File size: 4,027 Bytes
bee9bbb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | """Source-grounded presentation for the Deep Learning Specialization prototype."""
from __future__ import annotations
from pathlib import Path
from typing import Any
from jinja2 import Environment, FileSystemLoader, select_autoescape
TEMPLATE_ROOT = Path(__file__).resolve().parent / "templates"
_TEMPLATES = Environment(
loader=FileSystemLoader(TEMPLATE_ROOT),
autoescape=select_autoescape(("html", "xml")),
)
_COURSE_PRESENTATION = {
"neural-networks-deep-learning": {
"number": 1,
"asset": "course-neural-networks.png",
"duration": "4 weeks",
},
"improving-deep-neural-networks": {
"number": 2,
"asset": "course-improving-networks.png",
"duration": "3 weeks",
},
"structuring-machine-learning-projects": {
"number": 3,
"asset": "course-structuring-projects.png",
"duration": "2 weeks",
},
"convolutional-neural-networks": {
"number": 4,
"asset": "course-convolutional.png",
"duration": "4 weeks",
},
"sequence-models": {
"number": 5,
"asset": "course-sequence-models.png",
"duration": "4 weeks",
},
}
def render_specialization_body(
*,
components: list[dict[str, Any]],
authenticated: bool,
specialization: dict[str, Any] | None = None,
) -> str:
"""Render the observed English page while keeping all actions clone-local.
For programs whose component records were not individually observed, the
program's own ``syllabus`` (observed course titles) synthesizes the course
list so the series section is never empty.
"""
specialization = specialization or {}
courses = []
for component in components:
presentation = _COURSE_PRESENTATION.get(str(component["id"]))
if presentation is None:
number = int(component.get("component_number", len(courses) + 1))
presentation = {
"number": number,
"asset": "course-neural-networks.png",
"duration": "4 weeks",
}
courses.append({**component, **presentation})
courses.sort(key=lambda course: int(course["number"]))
program_id = str(specialization.get("id", "deep-learning-specialization"))
is_deep_learning = program_id == "deep-learning-specialization"
instructor_names = (
["Andrew Ng", "Younes Bensouda Mourri", "Kian Katanforoosh"]
if is_deep_learning
else [f"{str(specialization.get('provider', 'Program'))} instructor team"]
)
return _TEMPLATES.get_template("pages/specialization.html").render(
authenticated=authenticated,
courses=courses,
program={
"id": program_id,
"title": str(specialization.get("title", "Deep Learning Specialization")),
"provider": str(specialization.get("provider", "DeepLearning.AI")),
"subject": str(specialization.get("subject", "Data Science")),
"subtitle": (
"Become a Machine Learning expert."
if is_deep_learning
else f"Build job-ready skills in {str(specialization.get('subject', 'this field'))}."
),
"lead": (
"Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!"
if is_deep_learning
else "Learn from industry experts and build a strong foundation with hands-on projects and real-world applications."
),
"instructors": instructor_names,
"top_instructor": is_deep_learning,
"course_count": len(courses),
"reviews": "147,228" if is_deep_learning else "deterministic local learner summary",
"level": "Intermediate" if is_deep_learning else "Beginner",
"duration": "3 months at 10 hours a week" if is_deep_learning else "Flexible schedule",
"enrolled": "997,307" if is_deep_learning else "10,000+",
},
)
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