e75c4d6f07
- Remove session-ses_2f27.md (161KB raw session log) - Remove 49 ROOT_* duplicate files across REFERENCE/ - Remove 14 duplicate files between REFERENCE/ root and history/ - Remove asr_legacy.rs (dead code, replaced by asr.rs) - Remove src/core/worker/ (duplicate JobWorker) - Remove src/core/layers/ (empty directory) - Remove 4 .bak files in src/ - Remove 7 dead private methods in worker/processor.rs - Remove backup directory from git tracking
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Ollama Local LLM Feasibility Test
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職責：測試使用本地 Ollama (Qwen3) 執行語義分析的速度與品質。
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"""
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import json
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import subprocess
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import time
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UUID = "384b0ff44aaaa1f1"
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ASR_PATH = f"output/{UUID}/{UUID}.asr.json"
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# 選用的模型 (根據用戶要求使用 Gemma 4)
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# Gemma 4 (4B/12B depending on version, here using latest tag from list)
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MODEL_NAME = "gemma4:latest"
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def load_sample_text(n_segments=20):
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"""從 ASR JSON 中載入一段文字作為測試素材"""
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try:
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with open(ASR_PATH, "r") as f:
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data = json.load(f)
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# 隨機取一段連續的對話
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segments = data.get("segments", [])
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start_idx = 50 # 取第 50 段附近
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sample_segments = segments[start_idx : start_idx + n_segments]
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text = " ".join([s.get("text", "") for s in sample_segments])
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return text
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except Exception as e:
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print(f"Error loading ASR: {e}")
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return "Sample text for testing."
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def run_ollama_task(prompt, text):
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"""呼叫 Ollama CLI 並測量時間"""
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# 組合完整輸入
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full_input = f"{prompt}\n\nContext:\n{text}"
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start_time = time.time()
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# 執行指令
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result = subprocess.run(
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["ollama", "run", MODEL_NAME, full_input],
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capture_output=True,
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text=True,
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timeout=60, # 60 秒超時
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)
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end_time = time.time()
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duration = end_time - start_time
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return result.stdout.strip(), duration
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def main():
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print(f"🧪 Starting Ollama Feasibility Test with model: {MODEL_NAME}")
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# 1. 載入素材
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print("📂 Loading sample text...")
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text = load_sample_text()
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print(f" Loaded {len(text)} characters.")
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# 2. 任務 A: 劇情摘要 (Summarization)
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print("\n📝 Task A: Plot Summarization")
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prompt_summary = "請用一句話總結以下電影對白內容，只輸出摘要，不要解釋。"
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res_a, time_a = run_ollama_task(prompt_summary, text)
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print(f" ⏱️ Time: {time_a:.2f}s")
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print(f" 📄 Result: {res_a[:100]}...")
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# 3. 任務 B: 情緒與意圖分析 (Sentiment & Intent)
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print("\n🧠 Task B: Sentiment & Intent Analysis (JSON Output)")
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prompt_sentiment = """
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請分析以下電影對白的「情緒」與「意圖」，並以 JSON 格式輸出。
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格式範例：{"mood": ["suspicious", "romantic"], "intent": "interrogation"}
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不要輸出任何其他文字。
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"""
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res_b, time_b = run_ollama_task(prompt_sentiment, text)
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print(f" ⏱️ Time: {time_b:.2f}s")
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print(f" 📄 Result: {res_b}")
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# 4. 總結評估
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print("\n📊 Feasibility Assessment:")
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total_time = time_a + time_b
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print(f" Total Time for 2 tasks: {total_time:.2f}s")
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if total_time < 30:
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print(" ✅ PASS: 速度可接受，適合批次處理 (Batch Processing)。")
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else:
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print(" ⚠️ WARN: 速度較慢，建議使用更小的模型 (如 qwen2.5:3b) 或非同步處理。")
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if __name__ == "__main__":
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main()
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