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 smart_breed_matcher import SmartBreedMatcher
from description_search_ui import create_description_search_tab
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"
)
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")
with gr.Tab("Find by Description"):
description_input, description_search_btn, description_output, loading_msg = create_description_search_tab()
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"
def on_description_search(description: str):
try:
matcher = SmartBreedMatcher(dog_data)
breed_recommendations = matcher.match_user_preference(description, top_n=10)
print("Creating user preferences...")
user_prefs = UserPreferences(
living_space="apartment" if "apartment" in description.lower() else "house_small",
exercise_time=60,
grooming_commitment="medium",
experience_level="intermediate",
has_children="children" in description.lower() or "kids" in description.lower(),
noise_tolerance="medium",
space_for_play=True if "yard" in description.lower() or "garden" in description.lower() else False,
other_pets=False,
climate="moderate",
health_sensitivity="medium",
barking_acceptance=None
)
final_recommendations = []
for smart_rec in breed_recommendations:
breed_name = smart_rec['breed']
breed_info = get_dog_description(breed_name)
if not isinstance(breed_info, dict):
continue
# 計算基礎相容性分數
compatibility_scores = calculate_compatibility_score(breed_info, user_prefs)
bonus_reasons = []
bonus_score = 0
is_preferred = smart_rec.get('is_preferred', False)
similarity = smart_rec.get('similarity', 0)
# 用戶直接提到的品種
if is_preferred:
bonus_score = 0.15 # 15% bonus
bonus_reasons.append("Directly mentioned breed (+15%)")
# 高相似度品種
elif similarity > 0.8:
bonus_score = 0.10 # 10% bonus
bonus_reasons.append("Very similar to preferred breed (+10%)")
# 中等相似度品種
elif similarity > 0.6:
bonus_score = 0.05 # 5% bonus
bonus_reasons.append("Similar to preferred breed (+5%)")
# 基於品種特性的額外加分
temperament = breed_info.get('Temperament', '').lower()
if any(trait in temperament for trait in ['friendly', 'gentle', 'affectionate']):
bonus_score += 0.02 # 2% bonus
bonus_reasons.append("Positive temperament traits (+2%)")
if breed_info.get('Good with Children') == 'Yes' and user_prefs.has_children:
bonus_score += 0.03 # 3% bonus
bonus_reasons.append("Excellent with children (+3%)")
# 基礎分數和最終分數計算
base_score = compatibility_scores.get('overall', 0.7)
final_score = min(0.95, base_score + bonus_score) # 確保不超過95%
final_recommendations.append({
'rank': 0,
'breed': breed_name,
'base_score': round(base_score, 4),
'bonus_score': round(bonus_score, 4),
'final_score': round(final_score, 4),
'scores': compatibility_scores,
'match_reason': ' • '.join(bonus_reasons) if bonus_reasons else "Standard match",
'info': breed_info,
'noise_info': breed_noise_info.get(breed_name, {}),
'health_info': breed_health_info.get(breed_name, {})
})
# 根據最終分數排序
final_recommendations.sort(key=lambda x: (-x['final_score'], x['breed']))
# 更新排名
for i, rec in enumerate(final_recommendations, 1):
rec['rank'] = i
# 新增：保存到歷史記錄
history_results = [{
'breed': rec['breed'],
'rank': rec['rank'],
'final_score': rec['final_score']
} for rec in final_recommendations[:10]] # 只保存前10名
history_component.save_search(
user_preferences=None, # description搜尋不需要preferences
results=history_results,
search_type="description",
description=description # 用戶輸入的描述文字
)
# 驗證排序
print("\nFinal Rankings:")
for rec in final_recommendations:
print(f"#{rec['rank']} {rec['breed']}")
print(f"Base Score: {rec['base_score']:.4f}")
print(f"Bonus Score: {rec['bonus_score']:.4f}")
print(f"Final Score: {rec['final_score']:.4f}")
print(f"Reason: {rec['match_reason']}\n")
result = format_recommendation_html(final_recommendations)
return [gr.update(value=result), gr.update(visible=False)]
except Exception as e:
error_msg = f"Error processing your description. Details: {str(e)}"
return [gr.update(value=error_msg), gr.update(visible=False)]
def show_loading():
return [gr.update(value=""), gr.update(visible=True)]
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
)
description_search_btn.click(
fn=show_loading, # 先顯示加載消息
outputs=[description_output, loading_msg]
).then( # 然後執行搜索
fn=on_description_search,
inputs=[description_input],
outputs=[description_output, loading_msg]
)
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,
'description_input': description_input,
'description_search_btn': description_search_btn,
'description_output': description_output
}