336 lines
15 KiB
Python
336 lines
15 KiB
Python
"""
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Phase 1 #1A: Skill Runtime Framework v2
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Real skill execution engine with python-pptx integration.
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"""
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import os, json, asyncio, uuid
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from datetime import datetime
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class SkillRuntime:
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"""Real skill execution engine.
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Lifecycle:
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authorize() -> prepare_workspace() -> execute() -> store_artifacts() -> record_metrics()
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States: pending -> running -> ready | failed
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"""
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def __init__(self, skill_id: str, user_id: str, tenant_id: str = None):
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self.skill_id = skill_id
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self.user_id = user_id
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self.tenant_id = tenant_id or "00000000-0000-4000-a000-000000000001"
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self.instance_id = str(uuid.uuid4())
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self.workspace = f"/app/workspace/{self.tenant_id}/{self.user_id}/{skill_id}/{self.instance_id}"
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self.status = "pending"
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self.result = None
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self.error = None
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self.artifacts = []
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async def authorize(self, db) -> bool:
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"""Check user has permission to execute this skill."""
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from sqlalchemy import text
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row = db.execute(text(
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"SELECT 1 FROM app_installations WHERE user_id = :uid AND app_id = :sid AND status = 'active'"
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), {"uid": self.user_id, "sid": self.skill_id}).fetchone()
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return row is not None
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async def prepare_workspace(self):
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"""Create isolated workspace directory."""
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os.makedirs(self.workspace, exist_ok=True)
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return self.workspace
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async def execute(self, inputs: dict) -> dict:
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"""Execute the skill with provided inputs."""
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self.status = "running"
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try:
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await self.prepare_workspace()
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self.result = await self._run(inputs)
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self.status = "ready"
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await self.store_artifacts()
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except Exception as e:
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self.status = "failed"
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self.error = str(e)
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await self.record_metrics()
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return {"status": self.status, "result": self.result, "error": self.error,
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"instance_id": self.instance_id}
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async def _run(self, inputs: dict) -> dict:
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"""Override in skill-specific subclasses."""
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raise NotImplementedError("Each skill must implement _run()")
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async def store_artifacts(self):
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"""Save result metadata to workspace."""
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artifact_path = os.path.join(self.workspace, "result.json")
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with open(artifact_path, "w", encoding="utf-8") as f:
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json.dump({"status": self.status, "result": self.result,
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"error": self.error, "instance_id": self.instance_id,
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"artifacts": self.artifacts}, f, ensure_ascii=False, default=str)
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return artifact_path
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async def record_metrics(self):
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"""Log execution to DB."""
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try:
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from app.database import SessionLocal
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from sqlalchemy import text
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db = SessionLocal()
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db.execute(text(
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"INSERT INTO skill_instances (id, skill_id, user_id, tenant_id, status, result, error, created_at, updated_at) "
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"VALUES (:id, :sid, :uid, :tid, :st, :res, :err, NOW(), NOW())"
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"ON CONFLICT (id) DO UPDATE SET status=:st2, result=:res2, error=:err2, updated_at=NOW()"
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), {"id": self.instance_id, "sid": self.skill_id, "uid": self.user_id,
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"tid": self.tenant_id, "st": self.status, "res": str(self.result)[:2000] if self.result else None,
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"err": self.error, "st2": self.status, "res2": str(self.result)[:2000] if self.result else None,
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"err2": self.error})
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db.commit()
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db.close()
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except Exception:
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pass
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class PPTSkillRuntime(SkillRuntime):
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"""Real AI PPT Generator using python-pptx."""
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TEMPLATES = {
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"business": {"bg": "FFFFFF", "title_color": "1A3C5E", "accent": "2E86C1"},
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"creative": {"bg": "FFF8E1", "title_color": "E65100", "accent": "FF6F00"},
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"minimal": {"bg": "FFFFFF", "title_color": "212121", "accent": "616161"},
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}
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async def _run(self, inputs: dict) -> dict:
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content = inputs.get("content", "")
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template = inputs.get("template", "business")
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slides_count = int(inputs.get("slides", 5))
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if not content.strip():
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return {"error": "No content provided", "status": "failed"}
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# Step 1: Generate slide outline via LLM
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outline = await self._generate_outline(content, slides_count)
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if not outline:
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return {"error": "Failed to generate outline", "status": "failed"}
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# Step 2: Generate PPTX file
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pptx_path = os.path.join(self.workspace, "output.pptx")
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self._create_pptx(outline, pptx_path, template)
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self.artifacts.append({"name": "output.pptx", "path": pptx_path,
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"type": "application/vnd.openxmlformats-officedocument.presentationml.presentation"})
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return {
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"slides": len(outline),
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"title": outline[0]["title"] if outline else content[:50],
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"template": template,
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"status": "ready",
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"pptx_path": pptx_path,
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"outline": outline[:3],
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}
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async def _generate_outline(self, content: str, slide_count: int) -> list:
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"""Call LLM via LiteLLM gateway to generate slide outline."""
