Initial commit: Screen Leads app

Screen-capture lead tool: capture agent, Claude vision extractor with pluggable site recipes (LinkedIn), canonical funnel model, SQLite storage, FastAPI backend, dashboard, and setup/run scripts. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-27 07:43:33 +05:30
commit c1eef4c6aa
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+"""LinkedIn profile recipe — the first supported site.
+
+Target page: a person's profile (linkedin.com/in/...), typically reached from
+search. Anything else on LinkedIn (feed, search results, messaging) is not a
+target; non-LinkedIn pages don't match at all.
+"""
+from __future__ import annotations
+
+import re
+from typing import Any, Optional
+
+from core.models import Lead, PageContext, PageMatch
+from .base import SiteRecipe
+
+_PROFILE_RE = re.compile(r"linkedin\.com/in/[^/?#]+", re.I)
+_LINKEDIN_RE = re.compile(r"linkedin\.com", re.I)
+
+
+class LinkedInRecipe(SiteRecipe):
+    site_id = "linkedin"
+    display_name = "LinkedIn"
+    enabled = True
+    target_page_type = "profile"
+
+    def matches(self, ctx: PageContext) -> Optional[PageMatch]:
+        url = ctx.best_url
+        if url:
+            if _PROFILE_RE.search(url):
+                return PageMatch(self.site_id, "profile", is_target=True)
+            if _LINKEDIN_RE.search(url):
+                return PageMatch(self.site_id, "other", is_target=False)
+            return None
+        # No URL available: fall back to the AI's visual read.
+        if (ctx.ai_site or "").lower() == "linkedin":
+            page_type = (ctx.ai_page_type or "other").lower()
+            return PageMatch(self.site_id, page_type, is_target=(page_type == "profile"))
+        return None
+
+    @property
+    def extraction_schema(self) -> dict[str, Any]:
+        str_fields = [
+            "full_name", "headline", "current_title", "current_company",
+            "location", "about", "email", "phone", "website",
+        ]
+        props: dict[str, Any] = {f: {"type": "string"} for f in str_fields}
+        props["other_profiles"] = {"type": "array", "items": {"type": "string"}}
+        props["confidence"] = {"type": "number"}
+        return {
+            "type": "object",
+            "properties": props,
+            "required": str_fields + ["other_profiles", "confidence"],
+            "additionalProperties": False,
+        }
+
+    @property
+    def extraction_prompt(self) -> str:
+        return (
+            "These screenshots are a single LinkedIn profile page, captured top "
+            "to bottom. Extract the person's details into the required JSON. "
+            "Rules: use an empty string \"\" for any field not visible; do NOT "
+            "guess or invent contact details. Email, phone and website usually "
+            "only appear if the user opened the 'Contact info' panel — extract "
+            "them only if they are actually shown. 'other_profiles' is any other "
+            "social/web links visible (Twitter/X, GitHub, personal site). "
+            "'confidence' is 0-1 for how legible the profile was."
+        )
+
+    def to_canonical(self, raw: dict[str, Any]) -> Lead:
+        socials: dict[str, str] = {}
+        for link in raw.get("other_profiles") or []:
+            low = link.lower()
+            if "github.com" in low:
+                socials["github"] = link
+            elif "twitter.com" in low or "x.com" in low:
+                socials["twitter"] = link
+            else:
+                socials.setdefault("other", link)
+        return Lead(
+            full_name=raw.get("full_name", "") or "",
+            headline=raw.get("headline", "") or "",
+            title=raw.get("current_title", "") or "",
+            company=raw.get("current_company", "") or "",
+            location=raw.get("location", "") or "",
+            about=raw.get("about", "") or "",
+            email=raw.get("email", "") or "",
+            phone=raw.get("phone", "") or "",
+            website=raw.get("website", "") or "",
+            social_handles=socials,
+            source_site=self.site_id,
+            confidence=float(raw.get("confidence", 0) or 0),
+        )