### 1. Imports and class names setup ### 
import gradio as gr
import os
import torch
from model import TinyCNN
from timeit import default_timer as timer
from typing import Tuple, Dict
import torch
import torchvision
from torchvision import transforms
from torch import nn
# Setup class names
with open("class_names.txt", "r") as f: # reading them in from class_names.txt
    class_names = [defects.strip() for defects in  f.readlines()]
    
### 2. Model and transforms preparation ###    

# Create model
TinyCNN_model = TinyCNN(input_shape=3,
                  hidden_units=64, 
                  output_shape=len(class_names))


transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

# Load saved weights
TinyCNN_model.load_state_dict(
    torch.load(
        f="cnn.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 = transform(img).unsqueeze(dim=0)
    
    # Put model into evaluation mode and turn on inference mode
    TinyCNN_model.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(TinyCNN_model(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 = "Wafer Defect Detection"
description = "An app to predict Wafer Defects in semiconductors.[Center, Donut, Edge-Loc, Edge-Ring, Loc, Near-full, Random, Scratch, none]"


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

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

)

# Launch the app!
demo.launch()