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import os
import streamlit as st
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import one_hot
import pickle
import emoji
# Set TensorFlow to use only CPU
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# Streamlit app title
st.title('Unveiling Sentiment: A Deep Dive into Sentiment Analysis 🐨')
# Function to load model and predict sentiment
def predict_sentiment(custom_data):
try:
# Load the trained model
model_path = 'sentiment_analysis_model.h5'
if not os.path.exists(model_path):
st.error(f"Model file not found: {model_path}")
return None
model = load_model(model_path)
# Load the one-hot encoding information
one_hot_info_path = 'one_hot_info_1.pkl'
if not os.path.exists(one_hot_info_path):
st.error(f"One-hot info file not found: {one_hot_info_path}")
return None
with open(one_hot_info_path, 'rb') as handle:
one_hot_info = pickle.load(handle)
vocab_size = one_hot_info['vocab_size']
max_len = one_hot_info['max_len']
# Define labels with emojis
labels_with_emojis = {
'Positive': '😊',
'Neutral': '😐',
'Negative': '😔'
}
# One-hot encode each tweet
one_hot_texts = [one_hot(text, vocab_size) for text in custom_data]
# Pad the sequences
padded_texts = pad_sequences(one_hot_texts, padding='pre', maxlen=max_len)
# Predict the sentiments for all tweets
predictions = model.predict(np.array(padded_texts))
# Convert predictions to class labels and probabilities
predicted_sentiments = []
for prediction in predictions:
sentiment = np.argmax(prediction)
sentiment_label = list(labels_with_emojis.keys())[sentiment]
sentiment_emoji = labels_with_emojis[sentiment_label]
sentiment_probabilities = {label: round(prob, 4) for label, prob in zip(labels_with_emojis.keys(), prediction)}
predicted_sentiments.append((sentiment_label, sentiment_emoji, sentiment_probabilities))
return predicted_sentiments
except Exception as e:
st.error(f"Error during prediction: {e}")
return None
# Streamlit UI
user_input = st.text_area("Please enter the tweet you'd like analyzed 🐋")
if st.button('Analyze'):
if user_input.strip(): # Check if input is not empty
# Remove emojis and replace with their description
user_input = emoji.demojize(user_input)
# Split input by newlines to handle multiple tweets
tweets = user_input.split('\n')
# Predict sentiment for custom data
predicted_sentiments = predict_sentiment(tweets)
if predicted_sentiments is not None:
# Display results for each tweet
st.write("## Predicted Sentiments:")
for i, (sentiment_label, sentiment_emoji, sentiment_probabilities) in enumerate(predicted_sentiments):
st.write(f"Tweet {i+1}: {sentiment_label} {sentiment_emoji}")
st.write("Probabilities:")
for label, prob in sentiment_probabilities.items():
st.write(f"{label}: {prob:.4f}")
else:
st.write("Please enter tweet(s) to analyze.")
# Add some Streamlit visual effects
st.snow()
st.balloons()
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