import gradio as gr
import os
import torch

from model import ResNet18_model
from transform import transforms_img

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

# Setup class names
class_names = ["Anthracnose", "Chimaera", "Healthy Leaves"]

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

# Create model
result_0 = ResNet18_model(num_classes = len(class_names))

Transform = transforms_img

# Load saved weights
result_0.load_state_dict(torch.load(f="Palm_Leaves_ResNet18.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)

### 3. Predict function ###

# Create predict function
from typing import Tuple, Dict

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(0)
    
    # Put model into evaluation mode and turn on inference mode
    result_0.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(result_0(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 = "Palm Leaves Detection Using CNN"
description = "Deep Learning model that classify what condition of Palm Leaves."
article = "Created by Mohammad Lukman."

# 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=3, 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,
                    article=article,
                    allow_flagging = 'never'
                   )
# Launch the demo!
demo.launch()