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import gradio as gr
import torch
import torchvision.models as models
from huggingface_hub import hf_hub_download
import json
from PIL import Image
import torchvision.transforms as transforms
# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load model weights and class labels
weights_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-sports-category-classification", filename="resnet18_sports_classification.pth")
labels_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-sports-category-classification", filename="class_labels.json")
with open(labels_path, "r") as f:
class_labels = json.load(f)
# Load model
model = models.resnet18(pretrained=False)
num_classes = len(class_labels)
model.fc = torch.nn.Linear(in_features=512, out_features=num_classes)
model.load_state_dict(torch.load(weights_path, map_location=device))
model.to(device)
model.eval()
# Define transform
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Prediction function
def predict(image):
image = transform(image).unsqueeze(0).to(device) # Preprocess image
with torch.no_grad():
output = model(image)
_, predicted = torch.max(output, 1)
predicted_class = class_labels[predicted.item()]
return {predicted_class: 1.0} # Confidence is assumed to be 1.0 for simplicity
# Gradio Interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload a Sports Image"),
outputs=gr.Label(label="Predicted Sport Category"),
title="🏆 Sports Category Classifier 🏅",
description="🎯 Upload an image of a sports activity, and the AI model will classify it into the correct category! 🏀⚽🏓",
theme="huggingface",
allow_flagging="never",
)
demo.launch() |