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
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, 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_description_search(description: str):
try:
# 初始化匹配器
matcher = SmartBreedMatcher(dog_data)
breed_recommendations = matcher.match_user_preference(description, top_n=10)
# 從描述中提取用戶偏好
user_prefs = UserPreferences(
living_space="apartment" if any(word in description.lower()
for word in ["apartment", "flat", "condo"]) else "house_small",
exercise_time=120 if any(word in description.lower()
for word in ["active", "exercise", "running", "athletic", "high energy"]) else 60,
grooming_commitment="high" if any(word in description.lower()
for word in ["grooming", "brush", "maintain"]) else "medium",
experience_level="experienced" if any(word in description.lower()
for word in ["experienced", "trained", "professional"]) else "intermediate",
has_children=any(word in description.lower()
for word in ["children", "kids", "family", "child"]),
noise_tolerance="low" if any(word in description.lower()
for word in ["quiet", "peaceful", "silent"]) else "medium",
space_for_play=any(word in description.lower()
for word in ["yard", "garden", "outdoor", "space"]),
other_pets=any(word in description.lower()
for word in ["other pets", "cats", "dogs"]),
climate="moderate",
health_sensitivity="high" if any(word in description.lower()
for word in ["health", "medical", "sensitive"]) else "medium",
barking_acceptance="low" if any(word in description.lower()
for word in ["quiet", "no barking"]) else 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
# 獲取基礎分數
base_score = smart_rec.get('base_score', 0.7)
similarity = smart_rec.get('similarity', 0)
is_preferred = smart_rec.get('is_preferred', False)
bonus_reasons = []
bonus_score = 0
# 1. 尺寸評估
size = breed_info.get('Size', '')
if size in ['Small', 'Tiny']:
if "apartment" in description.lower():
bonus_score += 0.05
bonus_reasons.append("Suitable size for apartment (+5%)")
else:
bonus_score -= 0.25
bonus_reasons.append("Size too small (-25%)")
elif size == 'Medium':
bonus_score += 0.15
bonus_reasons.append("Ideal size (+15%)")
elif size == 'Large':
if "apartment" in description.lower():
bonus_score -= 0.05
bonus_reasons.append("May be too large for apartment (-5%)")
elif size == 'Giant':
bonus_score -= 0.20
bonus_reasons.append("Size too large (-20%)")
# 2. 運動需求評估
exercise_needs = breed_info.get('Exercise_Needs', '')
if any(word in description.lower() for word in ['active', 'energetic', 'running']):
if exercise_needs in ['High', 'Very High']:
bonus_score += 0.20
bonus_reasons.append("Exercise needs match (+20%)")
elif exercise_needs == 'Low':
bonus_score -= 0.15
bonus_reasons.append("Insufficient exercise level (-15%)")
else:
if exercise_needs == 'Moderate':
bonus_score += 0.10
bonus_reasons.append("Moderate exercise needs (+10%)")
# 3. 美容需求評估
grooming = breed_info.get('Grooming_Needs', '')
if user_prefs.grooming_commitment == "high":
if grooming == 'High':
bonus_score += 0.10
bonus_reasons.append("High grooming match (+10%)")
else:
if grooming == 'High':
bonus_score -= 0.15
bonus_reasons.append("High grooming needs (-15%)")
elif grooming == 'Low':
bonus_score += 0.10
bonus_reasons.append("Low grooming needs (+10%)")
# 4. 家庭適應性評估
if user_prefs.has_children:
if breed_info.get('Good_With_Children'):
bonus_score += 0.15
bonus_reasons.append("Excellent with children (+15%)")
temperament = breed_info.get('Temperament', '').lower()
if any(trait in temperament for trait in ['gentle', 'patient', 'friendly']):
bonus_score += 0.05
bonus_reasons.append("Family-friendly temperament (+5%)")
# 5. 噪音評估
if user_prefs.noise_tolerance == "low":
noise_level = breed_noise_info.get(breed_name, {}).get('noise_level', 'Unknown')
if noise_level == 'High':
bonus_score -= 0.10
bonus_reasons.append("High noise level (-10%)")
