8f05a7c188
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
122 lines
3.7 KiB
Python
122 lines
3.7 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Test Parent Chunk Summary Generation (Gemma 4)
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"""
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import json
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import ollama
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import time
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# Configuration
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UUID = "384b0ff44aaaa1f1"
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ASR_PATH = f"output/{UUID}/{UUID}.asr.json"
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MODEL = "gemma4:latest"
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# The Prompt Template
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PARENT_SUMMARY_PROMPT = """
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You are an expert film analyst. Analyze the following movie dialogue segment (approx 60 seconds).
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Your task is to generate a structured JSON summary containing:
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1. **narrative_summary**: A one-sentence summary of the main event/plot point.
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2. **entities**: Key information extracted:
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- `who`: List of characters involved.
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- `where`: Inferred location (e.g., "Apartment", "Train").
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- `objects`: Key props mentioned (e.g., "Ticket", "Money").
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3. **emotional_arc**: The emotional transition:
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- `start_mood`: Mood at the beginning.
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- `end_mood`: Mood at the end.
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4. **plot_sequence**:
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- `scene_type`: Type of scene (e.g., "Confrontation", "Romance", "Discovery").
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- `key_action`: The main action taking place.
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**IMPORTANT RULES:**
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- Output **ONLY** valid JSON.
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- Do NOT include "Thinking Process" or markdown formatting.
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- If information is unknown, use "Unknown".
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- Context: This is from the movie "Charade" (1963).
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Dialogue:
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{context}
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"""
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def load_sample(start_index, count=20):
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"""Load a slice of dialogue to simulate a Parent Chunk"""
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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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segments = data.get("segments", [])
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selected = segments[start_index : start_index + count]
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text = " ".join([s.get("text", "") for s in selected])
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print(f"📂 Loaded Sample {start_index}: {len(selected)} segments.")
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return text
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except Exception as e:
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return f"Error: {e}"
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def run_test(name, context_text):
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print(f"\n🧪 Testing: {name}")
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print("-" * 50)
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print(f"📖 Input Preview: {context_text[:100]}...")
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prompt = PARENT_SUMMARY_PROMPT.format(context=context_text)
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try:
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start = time.time()
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response = ollama.chat(
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model=MODEL, messages=[{"role": "user", "content": prompt}]
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)
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duration = time.time() - start
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content = response["message"]["content"]
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# Clean up thinking tags if present
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if "```json" in content:
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content = content.split("```json")[1].split("```")[0]
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elif "Thinking..." in content:
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# crude cleanup for demo
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content = content.split("...")[-1]
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# Attempt parse
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try:
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result = json.loads(content.strip())
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print(f"✅ Success ({duration:.2f}s)")
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print(json.dumps(result, indent=2))
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return True
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except json.JSONDecodeError:
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print(f"⚠️ JSON Parse Failed ({duration:.2f}s)")
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print(content[:500])
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return False
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except Exception as e:
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print(f"❌ API Error: {e}")
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return False
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def main():
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print(f"🚀 Starting Parent Chunk Summary Tests on '{UUID}'")
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# Test 1: Early Dialogue (Entities & Narrative Focus)
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# "possessed a ticket of passage..."
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txt1 = load_sample(start_index=10)
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res1 = run_test("Test 1: Early Plot (Entities & Narrative)", txt1)
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time.sleep(2) # Cool down
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# Test 2: Middle Conflict (Emotional Arc Focus)
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# "where did he keep his money..." (From previous context)
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txt2 = load_sample(start_index=50)
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res2 = run_test("Test 2: Conflict (Emotional Arc)", txt2)
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time.sleep(2) # Cool down
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# Test 3: Later Dialogue (Plot Sequence Focus)
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# Looking for a scene involving a conclusion or death aftermath
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# Let's pick a later section to test robustness
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txt3 = load_sample(start_index=150)
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res3 = run_test("Test 3: Late Plot (Sequence)", txt3)
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if __name__ == "__main__":
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main()
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