import gradio as gr
from typing import Dict, List, Any, Optional
import traceback
import spaces
from semantic_breed_recommender import get_breed_recommendations_by_description, get_enhanced_recommendations_with_unified_scoring
from natural_language_processor import get_nlp_processor
from recommendation_html_format import format_unified_recommendation_html
def create_description_examples():
"""Create HTML for description examples with dynamic visibility"""
return """
💡 Example Descriptions - Try These Expression Styles:
🏡 Active Lifestyle & Space:
"I live in a large house with a big backyard, and I love hiking and outdoor activities. I don't mind if the dog is noisy, as long as it's active and playful."
🎾 Activity Preferences:
"I want an active medium to large dog for hiking and outdoor activities"
🚶 Balanced Daily Routine:
"I live in a medium-sized house, walk about 30 minutes every day, and I'm okay with a moderately vocal dog. Looking for a balanced companion."
👥 Family Situation:
"Looking for a calm, low-maintenance companion dog for elderly person"
🔍 Tips:
Please describe in English, including living environment, preferred breeds, family situation, activity needs, etc. The more detailed your description, the more accurate the recommendations!
"""
def create_recommendation_tab(
UserPreferences,
get_breed_recommendations,
format_recommendation_html,
history_component
):
"""Create the enhanced breed recommendation tab with natural language support"""
with gr.TabItem("Breed Recommendation"):
with gr.Tabs():
# --------------------------
# Find by Criteria
# --------------------------
with gr.Tab("Find by Criteria"):
gr.HTML("""
Tell us about your lifestyle, and we'll recommend the perfect dog breeds for you!
🔬
The 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
)
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_val):
"""Update children age visibility based on has_children checkbox"""
return gr.update(visible=has_children_val)
has_children.change(
fn=update_children_age_visibility,
inputs=[has_children],
outputs=[children_age]
)
# --------- 條件搜尋---------
def find_breed_matches(
living_space, yard_access, exercise_time, exercise_type,
grooming_commitment, size_preference, experience_level,
time_availability, has_children, children_age, noise_tolerance
):
"""Process criteria-based breed matching and persist history"""
try:
# 1) 建立偏好
user_prefs = UserPreferences(
living_space=living_space,
yard_access=yard_access,
exercise_time=exercise_time,
exercise_type=exercise_type,
grooming_commitment=grooming_commitment,
size_preference=size_preference,
experience_level=experience_level,
time_availability=time_availability,
has_children=has_children,
children_age=children_age if has_children else None,
noise_tolerance=noise_tolerance,
# 其他欄位依原始設計
space_for_play=(living_space != "apartment"),
other_pets=False,
climate="moderate",
health_sensitivity="medium",
barking_acceptance=noise_tolerance
)
# 2) 取得推薦
recommendations = get_breed_recommendations(user_prefs)
print(f"[CRITERIA] generated={len(recommendations) if recommendations else 0}")
if not recommendations:
return format_recommendation_html([], is_description_search=False)
# 3) 準備歷史資料（final_score / overall_score 同步）
history_results = []
for idx, rec in enumerate(recommendations, start=1):
final_score = rec.get("final_score", rec.get("overall_score", 0))
overall_score = final_score # Ensure consistency
history_results.append({
"breed": rec.get("breed", "Unknown"),
"rank": rec.get("rank", idx),
"final_score": final_score,
"overall_score": overall_score,
"base_score": rec.get("base_score", 0),
"bonus_score": rec.get("bonus_score", 0),
"scores": rec.get("scores", {})
})
prefs_dict = user_prefs.__dict__ if hasattr(user_prefs, "__dict__") else user_prefs
# 4) 寫入歷史（criteria）
try:
ok = history_component.save_search(
user_preferences=prefs_dict,
results=history_results,
search_type="criteria",
description=None
)
print(f"[CRITERIA SAVE] ok={ok}, saved={len(history_results)}")
except Exception as e:
print(f"[CRITERIA SAVE][ERROR] {str(e)}")
# 5) 顯示結果
return format_recommendation_html(recommendations, is_description_search=False)
except Exception as e:
print(f"[CRITERIA][ERROR] {str(e)}")
print(traceback.format_exc())
return f"""
⚠️ Error generating recommendations
We encountered an issue while processing your preferences.
Error details: {str(e)}
"""
find_button = gr.Button("🔍 Find My Perfect Match!", elem_id="find-match-btn", size="lg")
criteria_results = gr.HTML(label="Breed Recommendations")
find_button.click(
fn=find_breed_matches,
inputs=[living_space, yard_access, exercise_time, exercise_type,
grooming_commitment, size_preference, experience_level,
time_availability, has_children, children_age, noise_tolerance],
outputs=criteria_results
)
# --------------------------
# Find by Description
# --------------------------
with gr.Tab("Find by Description") as description_tab:
gr.HTML("""
NEW
Describe your needs in natural language, and AI will find the most suitable breeds!
🚀
New Feature: Based on advanced semantic understanding technology, making search more aligned with your real needs!
