minye

get-age.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import requests
+import torch
+from io import BytesIO
+from PIL import Image
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+# Init model, transforms
+@st.cache_resource  
+def get_model_transformers():
+    model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
+    transforms = ViTImageProcessor.from_pretrained('nateraw/vit-age-classifier')
+    return model, transforms
+    
+st.title("나이를 예측해봅시다!")
+
+uploaded_file = st.file_uploader("나이를 예측할 사람의 이미지를 업로드하세요.", type=["jpg", "jpeg", "png", 'gif', 'webp'])
+
+if uploaded_file:
+    st.write(f'업로드된 파일 이름: {uploaded_file}')
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+    
+    # Get example image from official fairface repo + read it in as an image
+    r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
+    im = Image.open(uploaded_file)
+
+    model, transforms = get_model_transformers()
+
+    # Transform our image and pass it through the model
+    inputs = transforms(im, return_tensors='pt')
+    output = model(**inputs)
+
+    # Predicted Class probabilities
+    proba = output.logits.softmax(1)
+
+    values, indices = torch.topk(proba, k=5)
+
+    result_dict = {model.config.id2label[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
+    first_result = list(result_dict.keys())[0]
+
+    print(f'predicted result:{result_dict}')
+    print(f'first_result: {first_result}')
+
+
+
+
+    st.header('결과')
+    st.subheader(f'예측된 나이는 {first_result} 입니다')
+
+    for key, value in result_dict.items():
+        st.write(f'{key}: {value * 100:.2f}%')

requirements.txt

@@ -0,0 +1,8 @@
+transformers
+pillow
+streamlit
+
+# https://pytorch.org/
+torch
+torchvision
+torchaudio

requirements.txt

@@ -0,0 +1,15 @@
+transformers
+pillow
+streamlit
+
+# https://pytorch.org/
+torch
+torchvision
+torchaudiotransformers
+pillow
+streamlit
+
+# https://pytorch.org/
+torch
+torchvision
+torchaudio

sample.py

@@ -0,0 +1,28 @@
+import requests
+import torch
+from io import BytesIO
+from PIL import Image
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+# Get example image from official fairface repo + read it in as an image
+r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
+im = Image.open(BytesIO(r.content))
+
+# Init model, transforms
+model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
+transforms = ViTImageProcessor.from_pretrained('nateraw/vit-age-classifier')
+
+# Transform our image and pass it through the model
+inputs = transforms(im, return_tensors='pt')
+output = model(**inputs)
+
+# Predicted Class probabilities
+proba = output.logits.softmax(1)
+
+values, indices = torch.topk(proba, k=5)
+
+result_dict = {model.config.id2label[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
+first_result = list(result_dict.keys())[0]
+
+print(f'predicted result:{result_dict}')
+print(f'first_result: {first_result}')