elif noise_level == 'Low':
bonus_score += 0.10
bonus_reasons.append("Low noise level (+10%)")
# 6. 健康考慮
if user_prefs.health_sensitivity == "high":
health_score = smart_rec.get('health_score', 0.5)
if health_score > 0.8:
bonus_score += 0.10
bonus_reasons.append("Excellent health score (+10%)")
elif health_score < 0.5:
bonus_score -= 0.10
bonus_reasons.append("Health concerns (-10%)")
# 7. 品種偏好獎勵
if is_preferred:
bonus_score += 0.15
bonus_reasons.append("Directly mentioned breed (+15%)")
elif similarity > 0.8:
bonus_score += 0.10
bonus_reasons.append("Very similar to preferred breed (+10%)")
# 計算最終分數
final_score = min(0.95, base_score + bonus_score)
space_score = _calculate_space_compatibility(
breed_info.get('Size', 'Medium'),
user_prefs.living_space
)
exercise_score = _calculate_exercise_compatibility(
breed_info.get('Exercise_Needs', 'Moderate'),
user_prefs.exercise_time
)
grooming_score = _calculate_grooming_compatibility(
breed_info.get('Grooming_Needs', 'Moderate'),
user_prefs.grooming_commitment
)
experience_score = _calculate_experience_compatibility(
breed_info.get('Care_Level', 'Moderate'),
user_prefs.experience_level
)
scores = {
'overall': final_score,
'space': space_score,
'exercise': exercise_score,
'grooming': grooming_score,
'experience': experience_score,
'noise': smart_rec.get('scores', {}).get('noise', 0.0),
'health': smart_rec.get('health_score', 0.5),
'temperament': smart_rec.get('scores', {}).get('temperament', 0.0)
}
final_recommendations.append({
'rank': 0,
'breed': breed_name,
'scores': scores,
'base_score': round(base_score, 4),
'bonus_score': round(bonus_score, 4),
'final_score': round(final_score, 4),
'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
# 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]]
history_component.save_search(
user_preferences=None,
results=history_results,
search_type="description",
description=description
)
result = format_recommendation_html(final_recommendations, is_description_search=True)
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 _calculate_space_compatibility(size: str, living_space: str) -> float:
"""住宿空間適應性評分"""
if living_space == "apartment":
scores = {
'Tiny': 0.6, # 公寓可以，但不是最佳
'Small': 0.8, # 公寓較好
'Medium': 1.0, # 最佳選擇
'Medium-Large': 0.6, # 可能有點大
'Large': 0.4, # 太大了
'Giant': 0.2 # 不建議
}
else: # house
scores = {
'Tiny': 0.4, # 房子太大了
'Small': 0.6, # 可以但不是最佳
'Medium': 0.8, # 不錯的選擇
'Medium-Large': 1.0, # 最佳選擇
'Large': 0.9, # 也很好
'Giant': 0.7 # 可以考慮
}
return scores.get(size, 0.5)
def _calculate_exercise_compatibility(exercise_needs: str, exercise_time: int) -> float:
"""運動需求相容性評分"""
# 轉換運動時間到評分標準
if exercise_time >= 120: # 高運動量
scores = {
'Very High': 1.0,
'High': 0.8,
'Moderate': 0.5,
'Low': 0.2
}
elif exercise_time >= 60: # 中等運動量
scores = {
'Very High': 0.5,
'High': 0.7,
'Moderate': 1.0,
'Low': 0.8
}
else: # 低運動量
scores = {
'Very High': 0.2,
'High': 0.4,
'Moderate': 0.7,
'Low': 1.0
}
return scores.get(exercise_needs, 0.5)
def _calculate_grooming_compatibility(grooming_needs: str, grooming_commitment: str) -> float:
"""美容需求相容性評分"""
if grooming_commitment == "high":
scores = {
'High': 1.0,
'Moderate': 0.8,
'Low': 0.5
}
elif grooming_commitment == "medium":
scores = {
'High': 0.6,
'Moderate': 1.0,
'Low': 0.8
}
else: # low
scores = {
'High': 0.3,
'Moderate': 0.6,
'Low': 1.0
}
return scores.get(grooming_needs, 0.5)
def _calculate_experience_compatibility(care_level: str, experience_level: str) -> float:
if experience_level == "experienced":
care_scores = {
'High': 1.0,
'Moderate': 0.8,
'Low': 0.6
}
elif experience_level == "intermediate":
care_scores = {
'High': 0.6,
'Moderate': 1.0,
'Low': 0.8
}
else: # beginner
care_scores = {
'High': 0.3,
'Moderate': 0.7,
'Low': 1.0
}
return care_scores.get(care_level, 0.7)
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
}