### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
import numpy as np
from PIL import Image
from model import create_vit_model  # Make sure this function exists in model.py
from timeit import default_timer as timer
from typing import Tuple, Dict

# Setup class names (or hardcode them if needed)
class_names = ["apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad",
               "beignets", "bibimbap", "biryani", "bread_pudding", "breakfast_burrito", "bruschetta", 
               "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "chai", "chapati", 
               "cheese_plate", "cheesecake", "chicken_curry", "chicken_quesadilla", "chicken_wings", 
               "chocolate_cake", "chocolate_mousse", "chole_bhature", "churros", "clam_chowder", 
               "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "dabeli", 
               "dal", "deviled_eggs", "dhokla", "donuts", "dosa", "dumplings", "edamame", "eggs_benedict", 
               "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", 
               "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", 
               "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", 
               "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", 
               "hummus", "ice_cream", "idli", "jalebi", "kathi_rolls", "kofta", "kulfi", "lasagna", 
               "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", 
               "momos", "mussels", "naan", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", 
               "paella", "pakoda", "pancakes", "pani_puri", "panna_cotta", "panner_butter_masala", 
               "pav_bhaji", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", 
               "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", 
               "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", 
               "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", 
               "tacos", "takoyaki", "tiramisu", "tuna_tartare", "vadapav", "waffles"]

### 2. Model and transforms setup ###

# Create the model and transforms
vit, vit_transforms = create_vit_model(num_classes=len(class_names))

# Load saved model weights (assumes model is trained and .pth file is in the correct path)
vit.load_state_dict(torch.load("vit_epoch_2.pth", map_location=torch.device("cpu")))

### 3. Prediction function ###

def predict(img) -> Tuple[Dict[str, float], float]:
    from PIL import UnidentifiedImageError

    try:
        # Convert ndarray to PIL.Image if needed
        if isinstance(img, np.ndarray):
            img = Image.fromarray(img.astype("uint8"))  # Ensure correct dtype

        # Ensure image is in RGB mode
        if img.mode != "RGB":
            img = img.convert("RGB")

        start_time = timer()

        # Apply transforms (expects a PIL image)
        img_tensor = vit_transforms(img).unsqueeze(0)

        vit.eval()
        with torch.inference_mode():
            pred_probs = torch.softmax(vit(img_tensor), dim=1)

        pred_labels_and_probs = {
            class_names[i]: float(pred_probs[0][i])
            for i in range(len(class_names))
        }

        pred_time = round(timer() - start_time, 5)

        return pred_labels_and_probs, pred_time

    except (UnidentifiedImageError, TypeError, ValueError) as e:
        return {"Error": f"Invalid image input: {str(e)}"}, 0.0


### 4. Gradio app setup ###

# Title, description, and article text
title = "VisionBite 🍕🥩🍣"
description = (
    "A Vision Transformer (ViT-Base-16) model trained to classify images of food "
    "into 121 distinct categories. The model uses a transformer-based architecture "
    "to extract visual features and achieve accurate classification across diverse food items."
)
article = (
    "Model trained on the [Food121 dataset](https://huggingface.co/datasets/ItsNotRohit/Food121) "
    "with 95% top-5 prediction accuracy."
)

# Setup example images (if available)
if os.path.exists("examples"):
    example_list = [["examples/" + f] for f in os.listdir("examples") if f.endswith((".jpg", ".jpeg", ".png"))]
else:
    example_list = []

# Create Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes=5, label="Top Predictions"),
        gr.Number(label="Prediction time (s)")
    ],
    # examples=example_list
    title=title,
    description=description,
    article=article
)

# Launch app
demo.launch()