app.py

# app.py

import os
import gradio as gr
from PIL import Image
import torch
import numpy as np
import cv2
from transformers import (
    CLIPProcessor, CLIPModel,
    AutoProcessor
)
import time

# —————————————————————————————
# CONFIG: set your HF token here or via env var HF_TOKEN
HF_TOKEN = os.getenv("HF_TOKEN")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 1. CLIP for breed, age, basic health
clip_model = CLIPModel.from_pretrained(
    "openai/clip-vit-base-patch16",
    token=HF_TOKEN
).to(device)
clip_processor = CLIPProcessor.from_pretrained(
    "openai/clip-vit-base-patch16",
    token=HF_TOKEN
)

# 2. Alternative medical analysis model (public, no gating issues)
try:
    medical_processor = AutoProcessor.from_pretrained(
        "microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224",
        token=HF_TOKEN
    )
    medical_model = CLIPModel.from_pretrained(
        "microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224",
        token=HF_TOKEN
    ).to(device)
    MEDICAL_MODEL_AVAILABLE = True
except:
    medical_processor = clip_processor
    medical_model = clip_model
    MEDICAL_MODEL_AVAILABLE = False

# 3. Stanford Dogs & lifespans (expanded list)
STANFORD_BREEDS = [
    "afghan hound", "african hunting dog", "airedale", "american staffordshire terrier",
    "appenzeller", "australian terrier", "basenji", "basset", "beagle", 
    "bedlington terrier", "bernese mountain dog", "black-and-tan coonhound",
    "blenheim spaniel", "bloodhound", "bluetick", "border collie", "border terrier",
    "borzoi", "boston bull", "bouvier des flandres", "boxer", "brabancon griffon",
    "briard", "brittany spaniel", "bull mastiff", "cairn", "cardigan",
    "chesapeake bay retriever", "chihuahua", "chow", "clumber", "cocker spaniel",
    "collie", "curly-coated retriever", "dandie dinmont", "dhole", "dingo",
    "doberman", "english foxhound", "english setter", "english springer",
    "entlebucher", "eskimo dog", "flat-coated retriever", "french bulldog",
    "german shepherd", "german short-haired pointer", "giant schnauzer",
    "golden retriever", "gordon setter", "great dane", "great pyrenees",
    "greater swiss mountain dog", "groenendael", "ibizan hound", "irish setter",
    "irish terrier", "irish water spaniel", "irish wolfhound", "italian greyhound",
    "japanese spaniel", "keeshond", "kelpie", "kerry blue terrier", "komondor",
    "kuvasz", "labrador retriever", "lakeland terrier", "leonberg", "lhasa",
    "malamute", "malinois", "maltese dog", "mexican hairless", "miniature pinscher",
    "miniature poodle", "miniature schnauzer", "newfoundland", "norfolk terrier",
    "norwegian elkhound", "norwich terrier", "old english sheepdog", "otterhound",
    "papillon", "pekinese", "pembroke", "pomeranian", "pug", "redbone",
    "rhodesian ridgeback", "rottweiler", "saint bernard", "saluki", "samoyed",
    "schipperke", "scotch terrier", "scottish deerhound", "sealyham terrier",
    "shetland sheepdog", "shih tzu", "siberian husky", "silky terrier",
    "soft-coated wheaten terrier", "staffordshire bullterrier", "standard poodle",
    "standard schnauzer", "sussex spaniel", "tibetan mastiff", "tibetan terrier",
    "toy poodle", "toy terrier", "vizsla", "walker hound", "weimaraner",
    "welsh springer spaniel", "west highland white terrier", "whippet",
    "wire-haired fox terrier", "yorkshire terrier"
]

