feat: update Python processors and add utility scripts

- Update ASR, face, OCR, pose processors
- Add release pre-flight check script
- Add synonym generation, chunk processing scripts
- Add face recognition, stamp search utilities

scripts/test_llm_capabilities.py

@@ -0,0 +1,124 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Local LLM (Gemma 4) Capability & Speed Benchmark
+"""
+
+import json
+import time
+import subprocess
+
+UUID = "384b0ff44aaaa1f1"
+ASR_PATH = f"output/{UUID}/{UUID}.asr.json"
+MODEL = "gemma4:latest"
+
+
+def load_context(n_segments=20):
+    try:
+        with open(ASR_PATH, "r") as f:
+            data = json.load(f)
+        segments = data.get("segments", [])[50 : 50 + n_segments]  # Pick a middle chunk
+        text = " ".join([s.get("text", "") for s in segments])
+        return text
+    except Exception as e:
+        return f"Error loading context: {e}"
+
+
+def run_test(name, prompt_template, context_text):
+    print(f"\n🧪 Testing: {name}")
+    print("-" * 50)
+
+    prompt = prompt_template.format(context=context_text)
+    full_input = f"{prompt}\n\nContext:\n{context_text}"
+
+    start = time.time()
+    try:
+        result = subprocess.run(
+            ["ollama", "run", MODEL, full_input],
+            capture_output=True,
+            text=True,
+            timeout=120,
+        )
+        duration = time.time() - start
+        output = result.stdout.strip()
+
+        # Check if it's JSON (basic check)
+        is_json = output.startswith("{") and output.endswith("}")
+        tag = "JSON ✅" if is_json else "Text ⚠️"
+
+        print(f"⏱️ Duration: {duration:.2f}s | Format: {tag}")
+        print(f"🤖 Output: {output[:300]}...")
+        return duration, output
+
+    except Exception as e:
+        duration = time.time() - start
+        print(f"❌ Failed ({duration:.2f}s): {e}")
+        return duration, None
+
+
+def main():
+    print(f"🚀 Starting Gemma 4 Capability Test on Context ({MODEL})")
+    context = load_context()
+    print(f"📂 Loaded Context: {len(context)} chars")
+    if len(context) < 50:
+        print("⚠️ Context too short, aborting.")
+        return
+
+    print(f"👀 Preview: {context[:100]}...")
+
+    results = []
+
+    # Test 1: Summarization
+    results.append(
+        run_test(
+            "1. Plot Summarization (摘要)",
+            "Summarize the following movie dialogue into ONE sentence. Do not explain, just give the summary.",
+            context,
+        )
+    )
+
+    # Test 2: 5W1H Extraction
+    results.append(
+        run_test(
+            "2. 5W1H Entity Extraction (資訊提取)",
+            "Extract the following information from the text and output valid JSON only:\n{{'who': '...', 'what': '...', 'where': '...', 'when': '...'}}.",
+            context,
+        )
+    )
+
+    # Test 3: Sentiment Analysis
+    results.append(
+        run_test(
+            "3. Sentiment & Mood Detection (情緒分析)",
+            "Analyze the emotional tone of the dialogue. Output JSON: {{'mood': ['...'], 'tension_level': 'high/medium/low'}}.",
+            context,
+        )
+    )
+
+    # Test 4: Logical Reasoning (Plot Deduction)
+    results.append(
+        run_test(
+            "4. Logical Reasoning (邏輯推理)",
+            "Based on the text, answer: What are the characters discussing or investigating? Be specific.",
+            context,
+        )
+    )
+
+    # Summary
+    valid_results = [r[0] for r in results if r[0] is not None]
+    if valid_results:
+        total = sum(valid_results)
+        avg = total / len(valid_results)
+        print(f"\n📊 Benchmark Summary:")
+        print(f"Total Time for 4 tasks: {total:.2f}s")
+        print(f"Average Time: {avg:.2f}s per task")
+
+        if avg > 20:
+            print(
+                "\n⚠️ Note: Gemma 4 is accurate but slow. Consider asynchronous processing or smaller models for speed."
+            )
+        else:
+            print("\n✅ Note: Performance is acceptable for background tasks.")
+
+
+if __name__ == "__main__":
+    main()