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  1. Trained_Model.h5 +3 -0
  2. app.py +57 -0
  3. requirements.txt +6 -0
Trained_Model.h5 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f7550046da07e357f41c5fc35788c86a057d80db494bb44b4ad86cf1cdd1db0d
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+ size 63471736
app.py ADDED
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+ import gradio as gr
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+ import cv2
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+ import tensorflow as tf
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+ import numpy as np
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+ from tensorflow.keras.preprocessing.image import ImageDataGenerator
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+ from tensorflow.keras.models import load_model
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+
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+ # Load the pre-trained model
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+ model = tf.keras.models.load_model('Trained_Model.h5')
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+
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+ # Define the emotion labels
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+ emotion_labels = {
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+ 0: 'Angry',
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+ 1: 'Disgust',
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+ 2: 'Fear',
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+ 3: 'Happy',
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+ 4: 'Neutral',
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+ 5: 'Sad',
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+ 6: 'Surprise'
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+ }
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+
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+ # Create the image generator for preprocessing
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+ img_gen = ImageDataGenerator(rescale=1./255)
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+
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+ # Define the function to predict emotions
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+ def predict_emotion(file):
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+ # Load the image or video
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+ cap = cv2.VideoCapture(file.name)
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+ if cap.isOpened():
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+ ret, frame = cap.read()
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+ # Check if it's an image or video
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+ if frame is not None:
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+ # Preprocess the image
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+ img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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+ img = cv2.resize(img, (48, 48))
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+ img = np.expand_dims(img, axis=-1)
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+ img = np.expand_dims(img, axis=0)
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+ img = img.astype('float32')
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+ img = img_gen.standardize(img)
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+ # Predict the emotion
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+ prediction = model.predict(img)
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+ label = emotion_labels[np.argmax(prediction)]
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+ else:
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+ label = "No frames found in the video"
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+ else:
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+ label = "Could not open the file"
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+ return label
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+
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+ # Create the Gradio interface
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+ input_type = gr.inputs.File(label="Upload an image or video to predict emotions")
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+ output_type = gr.outputs.Textbox(label="Predicted emotion")
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+ title = "Emotion Detection"
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+ description = "Upload an image or video to predict the corresponding emotion"
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+ iface = gr.Interface(fn=predict_emotion, inputs=input_type, outputs=output_type, title=title, description=description)
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+ if __name__ == '__main__':
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+ iface.launch(inline=False)
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+
requirements.txt ADDED
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+ tensorflow
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+ keras
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+ opencv-python
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+ numpy
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+ matplotlib
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+ gradio