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from deepface import DeepFace
import pandas as pd
import gradio as gr
import matplotlib.pyplot as plt
import tempfile
import os
def faceAnalyzer(image_path):
def analyze(image_path, attribute):
analysis = DeepFace.analyze(img_path=image_path, actions=['gender', 'race', 'emotion', 'age'])
df = pd.DataFrame(analysis[0])
plot = df[attribute].plot(kind='line', figsize=(9, 5), title=attribute).get_figure()
_, temp_filename = tempfile.mkstemp(suffix=".png")
plot.savefig(temp_filename, dpi=600)
plt.close(plot)
return temp_filename
attributes = ['gender', 'race', 'emotion']
images = [analyze(image_path, attribute) for attribute in attributes]
return [gr.Image(image) for attribute, image in zip(attributes, images)]
def faceAnalyzer2(image_path, attribute):
analysis = DeepFace.analyze(img_path=image_path, actions=['age', 'gender', 'race', 'emotion'])
# convert the resulting dictionary to a DataFrame
df = pd.DataFrame(analysis[0])
if attribute == "gender":
gender = df['gender'].plot(kind = 'line', figsize = (9, 5), title = 'Gender').get_figure()
return gender
elif attribute == "race":
race = df['race'].plot(kind = 'line', figsize = (9, 5), title = 'Race').get_figure()
return race
elif attribute == "emotion":
emotion = df['emotion'].plot(kind = 'line', figsize = (9, 5), title = 'Emotion').get_figure()
return emotion
app1 = gr.Interface(faceAnalyzer,
inputs=gr.Image(label="Upload Photo"),
outputs=[gr.Image(label="Gender Analysis"),
gr.Image(label="Race Analysis"),
gr.Image(label="Emotion Analysis")],
theme=gr.themes.Soft())
app2 = gr.Interface(faceAnalyzer2,
inputs=[gr.Image(label="Upload Photo"),gr.Radio(choices=["gender","race","emotion"],
value="gender",
label="Attributes",
info="Select an attribute")],
outputs=gr.Plot(label="Analysis Output"),
theme=gr.themes.Soft())
application = gr.TabbedInterface([app1,app2],["Full Analysis","Select Analysis"],theme=gr.themes.Soft())
application.launch()
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