BREED_LIFESPAN = {
    "afghan hound": 11.1, "african hunting dog": 10.5, "airedale": 11.5,
    "american staffordshire terrier": 12.5, "appenzeller": 13.0, "australian terrier": 13.5,
    "basenji": 12.1, "basset": 12.5, "beagle": 12.5, "bedlington terrier": 13.7,
    "bernese mountain dog": 10.1, "black-and-tan coonhound": 10.8, "blenheim spaniel": 13.3,
    "bloodhound": 9.3, "bluetick": 11.0, "border collie": 13.1, "border terrier": 14.2,
    "borzoi": 12.0, "boston bull": 11.8, "bouvier des flandres": 11.3, "boxer": 11.3,
    "brabancon griffon": 13.0, "briard": 12.6, "brittany spaniel": 13.5,
    "bull mastiff": 10.2, "cairn": 14.0, "cardigan": 13.2, "chesapeake bay retriever": 11.6,
    "chihuahua": 11.8, "chow": 12.1, "clumber": 12.3, "cocker spaniel": 13.3,
    "collie": 13.3, "curly-coated retriever": 12.2, "dandie dinmont": 12.8,
    "dhole": 10.0, "dingo": 10.0, "doberman": 11.2, "english foxhound": 13.0,
    "english setter": 13.1, "english springer": 13.5, "entlebucher": 13.0,
    "eskimo dog": 11.3, "flat-coated retriever": 11.7, "french bulldog": 9.8,
    "german shepherd": 11.3, "german short-haired pointer": 13.4, "giant schnauzer": 12.1,
    "golden retriever": 13.2, "gordon setter": 12.4, "great dane": 10.6,
    "great pyrenees": 10.9, "greater swiss mountain dog": 10.9, "groenendael": 12.0,
    "ibizan hound": 13.3, "irish setter": 12.9, "irish terrier": 13.5,
    "irish water spaniel": 10.8, "irish wolfhound": 9.9, "italian greyhound": 14.0,
    "japanese spaniel": 13.3, "keeshond": 12.3, "kelpie": 12.0, "kerry blue terrier": 12.4,
    "komondor": 10.5, "kuvasz": 10.5, "labrador retriever": 13.1, "lakeland terrier": 14.2,
    "leonberg": 10.0, "lhasa": 14.0, "malamute": 11.3, "malinois": 12.0,
    "maltese dog": 13.1, "mexican hairless": 13.0, "miniature pinscher": 13.7,
    "miniature poodle": 14.0, "miniature schnauzer": 13.3, "newfoundland": 11.0,
    "norfolk terrier": 13.5, "norwegian elkhound": 13.0, "norwich terrier": 14.0,
    "old english sheepdog": 12.1, "otterhound": 12.0, "papillon": 14.5,
    "pekinese": 13.3, "pembroke": 13.2, "pomeranian": 12.2, "pug": 11.6,
    "redbone": 12.0, "rhodesian ridgeback": 12.0, "rottweiler": 10.6,
    "saint bernard": 9.3, "saluki": 13.3, "samoyed": 13.1, "schipperke": 14.2,
    "scotch terrier": 12.7, "scottish deerhound": 10.5, "sealyham terrier": 13.1,
    "shetland sheepdog": 13.4, "shih tzu": 12.8, "siberian husky": 11.9,
    "silky terrier": 13.3, "soft-coated wheaten terrier": 13.7, "staffordshire bullterrier": 12.0,
    "standard poodle": 14.0, "standard schnauzer": 13.0, "sussex spaniel": 13.5,
    "tibetan mastiff": 13.3, "tibetan terrier": 13.8, "toy poodle": 14.0,
    "toy terrier": 13.0, "vizsla": 13.5, "walker hound": 12.0, "weimaraner": 12.8,
    "welsh springer spaniel": 14.0, "west highland white terrier": 13.4, "whippet": 13.4,
    "wire-haired fox terrier": 13.5, "yorkshire terrier": 13.3
}

# 4. VetMetrica HRQOL Framework with dropdown options
HRQOL_QUESTIONNAIRE = {
    "vitality": {
        "title": "🔋 Vitality & Energy Assessment",
        "description": "Evaluate your dog's energy levels and enthusiasm for activities",
        "questions": [
            {
                "id": "vitality_energy",
                "text": "How would you rate your dog's energy level over the past week?",
                "options": [
                    "Excellent - Very energetic, eager for activities",
                    "Very Good - Generally energetic with occasional rest",
                    "Good - Moderate energy, participates willingly",
                    "Fair - Lower energy, needs encouragement",
                    "Poor - Very low energy, reluctant to participate"
                ]
            },
            {
                "id": "vitality_play", 
                "text": "How often does your dog seek out play or interaction?",
                "options": [
                    "Always seeks play/interaction",
                    "Often seeks play/interaction",
                    "Sometimes seeks play/interaction",
                    "Rarely seeks play/interaction",
                    "Never seeks play/interaction"
                ]
            },
            {
                "id": "vitality_response",
                "text": "How quickly does your dog respond to exciting stimuli (treats, walks, visitors)?",
                "options": [
                    "Immediate enthusiastic response",
                    "Quick positive response",
                    "Moderate response time",
                    "Slow or delayed response",
                    "No response or negative reaction"
                ]
            }
        ],
        "weight": 0.25
    },
    "comfort": {
        "title": "😌 Comfort & Pain Management",
        "description": "Assess signs of discomfort, pain, or mobility issues",
        "questions": [
            {
                "id": "comfort_activities",
                "text": "How comfortable does your dog appear during normal activities?",
                "options": [
                    "Completely comfortable during all activities",
                    "Mostly comfortable with minor adjustments",
                    "Some discomfort during certain activities",
                    "Frequently uncomfortable, avoids some activities",
                    "Severe discomfort, avoids most activities"
                ]
            },
            {
                "id": "comfort_pain_frequency",
                "text": "How often do you notice signs of pain or discomfort?",
                "options": [
                    "Never shows pain signs",
                    "Rarely shows pain signs (< 1 day/week)",
                    "Sometimes shows pain signs (2-3 days/week)",
                    "Often shows pain signs (4-5 days/week)",
                    "Always shows pain signs (daily)"
                ]
            },
            {
                "id": "comfort_impact",
                "text": "How does your dog's comfort level affect daily activities?",
                "options": [
                    "No impact on daily activities",
                    "Minimal impact on daily activities",
                    "Moderate impact, some activities modified",
                    "Significant impact, many activities avoided",
                    "Severe impact, most activities impossible"
                ]
            }
        ],
        "weight": 0.25
    },
    "emotional_wellbeing": {
        "title": "😊 Emotional Wellbeing",
        "description": "Evaluate mood, anxiety levels, and social engagement",
        "questions": [
            {
                "id": "emotion_mood",
                "text": "How would you describe your dog's overall mood?",
                "options": [
                    "Very positive - happy, content, enthusiastic",
                    "Mostly positive - generally cheerful",
                    "Neutral - neither particularly happy nor sad",
                    "Mostly negative - seems subdued or withdrawn",
                    "Very negative - appears depressed or distressed"
                ]
            },
            {
                "id": "emotion_anxiety",
                "text": "How often does your dog show signs of anxiety or stress?",
                "options": [
                    "Never shows anxiety/stress",
                    "Rarely shows anxiety/stress",
                    "Sometimes shows anxiety/stress",
                    "Often shows anxiety/stress",
                    "Constantly shows anxiety/stress"
                ]
            },
            {
                "id": "emotion_engagement",
                "text": "How engaged is your dog with family activities?",
                "options": [
                    "Highly engaged, initiates family interactions",
                    "Well engaged, participates enthusiastically",
                    "Moderately engaged, participates when invited",
                    "Minimally engaged, needs encouragement",
                    "Not engaged, avoids family activities"
                ]
            }
        ],
        "weight": 0.25
    },
    "alertness": {
        "title": "🧠 Alertness & Cognition",
        "description": "Assess cognitive function, awareness, and responsiveness",
        "questions": [
            {
                "id": "alert_awareness",
                "text": "How alert and aware does your dog seem?",
                "options": [
                    "Highly alert, notices everything immediately",
                    "Alert, notices most things quickly",
                    "Moderately alert, notices things with some delay",
                    "Slightly alert, slow to notice surroundings",
                    "Not alert, seems confused or disoriented"
                ]
            },
            {
                "id": "alert_commands",
                "text": "How well does your dog respond to commands or their name?",
                "options": [
                    "Responds immediately to name/commands",
                    "Usually responds quickly to name/commands",
                    "Sometimes responds, may need repetition",
                    "Often doesn't respond, needs multiple attempts",
                    "Rarely or never responds to name/commands"
                ]
            },
            {
                "id": "alert_focus",
                "text": "How focused is your dog during training or play?",
                "options": [
                    "Highly focused, maintains attention easily",
                    "Good focus, occasional distraction",
                    "Moderate focus, some difficulty concentrating",
                    "Poor focus, easily distracted",
                    "No focus, cannot maintain attention"
                ]
            }
        ],
        "weight": 0.25
    }
}

