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age-gender-profilepic
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import gradio as gr

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

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

from torch_mtcnn import detect_faces
from torch_mtcnn import show_bboxes

def pipeline(img):
    bounding_boxes, landmarks = detect_faces(img)
    bb = [bounding_boxes[0,0], bounding_boxes[0,1], bounding_boxes[0,2], bounding_boxes[0,3]]
    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=["ex0.jpg","ex4.jpg", "ex1.jpg","ex2.jpg","ex3.jpg","ex5.jpg"]
).launch()