import sqlite3 import gradio as gr import asyncio from typing import Generator 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 filter_breed_matches(user_prefs: UserPreferences, top_n: int = 10) -> List[Dict]: """ 根據使用者偏好篩選並推薦狗狗品種。 Parameters: user_prefs: 使用者偏好設定 top_n: 要返回的推薦數量 Returns: List[Dict]: 排序後的推薦品種列表 """ all_breeds = [] for breed_info in breed_database: score = calculate_compatibility_score(breed_info, user_prefs) if score is not None: # 只添加未被過濾的品種 all_breeds.append({ 'breed': breed_info['Breed'], 'final_score': score['overall'], 'base_score': score.get('base_score', 0), 'bonus_score': score.get('bonus_score', 0), 'size': breed_info['Size'], 'scores': score }) # 根據體型偏好過濾 if user_prefs.size_preference != "no_preference": filtered_breeds = [b for b in all_breeds if b['size'].lower() == user_prefs.size_preference.lower()] # 如果符合體型的品種太少,調整返回數量 if len(filtered_breeds) < 5: # 設定最少要有5種品種 top_n = len(filtered_breeds) else: filtered_breeds = all_breeds # 為每個品種添加排名 sorted_breeds = sorted(filtered_breeds, key=lambda x: x['final_score'], reverse=True) for i, breed in enumerate(sorted_breeds, 1): breed['rank'] = i return sorted_breeds[:top_n] 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("""
BETA

Tell us about your lifestyle, and we'll recommend the perfect dog breeds for you!

🔬 Beta Feature: Our matching algorithm is continuously improving. Results are for reference only.
""") 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(): size_preference = gr.Radio( choices=["no_preference", "small", "medium", "large", "giant"], label="Preference Dog Size", info="Select your preferred dog size - this will strongly filter the recommendations", value = "no_preference" ) 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", visible=True, # 確保可見性 elem_id="recommendation-output" ) 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], size_preference=args[5], experience_level=args[6], time_availability=args[7], has_children=args[8], children_age=args[9] if args[8] else None, noise_tolerance=args[10], space_for_play=True if args[0] != "apartment" else False, other_pets=False, climate="moderate", health_sensitivity="medium", barking_acceptance=args[10] ) 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" get_recommendations_btn.click( fn=on_find_match_click, inputs=[ living_space, yard_access, exercise_time, exercise_type, grooming_commitment, size_preference, 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, }