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

# Add the current directory to sys.path to ensure modules are found
sys.path.append(os.path.dirname(__file__))

from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Tuple, Dict

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

### 2. Model and transforms preparation ###

# Create EffNetB2 big model and transforms
effnetb2_foodvision_big, effnetb2_foodvision_big_transforms  = create_effnetb2_model(
    num_classes= len(class_names), # len(class_names) would also work
)

# Load saved weights
effnetb2_foodvision_big.load_state_dict(
    torch.load(
        f="effnetb2_big_food101_20_percent.pth",
        map_location=torch.device("cpu"),  # load to 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 = effnetb2_foodvision_big_transforms(img).unsqueeze(0)

    # Put model into evaluation mode and turn on inference mode
    effnetb2_foodvision_big.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(effnetb2_foodvision_big(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 title, description and article strings
title = "FoodVision Big Model"
description = "An EfficientNetB2 computer vision model to classify images of food101 dataset."

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
                    inputs=gr.Image(type="pil"), # what are the inputs?
                    outputs=[gr.Label(num_top_classes=6, label="Predictions"), # what are the outputs?
                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
                    # Create examples list from "examples/" directory
                    examples=example_list,
                    title=title,
                    description=description,
                    )

# Launch the demo!
demo.launch()