breed_recommendation.py

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

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"
                        )

                        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():
                        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")
                recommendation_output = gr.HTML(
                    label="Breed Recommendations",
                    visible=True,  # 確保可見性
                    elem_id="recommendation-output"  # 添加唯一ID以便追蹤
                )

        # 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],
        #             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],
        #             space_for_play=True if args[0] != "apartment" else False,
        #             other_pets=False,
        #             climate="moderate",
        #             health_sensitivity="medium",
        #             barking_acceptance=args[9]
        #         )

        #         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"

        def on_find_match_click(*args):
            try:
                # 時間處理
                from datetime import datetime
                import pytz
                
                user_prefs = UserPreferences(
                    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],
                    space_for_play=True if args[0] != "apartment" else False,
                    other_pets=False,
                    climate="moderate",
                    health_sensitivity="medium",
                    barking_acceptance=args[9]
                )
        
                recommendations = get_breed_recommendations(user_prefs, top_n=10)
        
                # 確保 recommendations 不為空
                if not recommendations:
                    return "No matching breeds found. Please adjust your criteria."
        
                # 創建標準化的結果列表
                formatted_recommendations = []
                for i, rec in enumerate(recommendations, 1):
                    formatted_rec = {
                        'breed': rec['breed'],
                        'rank': i,
                        'final_score': rec['final_score'],  # 直接使用，因為這是必需的
                        'overall_score': rec['final_score'],  # 複製 final_score
                        'base_score': rec.get('base_score', 0),
                        'bonus_score': rec.get('bonus_score', 0),
                        'scores': rec.get('scores', {})
                    }
                    formatted_recommendations.append(formatted_rec)
                
                # 使用相同的格式化數據來保存歷史記錄和生成 HTML
                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]
                    },
                    results=formatted_recommendations  # 使用格式化後的結果
                )
                
                # 使用相同的格式化數據生成 HTML
                return format_recommendation_html(formatted_recommendations, is_description_search=False)
                
            except Exception as e:
                print(f"Error details: {str(e)}")
                print(traceback.format_exc())
                return f"Error getting recommendations: {str(e)}"

        get_recommendations_btn.click(
            fn=on_find_match_click,
            inputs=[
                living_space,
                yard_access,        
                exercise_time,
                exercise_type,      
                grooming_commitment,
                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,
    }