def predict_biological_age(img: Image.Image, breed: str) -> int:
    avg = BREED_LIFESPAN.get(breed.lower(), 12)
    prompts = [f"a {age}-year-old {breed}" for age in range(1, int(avg*2)+1)]
    inputs = clip_processor(text=prompts, images=img, return_tensors="pt", padding=True).to(device)
    with torch.no_grad():
        probs = clip_model(**inputs).logits_per_image.softmax(1)[0].cpu().numpy()
    return int(np.argmax(probs)+1)

def analyze_medical_image(img: Image.Image):
    health_conditions = [
        "healthy normal dog",
        "dog with visible health issues",
        "dog showing signs of illness",
        "dog with poor body condition",
        "dog with excellent health"
    ]
    
    if MEDICAL_MODEL_AVAILABLE:
        inputs = medical_processor(text=health_conditions, images=img, return_tensors="pt", padding=True).to(device)
        with torch.no_grad():
            logits = medical_model(**inputs).logits_per_image.softmax(-1)[0].cpu().numpy()
    else:
        inputs = clip_processor(text=health_conditions, images=img, return_tensors="pt", padding=True).to(device)
        with torch.no_grad():
            logits = clip_model(**inputs).logits_per_image.softmax(-1)[0].cpu().numpy()
    
    idx = int(np.argmax(logits))
    label = health_conditions[idx]
    conf = float(logits[idx])
    return label, conf

def classify_breed_and_health(img: Image.Image, override=None):
    inp = clip_processor(images=img, return_tensors="pt").to(device)
    with torch.no_grad():
        feats = clip_model.get_image_features(**inp)
    
    text_prompts = [f"a photo of a {b}" for b in STANFORD_BREEDS]
    ti = clip_processor(text=text_prompts, return_tensors="pt", padding=True).to(device)
    with torch.no_grad():
        tf = clip_model.get_text_features(**ti)
    sims = (feats @ tf.T).softmax(-1)[0].cpu().numpy()
    idx = int(np.argmax(sims))
    breed = override or STANFORD_BREEDS[idx]
    breed_conf = float(sims[idx])
    
    aspects = {
        "Coat Quality": ("shiny healthy coat","dull patchy fur"),
        "Eye Clarity": ("bright clear eyes","cloudy milky eyes"),
        "Body Condition": ("ideal muscle tone","visible ribs or bones"),
        "Dental Health": ("clean white teeth","yellow stained teeth")
    }
    health = {}
    for name,(p,n) in aspects.items():
        ti = clip_processor(text=[p,n], return_tensors="pt", padding=True).to(device)
        with torch.no_grad():
            tf2 = clip_model.get_text_features(**ti)
        sim2 = (feats @ tf2.T).softmax(-1)[0].cpu().numpy()
        choice = p if sim2[0]>sim2[1] else n
        health[name] = {"assessment":choice,"confidence":float(max(sim2))}
    return breed, breed_conf, health

def analyze_video_gait(video_path):
    if not video_path:
        return None
    
    try:
        cap = cv2.VideoCapture(video_path)
        fps = cap.get(cv2.CAP_PROP_FPS) or 24
        total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        if total == 0:
            cap.release()
            return None
        
        indices = np.linspace(0, total-1, min(15, total), dtype=int)
        health_scores = []
        movement_scores = []
        vitality_scores = []
        
        for i in indices:
            cap.set(cv2.CAP_PROP_POS_FRAMES, i)
            ret, frame = cap.read()
            if not ret: 
                continue
            img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            
            # Health assessment
            _, health_conf = analyze_medical_image(img)
            health_scores.append(health_conf)
            
