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import sqlite3
import gradio as gr
from dog_database import get_dog_description, dog_data
from breed_health_info import breed_health_info
from breed_noise_info import breed_noise_info
from scoring_calculation_system import UserPreferences, calculate_compatibility_score
from recommendation_html_format import format_recommendation_html, get_breed_recommendations
from search_history import create_history_tab, create_history_component
def create_recommendation_tab(UserPreferences, get_breed_recommendations, format_recommendation_html, history_component):
with gr.TabItem("Breed Recommendation"):
with gr.Tabs():
with gr.Tab("Find by Criteria"):
gr.HTML("""
<div style='
text-align: center;
padding: 20px 0;
margin: 15px 0;
background: linear-gradient(to right, rgba(66, 153, 225, 0.1), rgba(72, 187, 120, 0.1));
border-radius: 10px;
'>
<p style='
font-size: 1.2em;
margin: 0;
padding: 0 20px;
line-height: 1.5;
background: linear-gradient(90deg, #4299e1, #48bb78);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-weight: 600;
'>
Tell us about your lifestyle, and we'll recommend the perfect dog breeds for you!
</p>
</div>
""")
with gr.Row():
with gr.Column():
living_space = gr.Radio(
choices=["apartment", "house_small", "house_large"],
label="What type of living space do you have?",
info="Choose your current living situation",
value="apartment"
)
yard_access = gr.Radio(
choices=["no_yard", "shared_yard", "private_yard"],
label="Yard Access Type",
info="Available outdoor space",
value="no_yard"
)
exercise_time = gr.Slider(
minimum=0,
maximum=180,
value=60,
label="Daily exercise time (minutes)",
info="Consider walks, play time, and training"
)
exercise_type = gr.Radio(
choices=["light_walks", "moderate_activity", "active_training"],
label="Exercise Style",
info="What kind of activities do you prefer?",
value="moderate_activity"
)
grooming_commitment = gr.Radio(
choices=["low", "medium", "high"],
label="Grooming commitment level",
info="Low: monthly, Medium: weekly, High: daily",
value="medium"
)
with gr.Column():
experience_level = gr.Radio(
choices=["beginner", "intermediate", "advanced"],
label="Dog ownership experience",
info="Be honest - this helps find the right match",
value="beginner"
)
time_availability = gr.Radio(
choices=["limited", "moderate", "flexible"],
label="Time Availability",
info="Time available for dog care daily",
value="moderate"
)
has_children = gr.Checkbox(
label="Have children at home",
info="Helps recommend child-friendly breeds"
)
children_age = gr.Radio(
choices=["toddler", "school_age", "teenager"],
label="Children's Age Group",
info="Helps match with age-appropriate breeds",
visible=False # 默認隱藏，只在has_children=True時顯示
)
noise_tolerance = gr.Radio(
choices=["low", "medium", "high"],
label="Noise tolerance level",
info="Some breeds are more vocal than others",
value="medium"
)
def update_children_age_visibility(has_children):
return gr.update(visible=has_children)
has_children.change(
fn=update_children_age_visibility,
inputs=has_children,
outputs=children_age
)
get_recommendations_btn = gr.Button("Find My Perfect Match! 🔍", variant="primary")
# recommendation_output = gr.HTML(label="Breed Recommendations")
recommendation_output = gr.HTML(
label="Breed Recommendations",
visible=True, # 確保可見性
elem_id="recommendation-output" # 添加唯一ID以便追蹤
)
# def on_find_match_click(*args):
# try:
# user_prefs = UserPreferences(
# living_space=args[0],
# yard_access=args[1],
# exercise_time=args[2],
# exercise_type=args[3],
# grooming_commitment=args[4],
# experience_level=args[5],
# time_availability=args[6],
# has_children=args[7],
# children_age=args[8] if args[7] else None,
# noise_tolerance=args[9],
# space_for_play=True if args[0] != "apartment" else False,
# other_pets=False,
# climate="moderate",
# health_sensitivity="medium",
# barking_acceptance=args[9]
# )
# recommendations = get_breed_recommendations(user_prefs, top_n=10)
# history_results = [{
# 'breed': rec['breed'],
# 'rank': rec['rank'],
# 'overall_score': rec['final_score'],
# 'base_score': rec['base_score'],
# 'bonus_score': rec['bonus_score'],
# 'scores': rec['scores']
# } for rec in recommendations]
# history_component.save_search(
# user_preferences={
# 'living_space': args[0],
# 'yard_access': args[1],
# 'exercise_time': args[2],
# 'exercise_type': args[3],
# 'grooming_commitment': args[4],
# 'experience_level': args[5],
# 'time_availability': args[6],
# 'has_children': args[7],
# 'children_age': args[8] if args[7] else None,
# 'noise_tolerance': args[9],
# 'search_type': 'Criteria'
# },
# results=history_results
# )
# return format_recommendation_html(recommendations, is_description_search=False)
# except Exception as e:
# print(f"Error in find match: {str(e)}")
# import traceback
# print(traceback.format_exc())
# return "Error getting recommendations"
def on_find_match_click(*args):
try:
user_prefs = UserPreferences(
living_space=args[0],
yard_access=args[1],
exercise_time=args[2],
exercise_type=args[3],
grooming_commitment=args[4],
experience_level=args[5],
time_availability=args[6],
has_children=args[7],
children_age=args[8] if args[7] else None,
noise_tolerance=args[9],
space_for_play=True if args[0] != "apartment" else False,
other_pets=False,
climate="moderate",
health_sensitivity="medium",
barking_acceptance=args[9]
)
recommendations = get_breed_recommendations(user_prefs, top_n=10)
# 確保推薦結果不為空
if not recommendations:
return gr.update(value="No matching breeds found. Please try adjusting your criteria.")
# 創建完整的歷史記錄結果
history_results = []
for i, rec in enumerate(recommendations, 1):
history_results.append({
'breed': rec['breed'],
'rank': i, # 確保排名從1開始
'overall_score': rec['final_score'],
'base_score': rec['base_score'],
'bonus_score': rec['bonus_score'],
'scores': rec['scores'],
'display_score': f"{rec['final_score']*100:.1f}%" # 添加顯示用的分數
})
# 保存完整的搜索記錄
history_component.save_search(
user_preferences={
'living_space': args[0],
'yard_access': args[1],
'exercise_time': args[2],
'exercise_type': args[3],
'grooming_commitment': args[4],
'experience_level': args[5],
'time_availability': args[6],
'has_children': args[7],
'children_age': args[8] if args[7] else None,
'noise_tolerance': args[9],
'search_type': 'Criteria'
},
results=history_results
)
# 生成HTML結果
html_result = format_recommendation_html(recommendations, is_description_search=False)
enhanced_html = f"""
{html_result}
<script>
document.getElementById('recommendation-output').scrollIntoView({{
behavior: 'smooth'
}});
</script>
"""
return gr.update(value=enhanced_html)
except Exception as e:
print(f"Error in find match: {str(e)}")
import traceback
print(traceback.format_exc())
return gr.update(value="Error getting recommendations")
get_recommendations_btn.click(
fn=on_find_match_click,
inputs=[
living_space,
yard_access,
exercise_time,
exercise_type,
grooming_commitment,
experience_level,
time_availability,
has_children,
children_age,
noise_tolerance
],
outputs=recommendation_output
)
return {
'living_space': living_space,
'exercise_time': exercise_time,
'grooming_commitment': grooming_commitment,
'experience_level': experience_level,
'has_children': has_children,
'noise_tolerance': noise_tolerance,
'get_recommendations_btn': get_recommendations_btn,
'recommendation_output': recommendation_output,
} |