Base code

backend/api/podcast/presenter_personas.py

@@ -0,0 +1,143 @@
+"""
+Podcast Presenter Personas
+
+Lightweight, podcast-specific presenter persona presets used to steer avatar generation.
+
+Design goals:
+- Market-fit + style consistency without asking end-users to choose sensitive traits.
+- Deterministic persona selection using analysis hints (audience/content type/keywords).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, Optional, List
+
+
+@dataclass(frozen=True)
+class PresenterPersona:
+    id: str
+    label: str
+    target_market: str  # e.g. "global", "us_canada", "uk_eu", "india", "latam"
+    style: str  # e.g. "corporate", "tech_modern", "creator"
+    prompt: str  # prompt fragment to inject
+
+
+# NOTE: Avoid encoding/guessing ethnicity. Keep personas about market-fit + style.
+PERSONAS: Dict[str, PresenterPersona] = {
+    "global_corporate": PresenterPersona(
+        id="global_corporate",
+        label="Global — Corporate Host",
+        target_market="global",
+        style="corporate",
+        prompt=(
+            "professional podcast presenter, business professional attire (white shirt and light gray blazer), "
+            "confident, friendly, camera-ready, neutral background, studio lighting"
+        ),
+    ),
+    "global_tech_modern": PresenterPersona(
+        id="global_tech_modern",
+        label="Global — Tech Modern Host",
+        target_market="global",
+        style="tech_modern",
+        prompt=(
+            "modern professional podcast presenter, contemporary tech-forward style, "
+            "clean minimal studio background, soft studio lighting, friendly and energetic expression"
+        ),
+    ),
+    "global_news_anchor": PresenterPersona(
+        id="global_news_anchor",
+        label="Global — News Anchor",
+        target_market="global",
+        style="news_anchor",
+        prompt=(
+            "professional news-style presenter, polished on-camera appearance, "
+            "formal attire, authoritative yet approachable expression, studio lighting, neutral background"
+        ),
+    ),
+    "india_corporate": PresenterPersona(
+        id="india_corporate",
+        label="India — Corporate Host",
+        target_market="india",
+        style="corporate",
+        prompt=(
+            "professional podcast presenter for the Indian market, business professional attire, "
+            "polished and confident on-camera presence, clean studio background, soft studio lighting"
+        ),
+    ),
+    "us_canada_creator": PresenterPersona(
+        id="us_canada_creator",
+        label="US/Canada — Creator Host",
+        target_market="us_canada",
+        style="creator",
+        prompt=(
+            "professional podcast creator host, business casual style, approachable and conversational expression, "
+            "clean studio background, soft studio lighting"
+        ),
+    ),
+}
+
+
+def get_persona(persona_id: Optional[str]) -> Optional[PresenterPersona]:
+    if not persona_id:
+        return None
+    return PERSONAS.get(persona_id)
+
+
+def list_personas() -> List[PresenterPersona]:
+    return list(PERSONAS.values())
+
+
+def choose_persona_id(
+    audience: Optional[str] = None,
+    content_type: Optional[str] = None,
+    top_keywords: Optional[List[str]] = None,
+) -> str:
+    """
+    Choose a persona id using non-sensitive heuristics from analysis.
+
+    - Uses explicit market hints if present (e.g. "India", "US", "UK", etc.)
+    - Uses content_type / keywords to pick a style
+    - Falls back to global corporate
+    """
+    audience_l = (audience or "").lower()
+    content_l = (content_type or "").lower()
+    keywords_l = [k.lower() for k in (top_keywords or [])]
+
+    # Market hints (explicit only)
+    if any(x in audience_l for x in ["india", "indian"]):
+        market = "india"
+    elif any(x in audience_l for x in ["us", "usa", "united states", "canada", "north america"]):
+        market = "us_canada"
+    elif any(x in audience_l for x in ["uk", "united kingdom", "europe", "eu", "european"]):
+        market = "uk_eu"
+    elif any(x in audience_l for x in ["latam", "latin america", "south america"]):
+        market = "latam"
+    else:
+        market = "global"
+
+    # Style hints
+    style = "corporate"
+    if "news" in content_l or "analysis" in content_l:
+        style = "news_anchor"
+    if any(x in content_l for x in ["tech", "technology", "ai", "software"]) or any(
+        kw in ["ai", "technology", "tech", "software"] for kw in keywords_l
+    ):
+        style = "tech_modern"
+    if any(x in content_l for x in ["casual", "creator", "conversational"]) or any(
+        kw in ["creator", "youtube", "tiktok", "instagram"] for kw in keywords_l
+    ):
+        style = "creator"
+
+    # Map market+style to a concrete persona id
+    if market == "india" and style == "corporate":
+        return "india_corporate"
+    if market == "us_canada" and style == "creator":
+        return "us_canada_creator"
+    if style == "news_anchor":
+        return "global_news_anchor"
+    if style == "tech_modern":
+        return "global_tech_modern"
+    return "global_corporate"
+
+