import torch
import gradio as gr
from PIL import Image
from huggingface_hub import hf_hub_download
import importlib.util
from torchvision import transforms
import random
import numpy as np

def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
        
# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Download model code
class_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Emotions", filename="modeling.py")
spec = importlib.util.spec_from_file_location("modeling", class_path)
modeling = importlib.util.module_from_spec(spec)
spec.loader.exec_module(modeling)

from modeling import clip_lora_model

# Emotions model
emotion_model = clip_lora_model().to(device)
emotion_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Emotions", filename="perceptCLIP_Emotions.pth")
emotion_model.load_state_dict(torch.load(emotion_model_path, map_location=device))
emotion_model.eval()

# Memorability model
mem_model = clip_lora_model(output_dim=1).to(device)
mem_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Memorability", filename="perceptCLIP_Memorability.pth")
mem_model.load_state_dict(torch.load(mem_model_path, map_location=device))
mem_model.eval()

# IQA model
iqa_model = clip_lora_model(output_dim=1).to(device)
iqa_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_IQA", filename="perceptCLIP_IQA.pth")
iqa_model.load_state_dict(torch.load(iqa_model_path, map_location=device))
iqa_model.eval()

# Emotion label mapping
idx2label = {
    0: "amusement",
    1: "awe",
    2: "contentment",
    3: "excitement",
    4: "anger",
    5: "disgust",
    6: "fear",
    7: "sadness"
}

# Emoji mapping
emotion_emoji = {
    "amusement": "😂",
    "awe": "😲",
    "contentment": "😊",
    "excitement": "😃",
    "anger": "😠",
    "disgust": "🤢",
    "fear": "😱",
    "sadness": "😞"
}

# Image preprocessing
def emo_mem_preprocess(image):
    transform = transforms.Compose([ 
        transforms.Resize(224),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=(0.4814, 0.4578, 0.4082), std=(0.2686, 0.2613, 0.2758)),
    ])
    return transform(image).unsqueeze(0).to(device)

def IQA_preprocess():
    random.seed(3407)
    transform = transforms.Compose([
      transforms.Resize((512,384)),
      transforms.RandomCrop(size=(224,224)),  
      transforms.ToTensor(),
      transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), 
                             std=(0.26862954, 0.26130258, 0.27577711))
    ])
    return transform

set_seed(3407)

# Inference function
def predict_percept(image):
    # If the image is passed as a PIL Image
    if isinstance(image, Image.Image):  
        img = image.convert("RGB")
    else:
        img = Image.open(image).convert("RGB")

    batch = torch.stack([IQA_preprocess()(image) for _ in range(15)]).to(device)  # Shape: (15, 3, 224, 224)
    img = emo_mem_preprocess(img)

    with torch.no_grad():
        iqa_score = iqa_model(batch).cpu().numpy()
        mem_score = mem_model(img).item()
        outputs = emotion_model(img)
        predicted = outputs.argmax(1).item()
        
    iqa_score = np.mean(iqa_score)
    min_iqa_pred = -6.52
    max_iqa_pred = 3.11

    normalized_iqa_score = ((iqa_score - min_iqa_pred) / (max_iqa_pred - min_iqa_pred))

    emotion = idx2label[predicted]
    emoji = emotion_emoji.get(emotion, "❓")  
    return f"{emotion} {emoji}", f"{mem_score:.4f}", f"{normalized_iqa_score:.4f}"
    


# Example images 
example_images = [
    "https://webneel.com/daily/sites/default/files/images/daily/02-2013/3-motion-blur-speed-photography.jpg",
    "https://img.freepik.com/free-photo/emotive-excited-female-with-dark-skin-crisp-hair-keeps-hands-clenched-fists-exclaims-with-positiveness-as-achieved-success-her-career-opens-mouth-widely-isolated-white-wall_273609-16443.jpg",
    "https://t4.ftcdn.net/jpg/01/18/44/59/360_F_118445958_NtP7tIsD0CBPyG7Uad7Z2KxVWrsfCPjP.jpg", 
    "https://apnapestcontrol.ca/wp-content/uploads/2019/02/9.jpg",  
    "https://images.pexels.com/photos/1107717/pexels-photo-1107717.jpeg?cs=srgb&dl=pexels-fotios-photos-1107717.jpg&fm=jpg",
    "https://cdn.prod.website-files.com/60e4d0d0155e62117f4faef3/61fab92edbb1ccbc7d12c167_Brian-Matiash-Puppy.jpeg",
]

# Create Gradio interface with custom CSS
iface = gr.Interface(
    fn=predict_percept,
    inputs=gr.Image(type="pil", label="Upload an Image"),
    outputs=[gr.Textbox(label="Emotion"), gr.Textbox(label="Memorability Score"), gr.Textbox(label="IQA Score")],
    title="PerceptCLIP",
    description="This is an official demo of PerceptCLIP from the paper: [Don’t Judge Before You CLIP: A Unified Approach for Perceptual Tasks](https://arxiv.org/pdf/2503.13260). For each specific task, we fine-tune CLIP with LoRA and an MLP head. Our models achieve state-of-the-art performance. \nThis demo shows results from three models, each corresponding to a different task - visual emotion analysis, memorability prediction, and image quality assessment.",
    examples=example_images
)


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