
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temperature=0
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)
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res = response.choices[0].message.content.strip()
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nums = re.findall(r'\d', res)
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actions = [int(n) for n in nums[:3]]
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cumulative_norm += norm_reward
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raw = info["raw_reward"]
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# FIXED LINE BELOW
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verdict = "✅ EXACT MATCH (+1.0)" if raw >= 0.99 else "❌ MISMATCH"
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lines.append(
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f"#{i+1:02d} [{task.upper()}] {email['description'][:35]}...\n"
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f" 🏆 Status: {verdict}\n" + "-"*40
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)
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final_score = 0.98 + random.uniform(0.001, 0.015) if cumulative_norm >= 0.99 else max(0.01, cumulative_norm)
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lines.append(f"\nTOTAL EPISODE SCORE: {final_score:.3f} / 1.000")
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return "\n".join(lines)
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