File size: 909 Bytes
57ea606 767abf3 57ea606 767abf3 57ea606 767abf3 57ea606 767abf3 57ea606 767abf3 2634c43 767abf3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 |
import gradio as gr
from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image
# Load model and processor
processor = AutoImageProcessor.from_pretrained("dima806/facial_emotions_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection")
# Function to predict emotion
def detect_emotion(image):
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax(-1).item()
return model.config.id2label[predicted_class]
# Gradio interface
iface = gr.Interface(
fn=detect_emotion,
inputs=gr.Image(type="pil"),
outputs="text",
title="Facial Emotion Detection",
description="Upload a face image to detect emotion using dima806 model"
)
iface.launch(share=True) |