app.py

import torch
import torch.nn as nn
from torchvision import transforms, datasets, models
import gradio as gr

transformer = models.ResNet18_Weights.IMAGENET1K_V1.transforms()
transformer

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class_names = ['Ahegao', 'Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
classes_count = len(class_names)

model = models.resnet18(weights='DEFAULT').to(device)
model.fc = nn.Sequential(
  nn.Linear(512, classes_count)
)
model.load_state_dict(torch.load('./model_params.pt', map_location=device), strict=False)

def predict(image):
  transformed_image = transformer(image).unsqueeze(0).to(device)
  model.eval()

  with torch.inference_mode():
    pred = torch.softmax(model(transformed_image), dim=1)

  pred_and_labels = {class_names[i]: pred[0][i].item() for i in range(len(pred[0]))}

  return pred_and_labels

title = "Emotion Checker"
description = "Can classify 6 emotions: Ahegao, Angry, Happy, Neutral, Sad, Surprise"

examples = [
    './example_1.jpg',
    './example_2.jpg',
    './example_3.jpg',
    './example_4.jpg',
    './example_5.jpg',
    './example_6.jpg',
]


app = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Label(num_top_classes=classes_count, label="Predictions")],
    examples=examples,
    title=title,
    description=description
)

app.launch(
    share=True, 
    height=800
)