### 1. Imports and class names setup ### 
import gradio as gr
import os
import torch

from model import create_vit_model
from timeit import default_timer as timer
from typing import Tuple, Dict

# Setup class names
with open("categories.txt", "r") as f:
    class_names = [food_name.strip() for food_name in  f.readlines()]
    
### 2. Model and transforms preparation ###    

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

# Load saved weights
vit.load_state_dict(
    torch.load(
        f="pretrain_vit.pth",
        map_location=torch.device('cpu')
    )
)

### 3. Predict function ###

# Create predict function
def predict(img) -> Tuple[Dict, float]:
    """Transforms and performs a prediction on img and returns prediction and time taken.
    """
    # Start the timer
    start_time = timer()
    
    # Transform the target image and add a batch dimension
    img = vit_transforms(img).unsqueeze(0)
    
    # Put model into evaluation mode and turn on inference mode
    vit.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(vit(img), dim=1)
    
    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
    
    # Calculate the prediction time
    pred_time = round(timer() - start_time, 5)
    
    # Return the prediction dictionary and prediction time 
    return pred_labels_and_probs, pred_time

### 4. Gradio app ###

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

# Launch the app!
demo.launch()