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Running
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Zero
File size: 5,394 Bytes
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import gradio as gr
def create_recommendation_tab(UserPreferences, get_breed_recommendations, format_recommendation_html, history_component):
"""创建品种推荐标签页
Args:
UserPreferences: 用户偏好类
get_breed_recommendations: 获取品种推荐的函数
format_recommendation_html: 格式化推荐结果的函数
history_component: 历史记录组件
"""
with gr.TabItem("Breed Recommendation"):
gr.HTML("<p style='text-align: center;'>Tell us about your lifestyle, and we'll recommend the perfect dog breeds for you!</p>")
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"
)
exercise_time = gr.Slider(
minimum=0,
maximum=180,
value=60,
label="Daily exercise time (minutes)",
info="Consider walks, play time, and training"
)
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"
)
has_children = gr.Checkbox(
label="Have children at home",
info="Helps recommend child-friendly breeds"
)
noise_tolerance = gr.Radio(
choices=["low", "medium", "high"],
label="Noise tolerance level",
info="Some breeds are more vocal than others",
value="medium"
)
get_recommendations_btn = gr.Button("Find My Perfect Match! 🔍", variant="primary")
recommendation_output = gr.HTML(label="Breed Recommendations")
def on_find_match_click(*args):
try:
user_prefs = UserPreferences(
living_space=args[0],
exercise_time=args[1],
grooming_commitment=args[2],
experience_level=args[3],
has_children=args[4],
noise_tolerance=args[5],
space_for_play=True if args[0] != "apartment" else False,
other_pets=False,
climate="moderate",
health_sensitivity="medium", # 新增: 默認中等敏感度
barking_acceptance=args[5] # 使用 noise_tolerance 作為 barking_acceptance
)
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]
# 保存到歷史記錄，也需要更新保存的偏好設定
history_component.save_search(
user_preferences={
'living_space': args[0],
'exercise_time': args[1],
'grooming_commitment': args[2],
'experience_level': args[3],
'has_children': args[4],
'noise_tolerance': args[5],
'health_sensitivity': "medium",
'barking_acceptance': args[5]
},
results=history_results
)
return format_recommendation_html(recommendations)
except Exception as e:
print(f"Error in find match: {str(e)}")
import traceback
print(traceback.format_exc())
return "Error getting recommendations"
get_recommendations_btn.click(
fn=on_find_match_click,
inputs=[
living_space,
exercise_time,
grooming_commitment,
experience_level,
has_children,
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
}
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