Spaces:
Sleeping
Sleeping
File size: 2,785 Bytes
7272f73 47b58ef 7272f73 47b58ef 7272f73 27e7cf2 7272f73 9d457f8 7272f73 27e7cf2 7272f73 27e7cf2 7272f73 47b58ef 7272f73 47b58ef 9d457f8 7272f73 47b58ef 27e7cf2 7272f73 27e7cf2 7272f73 27e7cf2 7272f73 |
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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import streamlit as st
from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO
# Set page configuration
st.set_page_config(
page_title="Age Group Classification",
page_icon="👶👧👩👵",
layout="centered"
)
# Add title and description
st.title("Age Group Classification")
st.markdown("Upload an image of a person to classify their approximate age group using the `nateraw/vit-age-classifier` model.")
@st.cache_resource
def load_model():
"""Load the age classification model and cache it."""
return pipeline("image-classification", model="nateraw/vit-age-classifier")
def load_image_from_url(url):
"""Load an image from a URL."""
response = requests.get(url)
img = Image.open(BytesIO(response.content))
return img
# Load the model
pipe = load_model()
# Create file uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
# Function to get top prediction
def get_top_prediction(predictions):
# Get the prediction with highest confidence
top_prediction = max(predictions, key=lambda x: x['score'])
return top_prediction
# Process the image and display results
if uploaded_file is not None:
# Process uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_container_width=True)
with st.spinner("Analyzing age..."):
predictions = pipe(image)
top_pred = get_top_prediction(predictions)
# Display result
st.markdown("### Result:")
st.metric(
label="Predicted Age Range",
value=top_pred['label'],
delta=f"Confidence: {top_pred['score']:.2%}"
)
elif 'example_loaded' in st.session_state and st.session_state.example_loaded:
# Process example image
with st.spinner("Loading example image..."):
# Download and load the image properly
image = load_image_from_url(st.session_state.example_image)
st.image(image, caption="Example Image", use_container_width=True)
with st.spinner("Analyzing age..."):
# Pass the actual PIL Image object to the pipeline
predictions = pipe(image)
top_pred = get_top_prediction(predictions)
# Display result
st.markdown("### Result:")
st.metric(
label="Predicted Age Range",
value=top_pred['label'],
delta=f"Confidence: {top_pred['score']:.2%}"
)
# Add information about the model
st.markdown("---")
st.markdown("### About the Model")
st.markdown("""
This app uses the `nateraw/vit-age-classifier` model from Hugging Face, which classifies
images into age groups like "0-2", "3-9", "10-19", etc. The app displays only the most
likely age range prediction with its confidence score.
""") |