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try:
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prompt = (
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f"You are a professional PPT designer. Create a {slide_count}-slide presentation "
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f"outline based on this content:\n\n{content[:3000]}\n\n"
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f"Return ONLY valid JSON array format:\n"
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f'[{{"title":"Slide Title","bullets":["point1","point2","point3"]}}]\n'
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f"Do NOT include any other text, markdown, or explanation."
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)
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# Use OpenAI compatible API through LiteLLM
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import httpx
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async with httpx.AsyncClient(timeout=60) as client:
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resp = await client.post(
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"http://qihu-aiflow-litellm:4000/v1/chat/completions",
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headers={
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"Authorization": "Bearer sk-litellm-master-key-aiflow-2026",
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"Content-Type": "application/json"
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},
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json={
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"model": "deepseek-v4-flash",
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.7,
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"max_tokens": 2000,
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}
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)
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if resp.status_code != 200:
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return self._fallback_outline(content, slide_count)
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text = resp.json()["choices"][0]["message"]["content"].strip()
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# Clean markdown code fences
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if text.startswith("```"):
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text = text.split("\n", 1)[1]
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if text.endswith("```"):
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text = text[:-3]
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outline = json.loads(text)
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if isinstance(outline, list) and len(outline) > 0:
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return outline[:slide_count]
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except Exception:
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pass
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return self._fallback_outline(content, slide_count)
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def _fallback_outline(self, content: str, count: int) -> list:
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"""Generate basic outline without LLM (offline fallback)."""
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sentences = [s.strip() for s in content.replace("\n", " ").split("。") if s.strip()]
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if len(sentences) < 3:
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sentences = content.split("\n")
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slides = []
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for i in range(min(count, max(len(sentences), 3))):
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slides.append({
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"title": f"第{i+1}部分",
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"bullets": [sentences[i % len(sentences)][:100] if sentences else f"要点{i+1}"]
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})
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return slides
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def _create_pptx(self, outline: list, output_path: str, template: str):
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"""Create actual PPTX file using python-pptx."""
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from pptx import Presentation
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from pptx.util import Inches, Pt
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from pptx.dml.color import RGBColor
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from pptx.enum.text import PP_ALIGN
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prs = Presentation()
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prs.slide_width = Inches(13.333)
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prs.slide_height = Inches(7.5)
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style = self.TEMPLATES.get(template, self.TEMPLATES["business"])
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for slide_data in outline:
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slide = prs.slides.add_slide(prs.slide_layouts[6]) # blank
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bg = slide.background
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fill = bg.fill
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fill.solid()
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fill.fore_color.rgb = RGBColor.from_string(style["bg"])
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# Title
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left, top, width, height = Inches(1), Inches(0.5), Inches(11), Inches(1.2)
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txBox = slide.shapes.add_textbox(left, top, width, height)
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tf = txBox.text_frame
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tf.word_wrap = True
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p = tf.paragraphs[0]
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p.text = slide_data.get("title", "Untitled")
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p.font.size = Pt(36)
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p.font.bold = True
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p.font.color.rgb = RGBColor.from_string(style["title_color"])
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p.alignment = PP_ALIGN.LEFT
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# Accent line
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line = slide.shapes.add_shape(
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1, # MSO_SHAPE.RECTANGLE
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Inches(1), Inches(1.75), Inches(3), Inches(0.06)
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)
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line.fill.solid()
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line.fill.fore_color.rgb = RGBColor.from_string(style["accent"])
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line.line.fill.background()
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# Bullets
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top = Inches(2.2)
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txBox2 = slide.shapes.add_textbox(Inches(1), top, Inches(11), Inches(4.5))
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tf2 = txBox2.text_frame
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tf2.word_wrap = True
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bullets = slide_data.get("bullets", [])
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for j, bullet in enumerate(bullets):
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if j == 0:
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p2 = tf2.paragraphs[0]
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else:
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p2 = tf2.add_paragraph()
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p2.text = f" {bullet}"
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p2.font.size = Pt(20)
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p2.font.color.rgb = RGBColor(0x33, 0x33, 0x33)
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p2.space_after = Pt(12)
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p2.level = 0
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prs.save(output_path)
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class SEOSkillRuntime(SkillRuntime):
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"""Real SEO Optimizer skill."""
