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age-gender-profilepic
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import gradio as gr
from transformers import pipeline

from PIL import Image
import pandas as pd
import numpy as np

import torch
import torch.nn as nn
import torchvision
from torchvision import datasets, models, transforms

from torch_mtcnn import detect_faces
from torch_mtcnn import show_bboxes

# pipeline = pipeline(task="image-classification", model="njgroene/fairface")
def pipeline(img):
    bounding_boxes, landmarks = detect_faces(img)
    
    img_cropped = img.crop(bb)

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    model_fair_7 = torchvision.models.resnet34(pretrained=True)
    model_fair_7.fc = nn.Linear(model_fair_7.fc.in_features, 18)
    model_fair_7.load_state_dict(torch.load('res34_fair_align_multi_7_20190809.pt', map_location=torch.device('cpu')))
    model_fair_7 = model_fair_7.to(device)
    model_fair_7.eval()

    trans = transforms.Compose([
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])

    face_names = []
    gender_scores_fair = []
    age_scores_fair = []
    gender_preds_fair = []
    age_preds_fair = []

    image = trans(img_cropped)
    image = image.view(1, 3, 224, 224)  # reshape image to match model dimensions (1 batch size)
    image = image.to(device)

    # fair 7 class
    outputs = model_fair_7(image)
    outputs = outputs.cpu().detach().numpy()
    outputs = np.squeeze(outputs)

    gender_outputs = outputs[7:9]
    age_outputs = outputs[9:18]

    gender_score = np.exp(gender_outputs) / np.sum(np.exp(gender_outputs))
    age_score = np.exp(age_outputs) / np.sum(np.exp(age_outputs))

    gender_pred = np.argmax(gender_score)
    age_pred = np.argmax(age_score)

    gender_scores_fair.append(gender_score)
    age_scores_fair.append(age_score)

    gender_preds_fair.append(gender_pred)
    age_preds_fair.append(age_pred)

    result = pd.DataFrame([gender_preds_fair,
                           age_preds_fair]).T

    result.columns = ['gender_preds_fair',
                      'age_preds_fair']
    # gender
    result.loc[result['gender_preds_fair'] == 0, 'gender'] = 'Male'
    result.loc[result['gender_preds_fair'] == 1, 'gender'] = 'Female'

    # age
    result.loc[result['age_preds_fair'] == 0, 'age'] = '0-2'
    result.loc[result['age_preds_fair'] == 1, 'age'] = '3-9'
    result.loc[result['age_preds_fair'] == 2, 'age'] = '10-19'
    result.loc[result['age_preds_fair'] == 3, 'age'] = '20-29'
    result.loc[result['age_preds_fair'] == 4, 'age'] = '30-39'
    result.loc[result['age_preds_fair'] == 5, 'age'] = '40-49'
    result.loc[result['age_preds_fair'] == 6, 'age'] = '50-59'
    result.loc[result['age_preds_fair'] == 7, 'age'] = '60-69'
    result.loc[result['age_preds_fair'] == 8, 'age'] = '70+'

    return [result['gender'][0],result['age'][0]]   
    
def predict(image):
  predictions = pipeline(image)
  return "A " + predictions[0] + " in the age range of " + predictions[1]

gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload a profile picture of a single person", type="pil"),
    outputs=("text"),
    title="Estimate age and gender from profile picture",
    examples=["ex1.jpg"]
).launch()