            # Movement assessment
            movement_prompts = ["dog moving normally", "dog limping or showing pain", "dog moving stiffly"]
            inputs = clip_processor(text=movement_prompts, images=img, return_tensors="pt", padding=True).to(device)
            with torch.no_grad():
                movement_logits = clip_model(**inputs).logits_per_image.softmax(-1)[0].cpu().numpy()
            movement_scores.append(float(movement_logits[0]))
            
            # Vitality assessment
            vitality_prompts = ["energetic active dog", "lethargic tired dog", "alert playful dog"]
            inputs = clip_processor(text=vitality_prompts, images=img, return_tensors="pt", padding=True).to(device)
            with torch.no_grad():
                vitality_logits = clip_model(**inputs).logits_per_image.softmax(-1)[0].cpu().numpy()
            vitality_scores.append(float(vitality_logits[0] + vitality_logits[2]))
        
        cap.release()
        
        if not health_scores:
            return None
        
        return {
            "duration_sec": round(total/fps, 1),
            "mobility_score": float(np.mean(movement_scores)) * 100,
            "comfort_score": float(np.mean(health_scores)) * 100,
            "vitality_score": float(np.mean(vitality_scores)) * 100,
            "frames_analyzed": len(health_scores),
            "mobility_assessment": "Normal gait pattern" if np.mean(movement_scores) > 0.6 else "Mobility concerns detected",
            "comfort_assessment": "No obvious discomfort" if np.mean(health_scores) > 0.7 else "Possible discomfort signs",
            "vitality_assessment": "Good energy level" if np.mean(vitality_scores) > 0.6 else "Low energy observed"
        }
    except Exception as e:
        return None

def score_from_response(response, score_mapping):
    """Extract numeric score from text response"""
    if not response:
        return 50
    for key, value in score_mapping.items():
        if key.lower() in response.lower():
            return value
    return 50

def calculate_hrqol_scores(hrqol_responses):
    """Convert VetMetrica-style responses to 0-100 domain scores"""
    
    score_mapping = {
        "excellent": 100, "very good": 80, "good": 60, "fair": 40, "poor": 20,
        "always": 100, "often": 80, "sometimes": 60, "rarely": 40, "never": 20,
        "immediate": 100, "quick": 80, "moderate": 60, "slow": 40, "no response": 20,
        "completely": 100, "mostly": 80, "some": 60, "frequently": 40, "severe": 20,
        "very positive": 100, "mostly positive": 80, "neutral": 60, "mostly negative": 40, "very negative": 20,
        "highly": 100, "well": 80, "moderately": 60, "minimally": 40, "not": 20
    }
    
    domain_scores = {}
    
    # Vitality Domain
    vitality_scores = [
        score_from_response(hrqol_responses.get("vitality_energy", ""), score_mapping),
        score_from_response(hrqol_responses.get("vitality_play", ""), score_mapping),
        score_from_response(hrqol_responses.get("vitality_response", ""), score_mapping)
    ]
    domain_scores["vitality"] = np.mean(vitality_scores)
    
    # Comfort Domain (invert pain frequency)
    comfort_scores = [
        score_from_response(hrqol_responses.get("comfort_activities", ""), score_mapping),
        100 - score_from_response(hrqol_responses.get("comfort_pain_frequency", ""), score_mapping),
        score_from_response(hrqol_responses.get("comfort_impact", ""), score_mapping)
    ]
    domain_scores["comfort"] = max(0, np.mean(comfort_scores))
    
    # Emotional Wellbeing Domain (invert anxiety)
    emotion_scores = [
        score_from_response(hrqol_responses.get("emotion_mood", ""), score_mapping),
        100 - score_from_response(hrqol_responses.get("emotion_anxiety", ""), score_mapping),
        score_from_response(hrqol_responses.get("emotion_engagement", ""), score_mapping)
    ]
    domain_scores["emotional_wellbeing"] = max(0, np.mean(emotion_scores))
    
