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import gradio as gr
import torch
import torchvision.transforms as transforms
from PIL import Image
import os
from pathlib import Path
from gradio.flagging import SimpleCSVLogger
from utils import GradioConfig
class Resnet50Imagenet1kGradioApp:
def __init__(self,cfg: GradioConfig):
self.device = cfg.device # Change this to 'cuda' if you have a GPU available
# Validate model path parameters
# Convert to strings if needed and create path
model_dir = str(cfg.model_dir)
model_file = str(cfg.model_file_name)
model_full_path = Path(model_dir) / model_file
# Verify the file exists
if not model_full_path.exists():
raise FileNotFoundError(f"Model file not found at: {model_full_path}")
# load traced model
self.model = torch.jit.load(model_full_path)
self.model = self.model.to(self.device)
self.model.eval()
# Define the same transforms used during training/testing
self.transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
self.labels = cfg.labels
@torch.no_grad()
def predict(self, image):
if image is None:
return None
# Convert to PIL Image if needed
if not isinstance(image, Image.Image):
image = Image.fromarray(image).convert('RGB')
# Preprocess image
img_tensor = self.transforms(image).unsqueeze(0).to(self.device)
# Get prediction
output = self.model(img_tensor)
probabilities = torch.nn.functional.softmax(output[0], dim=0)
probs, indices = torch.topk(probabilities, k=5)
print(f"Top 5 predictions:")
for idx, prob in zip(indices, probs):
print(f"idx: {idx}, label : {self.labels[idx]} , prob: {prob.item() * 100:.2f}%") # Format probability to 2 decimal places)
return {
self.labels[idx]: float(prob)
for idx, prob in zip(indices, probs)
}
# Create classifier instance
cfg = GradioConfig()
classifier = Resnet50Imagenet1kGradioApp(cfg)
# Create Gradio interface
demo = gr.Interface(
fn=classifier.predict,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=5),
title="Resnet50 Imagenet 1k classifier",
description="Upload an image to classify Images",
flagging_mode="never",
flagging_callback=SimpleCSVLogger(),
examples=["examples/blue_lobster.jpeg",
"examples/lobster.jpeg",
"examples/lobster2.jpeg",
"examples/turtle.jpeg"]
)
if __name__ == "__main__":
demo.launch() |