from transformers import AutoModel, AutoTokenizer
import torch
import json
import requests
from PIL import Image
from torchvision import transforms
import urllib.request
from torchvision import models
import torch.nn as nn

schema ={
  "inputs": [
    {
      "name": "image",
      "type": "image",
      "description": "The image file to classify."
    },
    {
      "name": "title",
      "type": "string",
      "description": "The text title associated with the image."
    }
  ],
  "outputs": [
    {
      "name": "label",
      "type": "string",
      "description": "Predicted class label."
    },
    {
      "name": "probability",
      "type": "float",
      "description": "Prediction confidence score."
    }
  ]
}


# --- Define the Model ---
class FineGrainedClassifier(nn.Module):
    def __init__(self, num_classes=434):  # Updated to 434 classes
        super(FineGrainedClassifier, self).__init__()
        self.image_encoder = models.resnet50(pretrained=True)
        self.image_encoder.fc = nn.Identity()
        self.text_encoder = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en')
        self.classifier = nn.Sequential(
            nn.Linear(2048 + 768, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(1024, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(512, num_classes)  # Updated to 434 classes
        )
    
    def forward(self, image, input_ids, attention_mask):
        image_features = self.image_encoder(image)
        text_output = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask)
        text_features = text_output.last_hidden_state[:, 0, :]
        combined_features = torch.cat((image_features, text_features), dim=1)
        output = self.classifier(combined_features)
        return output

# --- Data Augmentation Setup ---
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(15),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# # Load the label-to-class mapping from your Hugging Face repository
# label_map_url = "https://huggingface.co/Maverick98/EcommerceClassifier/resolve/main/label_to_class.json"
# label_to_class = requests.get(label_map_url).json()

# Load your custom model from Hugging Face
model = FineGrainedClassifier(num_classes=len(label_to_class))
checkpoint_url = f"https://huggingface.co/Maverick98/EcommerceClassifier/resolve/main/model_checkpoint.pth"
checkpoint = torch.hub.load_state_dict_from_url(checkpoint_url, map_location=torch.device('cpu'))

# Strip the "module." prefix from the keys in the state_dict if they exist
# Clean up the state dictionary
state_dict = checkpoint.get('model_state_dict', checkpoint)
new_state_dict = {}
for k, v in state_dict.items():
    if k.startswith("module."):
        new_key = k[7:]  # Remove "module." prefix
    else:
        new_key = k

    # Check if the new_key exists in the model's state_dict, only add if it does
    if new_key in model.state_dict():
        new_state_dict[new_key] = v

model.load_state_dict(new_state_dict)

# Load the tokenizer from Jina
tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-en")

# def load_image(image_path_or_url):
#     if isinstance(image_path_or_url, str) and image_path_or_url.startswith("http"):
#         with urllib.request.urlopen(image_path_or_url) as url:
#             image = Image.open(url).convert('RGB')
#     else:
#         image = Image.open(image_path_or_url).convert('RGB')
    
#     image = transform(image)
#     image = image.unsqueeze(0)  # Add batch dimension
#     return image

# def predict(image_path_or_file, title, threshold=0.4):

def inference(inputs):
    image = inputs.get("image")
    title = inputs.get("title")
    if not isinstance(title, str):
        return {"error": "Title must be a string."}
    
    if not isinstance(image, (Image.Image, torch.Tensor)):
        return {"error": "Image must be a valid image file or a tensor."}
 
    threshold = 0.4
    # Validation: Check if the title is empty or has fewer than 3 words
    if not title or len(title.split()) < 3:
        raise gr.Error("Title must be at least 3 words long. Please provide a valid title.")
    
    # Preprocess the image
    image = load_image(image_path_or_file)
    
    # Tokenize title
    title_encoding = tokenizer(title, padding='max_length', max_length=200, truncation=True, return_tensors='pt')
    input_ids = title_encoding['input_ids']
    attention_mask = title_encoding['attention_mask']

    # Predict
    model.eval()
    with torch.no_grad():
        output = model(image, input_ids=input_ids, attention_mask=attention_mask)
        probabilities = torch.nn.functional.softmax(output, dim=1)
        top3_probabilities, top3_indices = torch.topk(probabilities, 3, dim=1)

    # Map indices to class names (Assuming you have a mapping)
    with open("label_to_class.json", "r") as f:
        label_to_class = json.load(f)
        
    # Map the top 3 indices to class names
    top3_classes = [label_to_class[str(idx.item())] for idx in top3_indices[0]]

    # Check if the highest probability is below the threshold
    if top3_probabilities[0][0].item() < threshold:
        top3_classes.insert(0, "Others")
        top3_probabilities = torch.cat((torch.tensor([[1.0 - top3_probabilities[0][0].item()]]), top3_probabilities), dim=1)

    # Prepare the output as a dictionary
    results = {}
    for i in range(len(top3_classes)):
        results[top3_classes[i]] = top3_probabilities[0][i].item()
    
    return results