    # Alertness Domain
    alertness_scores = [
        score_from_response(hrqol_responses.get("alert_awareness", ""), score_mapping),
        score_from_response(hrqol_responses.get("alert_commands", ""), score_mapping),
        score_from_response(hrqol_responses.get("alert_focus", ""), score_mapping)
    ]
    domain_scores["alertness"] = np.mean(alertness_scores)
    
    return domain_scores

def get_score_color(score):
    """Return background and text color based on score for better visibility"""
    if score >= 80:
        return {"bg": "#4CAF50", "text": "#FFFFFF"}  # Green background, white text
    elif score >= 60:
        return {"bg": "#FFC107", "text": "#000000"}  # Yellow background, black text
    elif score >= 40:
        return {"bg": "#FF9800", "text": "#FFFFFF"}  # Orange background, white text
    else:
        return {"bg": "#F44336", "text": "#FFFFFF"}  # Red background, white text

def get_healthspan_grade(score):
    if score >= 85:
        return "Excellent (A+)"
    elif score >= 75:
        return "Very Good (A)"
    elif score >= 65:
        return "Good (B)"
    elif score >= 55:
        return "Fair (C)"
    elif score >= 45:
        return "Poor (D)"
    else:
        return "Critical (F)"

def show_loading():
    """Display loading animation"""
    return """
    <div style="text-align: center; padding: 40px;">
        <div style="display: inline-block; width: 40px; height: 40px; border: 4px solid #f3f3f3; border-top: 4px solid #667eea; border-radius: 50%; animation: spin 1s linear infinite;"></div>
        <style>
        @keyframes spin {
          0% { transform: rotate(0deg); }
          100% { transform: rotate(360deg); }
        }
        </style>
        <h3 style="color: #667eea; margin-top: 20px;">🔬 Analyzing Your Dog's Health...</h3>
        <p style="color: #666;">Please wait while we process the image/video and questionnaire data.</p>
        <div style="background: #f0f0f0; border-radius: 20px; padding: 10px; margin: 20px auto; width: 300px;">
            <div style="background: linear-gradient(90deg, #667eea, #764ba2); height: 6px; border-radius: 10px; width: 0%; animation: progress 3s ease-in-out infinite;"></div>
        </div>
        <style>
        @keyframes progress {
          0% { width: 0%; }
          50% { width: 80%; }
          100% { width: 100%; }
        }
        </style>
    </div>
    """

def comprehensive_healthspan_analysis(input_type, image_input, video_input, breed, age, *hrqol_responses):
    """Combine image/video analysis with HRQOL assessment based on input type"""
    
    # Show loading first
    yield show_loading()
    
    # Simulate processing time
    time.sleep(2)
    
    # Determine which input to use based on dropdown selection
    if input_type == "Image Analysis":
        selected_media = image_input
        media_type = "image"
    elif input_type == "Video Analysis":
        selected_media = video_input
        media_type = "video"
    else:
        yield "❌ **Error**: Please select an input type."
        return
    
    if selected_media is None:
        yield f"❌ **Error**: Please provide a {media_type} for analysis."
        return
    
    # Check if questionnaire is completed
    if not hrqol_responses or all(not r for r in hrqol_responses):
        yield "❌ **Error**: Please complete the HRQOL questionnaire before analysis."
        return
    
    # Build HRQOL responses dictionary
    response_keys = []
    for domain_key, domain_data in HRQOL_QUESTIONNAIRE.items():
        for question in domain_data["questions"]:
            response_keys.append(question["id"])
    
    hrqol_dict = {key: hrqol_responses[i] if i < len(hrqol_responses) else "" 
                  for i, key in enumerate(response_keys)}
    
    # Calculate HRQOL scores
    hrqol_scores = calculate_hrqol_scores(hrqol_dict)
    
    # Initialize analysis variables
    video_features = {}
    breed_info = None
    bio_age = None
    health_aspects = {}
    
    # Perform analysis based on media type
    if media_type == "image":
        try:
            detected_breed, breed_conf, health_aspects = classify_breed_and_health(selected_media, breed)
            bio_age = predict_biological_age(selected_media, detected_breed)
            breed_info = {
                "breed": detected_breed,
                "confidence": breed_conf,
                "bio_age": bio_age
            }
        except Exception as e:
            pass
    
    elif media_type == "video":
        # For video, we analyze both movement and can extract frame for breed analysis
        video_features = analyze_video_gait(selected_media) or {}
        
        # Try to extract a frame from video for breed analysis
        try:
            cap = cv2.VideoCapture(selected_media)
            ret, frame = cap.read()
            if ret:
                img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                detected_breed, breed_conf, health_aspects = classify_breed_and_health(img, breed)
                bio_age = predict_biological_age(img, detected_breed)
                breed_info = {
                    "breed": detected_breed,
                    "confidence": breed_conf,
                    "bio_age": bio_age
                }
            cap.release()
        except Exception as e:
            pass
    
    # Calculate Composite Healthspan Score
    video_weight = 0.4 if video_features else 0.0
    hrqol_weight = 0.6 if video_features else 1.0
    
    if video_features:
        video_score = (
            video_features.get("mobility_score", 70) * 0.15 +
            video_features.get("comfort_score", 70) * 0.10 +
            video_features.get("vitality_score", 70) * 0.15
        )
    else:
        video_score = 0
    
    hrqol_composite = (
        hrqol_scores["vitality"] * 0.25 +
        hrqol_scores["comfort"] * 0.25 +
        hrqol_scores["emotional_wellbeing"] * 0.25 +
        hrqol_scores["alertness"] * 0.25
    )
    
    final_healthspan_score = (video_score * video_weight) + (hrqol_composite * hrqol_weight)
    final_healthspan_score = min(100, max(0, final_healthspan_score))
    
