app.py

import os
import torch
import gradio as gr
from PIL import Image
from torchvision import transforms
from timeit import default_timer as timer
from torch.nn import functional as F
from gradio.flagging import SimpleCSVLogger

torch.set_float32_matmul_precision("medium")
# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
device = torch.device("cpu") 
torch.set_default_device(device=device)
# torch.autocast(enabled=True, dtype="float16", device_type="cuda")


TEST_TRANSFORMS = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
class_labels = [
    "Beagle",
    "Boxer",
    "Bulldog",
    "Dachshund",
    "German_Shepherd",
    "Golden_Retriever",
    "Labrador_Retriever",
    "Poodle",
    "Rottweiler",
    "Yorkshire_Terrier",
]


# Model
model:torch.nn.Module = torch.jit.load("best_model.pt", map_location=device).to(device)


@torch.no_grad()
def predict_fn(img: Image):
    start_time = timer()
    try:
        # img = np.array(img)
        # print(img)
        img = TEST_TRANSFORMS(img).to(device)
        # print(type(img),img.shape)
        logits = model(img.unsqueeze(0))
        probabilities = F.softmax(logits, dim=-1)
        # print(torch.topk(probabilities,k=2))
        y_pred = probabilities.argmax(dim=-1).item()
        confidence = probabilities[0][y_pred].item()
        predicted_label = class_labels[y_pred]
        # print(confidence,predicted_label)
        pred_time = round(timer() - start_time, 5)
        res = {f"Title: {predicted_label}": confidence}
        return (res, pred_time)
    except Exception as e:
        print(f"error:: {e}")
        gr.Error("An error occured 💥!", duration=5)
        return ({"Title ☠️": 0.0}, 0.0)


gr.Interface(
    fn=predict_fn,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes=1, label="Predictions"),  # what are the outputs?
        gr.Number(label="Prediction time (s)"),
    ],
    examples=[
        ["examples/" + i]
        for i in os.listdir(os.path.join(os.path.dirname(__file__), "examples"))
    ],
    title="Dog Breeds Classifier 🐈",
    description="CNN-based Architecture for Fast and Accurate DogsBreed Classifier",
    article="Created by muthukamalan.m ❤️",
    cache_examples=True,
    flagging_options=[],
    flagging_callback=SimpleCSVLogger()
).launch(share=False, debug=False,server_name="0.0.0.0",server_port=7860,enable_monitoring=None)