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import torch
import gradio as gr
from typing import Tuple, Dict
from torchvision import models
import torch.nn as nn
from model import get_transforms, create_effnetb2_model
model = create_effnetb2_model(num_classes=3)
model.eval()
cns = ['negative', 'neutral', 'positive']
def predict(img) -> Tuple[Dict, float]:
transform = get_transforms()
img = transform(img).unsqueeze(0)
with torch.inference_mode():
pred_probs = torch.softmax(model(img), dim=1)
pred_labels_and_probs = {cns[i]: float(pred_probs[0][i]) for i in range(len(cns))}
return pred_labels_and_probs
title = "Effnetb2 Sentiment Analysis"
description = "An EfficientNetB2 feature extractor computer vision model to analyse image sentiment."
demo = gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=3, label="Predictions")],
title=title,
description=description)
if __name__ == "__main__":
demo.launch() |