    # Generate comprehensive report with improved colors
    input_type_icon = "📸" if media_type == "image" else "🎥"
    
    report_html = f"""
    <div style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 1000px; margin: 0 auto;">
        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 30px; border-radius: 15px; margin: 20px 0; text-align: center; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
            <h2 style="margin: 0; font-size: 2em; text-shadow: 1px 1px 2px rgba(0,0,0,0.3);">{input_type_icon} Comprehensive Healthspan Assessment</h2>
            <div style="font-size: 1.1em; margin: 10px 0; opacity: 0.9;">Analysis Type: {input_type}</div>
            <div style="font-size: 3em; font-weight: bold; margin: 15px 0; text-shadow: 2px 2px 4px rgba(0,0,0,0.3);">{final_healthspan_score:.1f}/100</div>
            <div style="font-size: 1.2em; background: rgba(255,255,255,0.2); padding: 8px 16px; border-radius: 20px; display: inline-block;">{get_healthspan_grade(final_healthspan_score)}</div>
        </div>
        
        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(240px, 1fr)); gap: 20px; margin: 30px 0;">
    """
    
    # Add domain score cards with improved contrast
    for domain, score in [("vitality", "🔋 Vitality"), ("comfort", "😌 Comfort"), ("emotional_wellbeing", "😊 Emotional"), ("alertness", "🧠 Alertness")]:
        colors = get_score_color(hrqol_scores[domain])
        report_html += f"""
            <div style="border: 2px solid #e0e0e0; padding: 20px; border-radius: 12px; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
                <h4 style="margin: 0 0 15px 0; color: #333333; font-weight: 600;">{score.split()[1]}</h4>
                <div style="background: #e9ecef; height: 12px; border-radius: 6px; margin: 10px 0; border: 1px solid #dee2e6;">
                    <div style="background: {colors['bg']}; height: 100%; width: {hrqol_scores[domain]}%; border-radius: 6px; transition: width 0.3s ease; position: relative; display: flex; align-items: center; justify-content: center;">
                        <span style="color: {colors['text']}; font-size: 10px; font-weight: bold; text-shadow: 1px 1px 1px rgba(0,0,0,0.3);">{hrqol_scores[domain]:.0f}</span>
                    </div>
                </div>
                <div style="font-size: 1.1em; font-weight: bold; color: #333333;">{hrqol_scores[domain]:.1f}/100</div>
            </div>
        """
    
    report_html += "</div>"
    
    # Visual Analysis section with better contrast
    if breed_info:
        pace_info = ""
        if age and age > 0:
            pace = breed_info["bio_age"] / age
            pace_status = "Accelerated" if pace > 1.2 else "Normal" if pace > 0.8 else "Slow"
            pace_color = "#FF5722" if pace > 1.2 else "#4CAF50" if pace < 0.8 else "#FF9800"
            pace_info = f"""<p style="margin: 8px 0;"><strong style="color: #333;">Aging Pace:</strong> 
                           <span style="background: {pace_color}; color: white; padding: 4px 8px; border-radius: 12px; font-weight: bold; text-shadow: 1px 1px 1px rgba(0,0,0,0.3);">
                           {pace:.2f}× ({pace_status})</span></p>"""
        
        report_html += f"""
        <div style="border: 2px solid #2196F3; padding: 20px; border-radius: 12px; margin: 20px 0; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
            <h3 style="color: #1976D2; margin: 0 0 15px 0; font-weight: 600; border-bottom: 2px solid #E3F2FD; padding-bottom: 8px;">{input_type_icon} Visual Analysis</h3>
            <p style="margin: 8px 0; color: #1976D2;"><strong>Detected Breed:</strong> <span style="color: #1976D2; font-weight: 600;">{breed_info['breed']}</span> <span style="background: #E3F2FD; color: #1976D2; padding: 2px 6px; border-radius: 8px; font-size: 0.9em;">({breed_info['confidence']:.1%} confidence)</span></p>
            <p style="margin: 8px 0; color: #1976D2;"><strong>Estimated Biological Age:</strong> <span style="color: #1976D2; font-weight: 600;">{breed_info['bio_age']} years</span></p>
            <p style="margin: 8px 0; color: #1976D2;"><strong>Chronological Age:</strong> <span style="color: #1976D2; font-weight: 600;">{age or 'Not provided'} years</span></p>
            {pace_info}
        </div>
        """
    
    # Add video-specific analysis if available
    if video_features:
        report_html += f"""
        <div style="border: 2px solid #FF5722; padding: 20px; border-radius: 12px; margin: 20px 0; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
            <h3 style="color: #D84315; margin: 0 0 15px 0; font-weight: 600; border-bottom: 2px solid #FFEBE7; padding-bottom: 8px;">🎥 Video Gait Analysis</h3>
            <p style="margin: 8px 0; color: #333;"><strong>Duration:</strong> <span style="color: #D84315; font-weight: 600;">{video_features['duration_sec']} seconds</span></p>
            <p style="margin: 8px 0; color: #333;"><strong>Mobility Assessment:</strong> <span style="color: #D84315; font-weight: 600;">{video_features['mobility_assessment']}</span></p>
            <p style="margin: 8px 0; color: #333;"><strong>Comfort Assessment:</strong> <span style="color: #D84315; font-weight: 600;">{video_features['comfort_assessment']}</span></p>
            <p style="margin: 8px 0; color: #333;"><strong>Vitality Assessment:</strong> <span style="color: #D84315; font-weight: 600;">{video_features['vitality_assessment']}</span></p>
            <p style="margin: 8px 0; color: #333;"><strong>Frames Analyzed:</strong> <span style="color: #D84315; font-weight: 600;">{video_features['frames_analyzed']}</span></p>
        </div>
        """
    
