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"""
Cold start onboarding module
Used to collect initial information from new users
"""
import gradio as gr
from typing import Dict, List
try:
from modules.personalized_learning import UserProfilingSystem
except ImportError:
# Fallback for direct import
from personalized_learning import UserProfilingSystem
def create_onboarding_interface(user_profiling: UserProfilingSystem, available_topics: List[str]):
"""Create cold start onboarding interface"""
def process_onboarding(user_id: str, background: str, learning_style: str,
learning_pace: str, learning_goals: List[str],
knowledge_survey: Dict[str, float]) -> Dict:
"""Process cold start data collection"""
# Build onboarding data
onboarding_data = {
'learning_style': learning_style,
'learning_pace': learning_pace,
'background_experience': background,
'learning_goals': learning_goals if learning_goals else [],
'initial_knowledge_survey': knowledge_survey,
'initial_assessment_completed': True
}
# Complete cold start setup
profile = user_profiling.complete_onboarding(user_id, onboarding_data)
return {
"status": "success",
"message": f"Onboarding completed for {user_id}",
"profile_summary": user_profiling.get_profile_summary(user_id)
}
def create_onboarding_form():
"""Create cold start form"""
with gr.Blocks(title="Welcome! Let's Get Started") as onboarding:
gr.Markdown("# 🎯 Welcome to Personalized Learning!")
gr.Markdown("We need some information to create your personalized learning path.")
with gr.Row():
user_id_input = gr.Textbox(
label="User ID",
placeholder="Enter your user ID",
value="new_user"
)
with gr.Accordion("📋 Step 1: Background Information", open=True):
background_input = gr.Radio(
label="What's your experience with ADAS systems?",
choices=[
("Beginner - I'm new to ADAS systems", "beginner"),
("Intermediate - I know some basics", "intermediate"),
("Experienced - I have good knowledge", "experienced")
],
value="beginner"
)
with gr.Accordion("🎨 Step 2: Learning Preferences", open=True):
learning_style_input = gr.Radio(
label="How do you prefer to learn?",
choices=[
("Visual - I like diagrams and illustrations", "visual"),
("Textual - I prefer reading and explanations", "textual"),
("Practical - I learn by doing", "practical"),
("Mixed - I like a combination", "mixed")
],
value="mixed"
)
learning_pace_input = gr.Radio(
label="What's your preferred learning pace?",
choices=[
("Slow - I like to take my time", "slow"),
("Medium - Normal pace is fine", "medium"),
("Fast - I want to learn quickly", "fast")
],
value="medium"
)
with gr.Accordion("🎯 Step 3: Learning Goals", open=True):
learning_goals_input = gr.CheckboxGroup(
label="What are your learning goals? (Select all that apply)",
choices=[
"Understand basic ADAS functions",
"Learn how to operate ADAS features",
"Master advanced ADAS capabilities",
"Troubleshoot ADAS issues",
"Prepare for certification",
"General knowledge improvement"
],
value=["Understand basic ADAS functions"]
)
with gr.Accordion("📊 Step 4: Initial Knowledge Assessment", open=True):
gr.Markdown("Rate your familiarity with each topic (0 = No knowledge, 1 = Expert)")
knowledge_sliders = {}
for topic in available_topics:
# Simplify topic name for display
display_name = topic.replace("Function of ", "").replace(" Assist", "")
knowledge_sliders[topic] = gr.Slider(
label=display_name,
minimum=0.0,
maximum=1.0,
value=0.0,
step=0.1
)
with gr.Row():
submit_btn = gr.Button("Complete Setup", variant="primary")
output_result = gr.JSON(label="Setup Result")
def submit_onboarding(user_id: str, background: str, learning_style: str,
learning_pace: str, learning_goals: List[str],
**knowledge_values):
"""Submit cold start data"""
# Build knowledge survey dictionary
knowledge_survey = {}
for topic in available_topics:
knowledge_survey[topic] = knowledge_values.get(topic, 0.0)
# Process background selection (extract value from tuple)
if isinstance(background, tuple):
background = background[1] if len(background) > 1 else background[0]
if isinstance(learning_style, tuple):
learning_style = learning_style[1] if len(learning_style) > 1 else learning_style[0]
if isinstance(learning_pace, tuple):
learning_pace = learning_pace[1] if len(learning_pace) > 1 else learning_pace[0]
result = process_onboarding(
user_id, background, learning_style, learning_pace,
learning_goals, knowledge_survey
)
return result
# Build input list
inputs = [user_id_input, background_input, learning_style_input,
learning_pace_input, learning_goals_input] + list(knowledge_sliders.values())
submit_btn.click(
submit_onboarding,
inputs=inputs,
outputs=output_result
)
return onboarding
return create_onboarding_form()
def check_and_show_onboarding(user_profiling: UserProfilingSystem, user_id: str) -> bool:
"""Check if cold start interface needs to be shown"""
return user_profiling.is_cold_start(user_id)
def get_onboarding_data_summary(user_profiling: UserProfilingSystem, user_id: str) -> Dict:
"""Get summary of data collected during cold start"""
if user_profiling.is_cold_start(user_id):
return {
"status": "cold_start",
"message": "User has not completed onboarding"
}
profile = user_profiling.get_or_create_profile(user_id)
return {
"status": "completed",
"has_completed_onboarding": profile.has_completed_onboarding,
"background_experience": profile.background_experience,
"learning_style": profile.learning_style,
"learning_pace": profile.learning_pace,
"learning_goals": profile.learning_goals if profile.learning_goals else [],
"initial_knowledge_survey": profile.initial_knowledge_survey if profile.initial_knowledge_survey else {},
"initial_assessment_completed": profile.initial_assessment_completed
}
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