### 1. Imports and class names setup

import gradio as gr
import os
import torch

from model import create_effnetb2_model
from timeit import default_timer as timer

# set up class names
with open('class_names.txt', "r") as f:
  class_names = [food.strip() for food in f.readlines()]

### 2. model and transforms preparation

# Create model
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes= len(class_names)
)

# Load saved weights
effnetb2.load_state_dict(
    torch.load(
        f="pretrained_effnetb2_feature_extractor_food101.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 = effnetb2_transforms(img).unsqueeze(0)

    # Put model into evaluation mode and turn on inference mode
    effnetb2.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(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 😋"
description = "An EfficientNetB2 feature extractor Computer Vision model to classify images of Food into [101 different classes](https://huggingface.co/spaces/pritesh29/FoodVision/blob/main/class_names.txt)."
article = "Created by [pritesh tadvi](https://pritesh2000.github.io/)\nTraining code at [kaggle/foodvision](https://www.kaggle.com/code/priteshtadvi/foodvision-a-food-classification-model)"

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

# 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)")],
    examples = example_list,
    title = title,
    description = description,
    article = article
)

# launch the app
demo.launch()