    # Physical Health Assessment with improved visibility
    if health_aspects and media_type == "image":
        report_html += f"""
        <div style="border: 2px solid #4CAF50; padding: 20px; border-radius: 12px; margin: 20px 0; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
            <h3 style="color: #2E7D32; margin: 0 0 15px 0; font-weight: 600; border-bottom: 2px solid #E8F5E8; padding-bottom: 8px;">📸 Physical Health Assessment</h3>
        """
        for aspect, data in health_aspects.items():
            is_healthy = any(word in data["assessment"].lower() for word in ["healthy", "bright", "clean", "ideal"])
            status_icon = "✅" if is_healthy else "⚠️"
            status_color = "#2E7D32" if is_healthy else "#F57C00"
            bg_color = "#E8F5E8" if is_healthy else "#FFF3E0"
            
            report_html += f"""
            <div style="margin: 10px 0; padding: 12px; background: {bg_color}; border-radius: 8px; border-left: 4px solid {status_color};">
                <p style="margin: 0; color: #333;">
                    <span style="font-size: 1.2em;">{status_icon}</span> 
                    <strong style="color: {status_color};">{aspect}:</strong> 
                    <span style="color: #333; font-weight: 500;">{data['assessment']}</span>
                    <span style="background: #E0E0E0; color: #424242; padding: 2px 6px; border-radius: 8px; font-size: 0.85em; margin-left: 8px;">
                    ({data['confidence']:.1%} confidence)</span>
                </p>
            </div>
            """
        report_html += "</div>"
    
    # Add recommendations
    recommendations = []
    if hrqol_scores["vitality"] < 60:
        recommendations.append("🔋 **Vitality Enhancement**: Consider shorter, frequent exercise sessions and mental stimulation")
    if hrqol_scores["comfort"] < 70:
        recommendations.append("😌 **Comfort Support**: Evaluate joint supplements and orthopedic bedding")
    if hrqol_scores["emotional_wellbeing"] < 65:
        recommendations.append("😊 **Emotional Care**: Increase routine predictability and reduce stressors")
    if hrqol_scores["alertness"] < 70:
        recommendations.append("🧠 **Cognitive Support**: Implement brain training games and mental challenges")
    
    if recommendations:
        report_html += f"""
        <div style="border: 2px solid #FF9800; padding: 20px; border-radius: 12px; margin: 20px 0; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
            <h3 style="color: #F57C00; margin: 0 0 15px 0; font-weight: 600; border-bottom: 2px solid #FFF3E0; padding-bottom: 8px;">🎯 Personalized Recommendations</h3>
            {''.join([f'<div style="margin: 10px 0; padding: 12px; background: #FFF8E1; border-radius: 8px; border-left: 4px solid #FF9800;"><p style="margin: 0; color: #333; font-weight: 500;">{rec}</p></div>' for rec in recommendations])}
        </div>
        """
    
    # Disclaimer with improved visibility
    report_html += """
        <div style="background: #F5F5F5; border: 1px solid #E0E0E0; padding: 20px; border-radius: 8px; margin: 20px 0;">
            <p style="margin: 0; font-size: 0.9em; color: #424242; line-height: 1.5;">
                <strong style="color: #D32F2F;">⚠️ Important Disclaimer:</strong> 
                This analysis uses validated HRQOL assessment tools but is for educational purposes only. 
                Always consult with a qualified veterinarian for professional medical advice and diagnosis.
            </p>
        </div>
    </div>
    """
    
    yield report_html

def update_media_input(input_type):
    """Update the visibility of media inputs based on dropdown selection"""
    if input_type == "Image Analysis":
        return gr.update(visible=True), gr.update(visible=False)
    else:  # Video Analysis
        return gr.update(visible=False), gr.update(visible=True)

# Custom CSS for enhanced styling
custom_css = """
/* Enhanced gradient background */
.gradio-container {
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    min-height: 100vh;
}

/* Card styling */
.input-card, .questionnaire-card {
    background: white;
    border-radius: 15px;
    padding: 25px;
    box-shadow: 0 8px 25px rgba(0,0,0,0.1);
    margin: 10px;
    border: 1px solid #e0e6ed;
}

/* Header styling */
.main-header {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
    text-align: center;
    padding: 30px;
    border-radius: 15px;
    margin-bottom: 30px;
    box-shadow: 0 8px 25px rgba(102, 126, 234, 0.3);
}

/* Button styling */
.analyze-button {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    border: none;
    color: white;
    padding: 15px 30px;
    font-size: 16px;
    font-weight: 600;
    border-radius: 25px;
    cursor: pointer;
    transition: all 0.3s ease;
    box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3);
}

.analyze-button:hover {
    transform: translateY(-2px);
    box-shadow: 0 8px 25px rgba(102, 126, 234, 0.4);
}

/* Accordion styling */
.accordion-header {
    background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%);
    border: 1px solid #dee2e6;
    border-radius: 8px;
    padding: 15px;
    margin: 10px 0;
    cursor: pointer;
    transition: all 0.3s ease;
}