""")
examples_display = gr.HTML(create_description_examples())
description_input = gr.Textbox(
label="🗣️ Please describe your needs",
placeholder=("Example: I live in an apartment and need a quiet, small dog that's good with children. "
"I prefer Border Collies and Golden Retrievers..."),
lines=4,
max_lines=6,
elem_classes=["description-input"]
)
validation_status = gr.HTML(visible=False)
# Accuracy disclaimer
gr.HTML("""
ⓘ
Accuracy Continuously Improving - Use as Reference Guide
The AI recommendation system is constantly learning and improving. Use these recommendations as a helpful reference for your pet adoption.
""")
def validate_description_input(text):
"""Validate description input"""
try:
nlp = get_nlp_processor()
validation = nlp.validate_input(text)
if validation.get("is_valid", True):
return gr.update(visible=False), True
else:
error_html = f"""
⚠️ {validation.get('error', 'Invalid input')}
{"
".join(f"• {s}" for s in validation.get('suggestions', []))}
"""
return gr.update(value=error_html, visible=True), False
except Exception as e:
# 無 NLP 驗證也可放行
print(f"[DESC][VALIDATE][WARN] {str(e)}")
return gr.update(visible=False), True
@spaces.GPU
def find_breeds_by_description(description_text):
"""Find breeds based on description and persist history"""
try:
if not description_text or not description_text.strip():
return """
Please enter your description to get personalized recommendations
"""
# 驗證（若可用）
try:
nlp = get_nlp_processor()
validation = nlp.validate_input(description_text)
if not validation.get("is_valid", True):
return f"""
⚠️ Input validation failed
{validation.get('error','Invalid input')}
{"".join(f"- {s}
" for s in validation.get('suggestions', []))}
"""
except Exception as e:
print(f"[DESC][VALIDATE][WARN] {str(e)} (skip validation)")
# 取得增強語意推薦
recommendations = get_enhanced_recommendations_with_unified_scoring(
user_description=description_text,
top_k=15
)
print(f"[DESC] generated={len(recommendations) if recommendations else 0}")
if not recommendations:
return """
😔 No matching breeds found
No dog breeds match your specific requirements. Please try:
- Providing a more general description
- Relaxing some specific requirements
- Including different breed preferences
"""
# 準備歷史資料
def _to_float(x, default=0.0):
try:
return float(x)
except Exception:
return default
history_results = []
for i, rec in enumerate(recommendations, start=1):
final_score = _to_float(rec.get("final_score", rec.get("overall_score", 0)))
overall_score = final_score # Ensure consistency between final_score and overall_score
history_results.append({
"breed": str(rec.get("breed", "Unknown")),
"rank": int(rec.get("rank", i)),
"final_score": final_score,
"overall_score": overall_score,
"semantic_score": _to_float(rec.get("semantic_score", 0)),
"comparative_bonus": _to_float(rec.get("comparative_bonus", 0)),
"lifestyle_bonus": _to_float(rec.get("lifestyle_bonus", 0)),
"size": str(rec.get("size", "Unknown")),
"scores": rec.get("scores", {})
})
# 寫入歷史（description）
try:
ok = history_component.save_search(
user_preferences=None,
results=history_results,
search_type="description",
description=description_text
)
print(f"[DESC SAVE] ok={ok}, saved={len(history_results)}")
except Exception as e:
print(f"[DESC SAVE][ERROR] {str(e)}")
# 使用統一HTML格式化器顯示增強推薦結果
html_output = format_unified_recommendation_html(recommendations, is_description_search=True)
return html_output
except RuntimeError as e:
error_msg = str(e)
print(f"[DESC][RUNTIME_ERROR] {error_msg}")
return f"""
🔧 System Configuration Issue
{error_msg.replace(chr(10), '
').replace('•', '•')}
💡 What you can try:
- Restart the application
- Use the "Find by Criteria" tab instead
- Contact support if the issue persists
"""
except ValueError as e:
error_msg = str(e)
print(f"[DESC][VALUE_ERROR] {error_msg}")
return f"""
🔍 No Matching Results
{error_msg}
💡 Suggestions to get better results:
- Try describing your lifestyle more generally
- Mention multiple breed preferences
- Include both what you want and what you can accommodate
- Consider using the "Find by Criteria" tab for structured search
"""
except Exception as e:
error_msg = str(e)
print(f"[DESC][ERROR] {error_msg}")
print(traceback.format_exc())
return f"""
⚠️ Unexpected Error
An unexpected error occurred while processing your description.
Show technical details
{error_msg}
Please try the "Find by Criteria" tab or contact support.
"""
description_input.change(
fn=lambda x: validate_description_input(x)[0],
inputs=[description_input],
outputs=[validation_status]
)
description_button = gr.Button("🤖 Smart Breed Recommendation", elem_id="find-by-description-btn", size="lg")
description_results = gr.HTML(label="AI Breed Recommendations")
description_button.click(
fn=find_breeds_by_description,
inputs=[description_input],
outputs=[description_results]
)
return {
'criteria_results': locals().get('criteria_results'),
'description_results': locals().get('description_results'),
'description_input': locals().get('description_input')
}