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import os
import gradio as gr
import PIL
import torch
from dotenv import load_dotenv
from model_inference import ModelInference
# Load secrets and config from optional .env file
load_dotenv()
GLOBAL_MODEL = "global_model_config23_21ep_timm_resnet50.pt"
CATEGORY_MAP = "category_map.json"
CATEG_TO_NAME_MAP = "categ_to_name_map.json"
# Model prediction function
def predict_species(image: PIL.Image.Image) -> dict[str, float]:
"""Moth species prediction"""
# Build the model class
device = "cuda" if torch.cuda.is_available() else "cpu"
fgrained_classifier = ModelInference(
GLOBAL_MODEL, "timm_resnet50", CATEGORY_MAP, CATEG_TO_NAME_MAP, device, topk=5
)
# Predict on image
sp_pred = fgrained_classifier.predict(image)
return sp_pred
demo = gr.Interface(
fn=predict_species,
inputs=gr.Image(type="pil"),
outputs=gr.Label(),
title="Mila Global Moth Species Classifier",
)
if __name__ == "__main__":
demo.launch(share=True)
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