.accordion-header:hover {
    background: linear-gradient(135deg, #e9ecef 0%, #dee2e6 100%);
    transform: translateY(-1px);
}

/* Dropdown styling */
.gr-dropdown {
    border-radius: 8px;
    border: 2px solid #e0e6ed;
    transition: border-color 0.3s ease;
}

.gr-dropdown:focus {
    border-color: #667eea;
    box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1);
}

/* Progress animation */
@keyframes pulse {
    0% { opacity: 1; }
    50% { opacity: 0.5; }
    100% { opacity: 1; }
}

.loading-pulse {
    animation: pulse 2s infinite;
}
"""

# Gradio Interface with Enhanced UI
with gr.Blocks(
    title="🐶 VetMetrica HRQOL Dog Health Analyzer", 
    theme=gr.themes.Soft(),
    css=custom_css
) as demo:
    
    # Main Header
    gr.HTML("""
    <div class="main-header">
        <h1 style="margin: 0; font-size: 2.5em; text-shadow: 2px 2px 4px rgba(0,0,0,0.3);">
            🐕 VetMetrica© HRQOL Dog Health & Age Analyzer
        </h1>
        <p style="margin: 15px 0 0 0; font-size: 1.2em; opacity: 0.9;">
            AI-powered comprehensive analysis using validated Health-Related Quality of Life metrics
        </p>
    </div>
    """)
    
    with gr.Row():
        # Left Column - Enhanced Media Input
        with gr.Column(scale=1):
            gr.HTML("""
            <div class="input-card">
                <h2 style="color: #667eea; margin: 0 0 20px 0; text-align: center;">
                    📸 Media Input Selection
                </h2>
            </div>
            """)
            
            # Enhanced dropdown with better styling
            input_type_dropdown = gr.Dropdown(
                choices=["Image Analysis", "Video Analysis"],
                label="🔍 Select Analysis Type",
                value="Image Analysis",
                interactive=True,
                elem_classes=["gr-dropdown"]
            )
            
            # Media input components with enhanced labels
            image_input = gr.Image(
                type="pil", 
                label="📷 Upload Dog Photo or Use Webcam",
                visible=True,
                sources=["upload", "webcam"],
                height=300
            )
            
            video_input = gr.Video(
                label="🎥 Upload Video (10-30 seconds) or Record with Webcam",
                visible=False,
                sources=["upload", "webcam"],
                height=300
            )
            
            # Update visibility based on dropdown selection
            input_type_dropdown.change(
                fn=update_media_input,
                inputs=[input_type_dropdown],
                outputs=[image_input, video_input]
            )
            
            # Enhanced optional information section
            gr.HTML("""
            <div style="margin: 20px 0;">
                <h3 style="color: #667eea; text-align: center; margin-bottom: 15px;">
                    ⚙️ Optional Information
                </h3>
            </div>
            """)
            
            breed_input = gr.Dropdown(
                STANFORD_BREEDS, 
                label="🐕 Dog Breed (Auto-detected if not specified)",
                value=None,
                allow_custom_value=True,
                elem_classes=["gr-dropdown"]
            )
            age_input = gr.Number(
                label="📅 Chronological Age (years)", 
                precision=1, 
                value=None,
                minimum=0,
                maximum=25
            )
        
        # Right Column - Enhanced HRQOL Questionnaire
        with gr.Column(scale=1):
            gr.HTML("""
            <div class="questionnaire-card">
                <h2 style="color: #667eea; margin: 0 0 10px 0; text-align: center;">
                    📋 VetMetrica© HRQOL Assessment
                </h2>
                <p style="text-align: center; color: #666; font-style: italic; margin-bottom: 20px;">
                    Complete all sections for accurate healthspan analysis
                </p>
            </div>
            """)
            
            hrqol_inputs = []
            
            for domain_key, domain_data in HRQOL_QUESTIONNAIRE.items():
                # Enhanced accordion header
                gr.HTML(f"""
                <div class="accordion-header">
                    <h3 style="margin: 0; color: #333;">
                        {domain_data['title']}
                    </h3>
                    <p style="margin: 5px 0 0 0; color: #666; font-size: 0.9em;">
                        {domain_data['description']}
                    </p>
                </div>
                """)
                
                with gr.Accordion(domain_data["title"], open=True):
                    for question in domain_data["questions"]:
                        # Enhanced dropdown for each question
                        dropdown = gr.Dropdown(
                            choices=question["options"],
                            label=question["text"],
                            value=None,
                            interactive=True,
                            elem_classes=["gr-dropdown"]
                        )
                        hrqol_inputs.append(dropdown)
    
    # Enhanced Analysis Button
    gr.HTML("""
    <div style="text-align: center; margin: 30px 0;">
    """)
    
    analyze_button = gr.Button(
        "🔬 Analyze Comprehensive Healthspan", 
        variant="primary", 
        size="lg",
        elem_classes=["analyze-button"]
    )
    
    gr.HTML("</div>")
    
    # Enhanced Results Section
    output_report = gr.HTML()
    
    # Connect analysis function with loading
    analyze_button.click(
        fn=comprehensive_healthspan_analysis,
        inputs=[input_type_dropdown, image_input, video_input, breed_input, age_input] + hrqol_inputs,
        outputs=output_report
    )
    


if __name__ == "__main__":
    demo.launch()