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

# 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 the model and tokenizer from your Hugging Face repository
model = AutoModel.from_pretrained("Maverick98/EcommerceClassifier")
tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-en")

# Define image preprocessing
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

def load_image(image_path_or_url):
    """
    Load an image from a URL or local path and preprocess it.
    """
    if 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_url, title, threshold=0.7):
    """
    Predict the top 3 categories for the given image and title.
    Includes "Others" if the confidence of the top prediction is below the threshold.
    """
    # Preprocess the image
    image = load_image(image_path_or_url)
    
    # Tokenize the title
    title_encoding = tokenizer(title, padding='max_length', max_length=32, 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 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

# Define the Gradio interface
title_input = gr.inputs.Textbox(label="Product Title", placeholder="Enter the product title here...")
image_input = gr.inputs.Textbox(label="Image URL or Path", placeholder="Enter image URL or local path here...")
output = gr.outputs.JSON(label="Top 3 Predictions with Probabilities")

gr.Interface(
    fn=predict, 
    inputs=[image_input, title_input], 
    outputs=output,
    title="Ecommerce Classifier",
    description="This model classifies ecommerce products into one of 434 categories. If the model is unsure, it outputs 'Others'.",
).launch()