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import cv2
import numpy as np
from keras.models import load_model
import gradio as gr
# Load pre-trained model
model = load_model("emotion_detection_model.h5")
# Load OpenCV's pre-trained face detector
face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)
# Define emotion labels
EMOTIONS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
# Function to detect and display emotions on faces
def detect_emotions(image):
frame = image.copy()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for x, y, w, h in faces:
face_roi = gray[y : y + h, x : x + w]
resized_roi = cv2.resize(face_roi, (48, 48))
normalized_roi = resized_roi / 255.0
reshaped_roi = normalized_roi.reshape(-1, 48, 48, 1)
# Predict emotions
predictions = model.predict(reshaped_roi)
emotion_label = EMOTIONS[np.argmax(predictions)]
# Display emotion label on face
cv2.putText(
frame,
emotion_label,
(x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.9,
(36, 255, 12),
2,
)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
return frame
# Define Gradio interface
iface = gr.Interface(
fn=detect_emotions,
inputs="image",
outputs="image",
title="Emotion Detection",
description="Upload an Image to Detect Emotions in Faces",
)
# Run Gradio app
iface.launch(share=True)
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