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import gradio as gr
import cv2
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import load_model
# Load the pre-trained model
model = tf.keras.models.load_model('Trained_Model.h5')
# Define the emotion labels
emotion_labels = {
0: 'Angry',
1: 'Disgust',
2: 'Fear',
3: 'Happy',
4: 'Neutral',
5: 'Sad',
6: 'Surprise'
}
# Create the image generator for preprocessing
img_gen = ImageDataGenerator(rescale=1./255)
# Define the function to predict emotions
def predict_emotion(file):
# Load the image or video
cap = cv2.VideoCapture(file.name)
if cap.isOpened():
ret, frame = cap.read()
# Check if it's an image or video
if frame is not None:
# Preprocess the image
img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (48, 48))
img = np.expand_dims(img, axis=-1)
img = np.expand_dims(img, axis=0)
img = img.astype('float32')
img = img_gen.standardize(img)
# Predict the emotion
prediction = model.predict(img)
label = emotion_labels[np.argmax(prediction)]
else:
label = "No frames found in the video"
else:
label = "Could not open the file"
return label
# Create the Gradio interface
input_type = gr.inputs.File(label="Upload an image or video to predict emotions")
output_type = gr.outputs.Textbox(label="Predicted emotion")
title = "Emotion Detection"
description = "Upload an image or video to predict the corresponding emotion"
iface = gr.Interface(fn=predict_emotion, inputs=input_type, outputs=output_type, title=title, description=description)
if __name__ == '__main__':
iface.launch(inline=False)
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