NANDI
d0e0e53087
Async pipeline: streamlink + ffmpeg frame capture → Gemini Vision API analysis → rich console output + log file. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
import base64
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from google import genai
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from google.genai import types
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SYSTEM_PROMPT_RU = (
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"Ты анализируешь кадры с Twitch-стрима. "
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"Кратко опиши что происходит на экране: игра, действия стримера, "
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"интерфейс, чат, оверлеи. Будь лаконичен (2-3 предложения). "
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"Если ничего не изменилось по сравнению с предыдущим описанием, "
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"скажи 'Без изменений' и уточни только новые детали."
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)
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SYSTEM_PROMPT_EN = (
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"You are analyzing frames from a Twitch stream. "
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"Briefly describe what's happening on screen: game, streamer actions, "
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"UI, chat, overlays. Be concise (2-3 sentences). "
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"If nothing changed compared to the previous description, "
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"say 'No changes' and only note new details."
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)
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class VisionAnalyzer:
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def __init__(self, api_key: str, base_url: str | None = None, lang: str = "ru"):
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client_kwargs = {"api_key": api_key}
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if base_url:
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client_kwargs["http_options"] = types.HttpOptions(base_url=base_url)
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self.client = genai.Client(**client_kwargs)
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self.model = "gemini-2.0-flash"
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self.system_prompt = SYSTEM_PROMPT_RU if lang == "ru" else SYSTEM_PROMPT_EN
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self.previous_description: str | None = None
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async def analyze_frame(self, frame_data: bytes) -> str:
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b64_image = base64.b64encode(frame_data).decode("utf-8")
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contents = []
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if self.previous_description:
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contents.append(
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types.Content(
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role="user",
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parts=[
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types.Part.from_text(
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text=f"Предыдущее описание: {self.previous_description}"
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)
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],
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)
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)
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contents.append(
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types.Content(
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role="model",
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parts=[types.Part.from_text(text="Понял, учту контекст.")],
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)
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)
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contents.append(
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types.Content(
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role="user",
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parts=[
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types.Part.from_bytes(data=frame_data, mime_type="image/jpeg"),
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types.Part.from_text(text="Опиши что сейчас происходит на стриме."),
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],
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)
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)
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response = await self.client.aio.models.generate_content(
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model=self.model,
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contents=contents,
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config=types.GenerateContentConfig(
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system_instruction=self.system_prompt,
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max_output_tokens=300,
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temperature=0.3,
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),
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)
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description = response.text or "(нет описания)"
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self.previous_description = description
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return description
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