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

from model import create_effnetb0_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 = [class_name.strip() for class_name in f.readlines()]

## 2. Model and transforms preparation
effnetb0, effnetb0_transforms = create_effnetb0_model(num_classes=3)

# Load saved weights
effnetb0.load_state_dict(
    torch.load(f="EfficientNet_b0.pth", map_location=torch.device("cpu"))
)


### 3. Predict function
def predict(img) -> Tuple[Dict, float]:
    # Start a timer
    start_time = timer()

    # Transform the input image for use with EffNetB0
    img = effnetb0_transforms(img).unsqueeze(
        0
    )  # unsqueeze = add batch dimension on 0th index

    # Put model into eval mode, make prediction
    effnetb0.eval()
    with torch.inference_mode():
        # Pass transformed image through the model and turn the prediction logits into probaiblities
        pred_probs = torch.softmax(effnetb0(img), dim=1)

    # Create a prediction label and prediction probability dictionary
    pred_labels_and_probs = {
        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
    }

    # Calculate pred time
    end_time = timer()
    pred_time = round(end_time - start_time, 4)

    # Return pred dict and pred time
    return pred_labels_and_probs, pred_time


### 4. Gradio app ###
# Create title, description and article
title = "EffNet Pneumonia, by Timothy Karani"
description = "An EfficientNetB0 model for multiclass pneumonia detection"
article = "AN EFFICIENT DEEP LEARNING APPROACH FOR MULTICLASS PNEUMONIA DETECTION IN CHEST X-RAY IMAGES."

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

# Create the Gradio demo
demo = gr.Interface(
    fn=predict,  # maps inputs to outputs
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes=3, label="Predictions"),
        gr.Number(label="Prediction time (s)"),
    ],
    examples=example_list,
    title=title,
    description=description,
    article=article,
)

# Launch the demo!
demo.launch()