app.py

import gradio as gr
import numpy as np
import urllib
import cv2
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model

# Load the pre-trained face detection model
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

# Load the pre-trained model
model = load_model('my_model.h5')
def classify_image(img):
 #print("image : ",type(img))
 #image_array = img_to_array(image)
 #print("image shape: ",img.shape)
 img_copy = img
 height, width = img_copy.shape[0], img_copy.shape[1]

 img_copy = cv2.resize(img_copy, (500, 500))
 # Convert the image to grayscale
 gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)

 # Detect faces in the image
 faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 # Check if any faces were detected
 if len(faces) > 0:
 #print("Human face detected in the image!")
 face_area_list = []
 # Draw rectangles around the detected faces
 for (x, y, w, h) in faces:
 cv2.rectangle(img_copy, (x, y), (x+w, y+h), (0, 255, 0), 2)
 area = w * h
 face_area_list.append(area)
 #print(sorted(face_area_list))
 big_face_area = sorted(face_area_list)[-1]
 img_area = img_copy.shape[0] * img_copy.shape[1]
 #print(big_face_area)
 #print(img_area)
 #print("% Area of the largest face in image:", (big_face_area/img_area)*100)
 perc_area = (big_face_area/img_area)*100
 if perc_area>7.5:
 img = image.img_to_array(img)
 img = np.expand_dims(img, axis=0)
 img /= 255.0
 
 # Use the model to make a prediction
 prediction = model.predict(img)[0]
 # Map the predicted class to a label
 dic = {'SFW': float(prediction[1]), 'NSFW': float(prediction[0])}
 else :
 dic = {'SFW': float(0), 'NSFW': float(1)}
 # Display the image with the detected faces
 #cv2_imshow(img)
 #cv2.imshow('Detected faces', img_copy)
 #cv2.waitKey(0)
 #cv2.destroyAllWindows()
 # Preprocess the input image
 
 else:
 dic = {'SFW': float(0), 'NSFW': float(1)}
 #print("No human face detected in the image.")
 
 return dic

def classify_url(url):
 # Load the image from the URL
 response = urllib.request.urlopen(url)
 img = image.load_img(response, target_size=(224, 224))
 
 return classify_image(img)


# Define the GRADIO output interface
examples = [f"example{i}.jpg" for i in range(1,9)]
# Define the GRADIO app
app = gr.Interface(classify_image, gr.Image(shape=(224, 224)), outputs="label", allow_flagging="never", examples = examples,title="NSFW/SFW Classifier")

# Start the GRADIO app
app.launch()