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import gradio as gr
import cv2
import numpy as np
from keras.models import load_model
# Load face cascade
face_classifier = cv2.CascadeClassifier(
cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)
# Load emotion model
classifier = load_model("Custom_CNN_model.keras")
emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
def detect_emotion(image):
if image is None:
return None
img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.1, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 255), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_gray = cv2.resize(roi_gray, (48, 48))
roi = roi_gray.astype("float32") / 255.0
roi = np.expand_dims(roi, axis=0)
roi = np.expand_dims(roi, axis=-1)
prediction = classifier.predict(roi)[0]
label = emotion_labels[prediction.argmax()]
cv2.putText(img, label, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
with gr.Blocks() as app:
gr.Markdown(
"""
<h1 style='text-align:center; color:#2D99FF;'>Emotion Detection App</h1>
<p style='text-align:center;'>Upload an image or use your webcam to detect emotions using a custom CNN model.</p>
"""
)
with gr.Row():
with gr.Column():
input_img = gr.Image(type="numpy", label="Upload Image")
webcam = gr.Image(type="numpy", sources=["webcam"], label="Webcam")
run_btn = gr.Button("Detect Emotion")
with gr.Column():
output_img = gr.Image(type="numpy", label="Result")
run_btn.click(fn=detect_emotion, inputs=input_img, outputs=output_img)
webcam.change(fn=detect_emotion, inputs=webcam, outputs=output_img)
app.launch()
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