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("""

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

""") 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 }