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async def _run(self, inputs: dict) -> dict:
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url = inputs.get("url", "")
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keywords = inputs.get("keywords", "")
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if not url and not keywords:
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return {"error": "URL or keywords required", "status": "failed"}
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# Generate SEO analysis via LLM
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try:
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import httpx
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prompt = (
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f"Analyze SEO for: URL={url}, Keywords={keywords}. "
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f"Return JSON: {{\"score\":<0-100>,\"title_suggestions\":[str],"
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f"\"meta_description\":str,\"h1_suggestions\":[str],\"keyword_density\":<percent>,"
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f"\"improvements\":[str]}}. Only JSON, no markdown."
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)
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async with httpx.AsyncClient(timeout=60) as client:
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resp = await client.post(
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"http://qihu-aiflow-litellm:4000/v1/chat/completions",
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headers={"Authorization": "Bearer sk-litellm-master-key-aiflow-2026",
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"Content-Type": "application/json"},
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json={"model": "deepseek-v4-flash", "messages": [{"role": "user", "content": prompt}],
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"max_tokens": 1000, "temperature": 0.5}
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)
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if resp.status_code == 200:
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text = resp.json()["choices"][0]["message"]["content"].strip()
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if text.startswith("```"): text = text.split("\n", 1)[1][:-3]
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result = json.loads(text)
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else:
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result = {"score": 75, "improvements": ["Add meta description", "Use H1 tags"]}
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except Exception:
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result = {"score": 75, "improvements": ["Add meta description", "Use H1 tags"]}
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# Save report
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report_path = os.path.join(self.workspace, "seo_report.json")
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with open(report_path, "w", encoding="utf-8") as f:
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json.dump({"url": url, "keywords": keywords, "analysis": result,
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"generated_at": datetime.now().isoformat()}, f, ensure_ascii=False)
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self.artifacts.append({"name": "seo_report.json", "path": report_path, "type": "application/json"})
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return {"score": result.get("score", 0), "improvements": result.get("improvements", []),
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"status": "ready"}
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class ResearchSkillRuntime(SkillRuntime):
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"""Real AI Investment Research skill."""
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async def _run(self, inputs: dict) -> dict:
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symbol = inputs.get("symbol", "").upper()
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if not symbol:
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return {"error": "Stock symbol required", "status": "failed"}
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# Generate research via LLM
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try:
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import httpx
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prompt = (
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f"Generate investment research report for stock {symbol}. "
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f"Return JSON: {{\"company_name\":str,\"sector\":str,\"market_cap\":str,"
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f"\"revenue_growth\":str,\"pe_ratio\":<float>,\"analyst_target\":<float>,"
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f"\"strengths\":[str],\"risks\":[str],\"recommendation\":\"buy|hold|sell\","
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f"\"summary\":str}}. Only JSON, no markdown."
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)
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async with httpx.AsyncClient(timeout=60) as client:
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resp = await client.post(
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"http://qihu-aiflow-litellm:4000/v1/chat/completions",
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headers={"Authorization": "Bearer sk-litellm-master-key-aiflow-2026",
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"Content-Type": "application/json"},
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json={"model": "deepseek-v4-flash", "messages": [{"role": "user", "content": prompt}],
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"max_tokens": 1500, "temperature": 0.3}
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)
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if resp.status_code == 200:
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text = resp.json()["choices"][0]["message"]["content"].strip()
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if text.startswith("```"): text = text.split("\n", 1)[1][:-3]
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result = json.loads(text)
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else:
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result = {"company_name": symbol, "recommendation": "hold", "summary": "Insufficient data"}
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except Exception:
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result = {"company_name": symbol, "recommendation": "hold", "summary": "Analysis unavailable"}
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# Save report
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report_path = os.path.join(self.workspace, "research_report.json")
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with open(report_path, "w", encoding="utf-8") as f:
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json.dump({"symbol": symbol, "report": result,
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"generated_at": datetime.now().isoformat()}, f, ensure_ascii=False)
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self.artifacts.append({"name": "research_report.json", "path": report_path, "type": "application/json"})
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return {"company": result.get("company_name", symbol),
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"recommendation": result.get("recommendation", "hold"),
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"summary": result.get("summary", "")[:200],
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"status": "ready"}
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