File size: 2,340 Bytes
5bfed7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import pickle
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
import sys
# Constants
MAX_SEQUENCE_LENGTH = 100
def load_resources():
try:
# Load the model
model = load_model('emotion_model.h5')
# Load the tokenizer
with open('tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
# Load the label encoder classes
with open('label_encoder_classes.npy', 'rb') as f:
classes = np.load(f, allow_pickle=True)
return model, tokenizer, classes
except Exception as e:
print(f"Error loading resources: {e}")
return None, None, None
def predict_emotion(text, model, tokenizer, classes):
# Tokenize and pad the input text
sequences = tokenizer.texts_to_sequences([text])
data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
# Predict
prediction = model.predict(data)
predicted_class_index = np.argmax(prediction)
predicted_emotion = classes[predicted_class_index]
confidence = prediction[0][predicted_class_index]
return predicted_emotion, confidence
if __name__ == "__main__":
model, tokenizer, classes = load_resources()
if model and tokenizer and classes is not None:
if len(sys.argv) > 1:
# Command line mode
text = " ".join(sys.argv[1:])
emotion, confidence = predict_emotion(text, model, tokenizer, classes)
print(f"Input: {text}")
print(f"Predicted Entity: {emotion}")
print(f"Confidence: {confidence:.2f}")
else:
# Interactive mode
print("Model loaded successfully.")
print("Type a sentence to analyze its emotion (or 'quit' to exit).")
while True:
user_input = input("\nEnter text: ")
if user_input.lower() == 'quit':
break
emotion, confidence = predict_emotion(user_input, model, tokenizer, classes)
print(f"Predicted Entity: {emotion}")
print(f"Confidence: {confidence:.2f}")
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