patrickbdevaney
commited on
Commit
•
35a837e
1
Parent(s):
981a502
upload of demo to hf spaces
Browse files- app.py +42 -0
- index.html +35 -0
- requirements.txt +5 -0
- sentiment_analysis_model.h5 +3 -0
- tokenizer.pickle +3 -0
app.py
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import streamlit as st
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import joblib
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import numpy as np
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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import pickle
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# Load the model and tokenizer
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try:
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model = load_model('sentiment_analysis_model.h5')
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except Exception as e:
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st.error(f"Error loading model: {e}")
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try:
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with open('tokenizer.pickle', 'rb') as handle:
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tokenizer = pickle.load(handle)
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except Exception as e:
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st.error(f"Error loading tokenizer: {e}")
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# Define a function to predict the sentiment of input text
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def predict_sentiment(text):
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# Tokenize and pad the input text
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text_sequence = tokenizer.texts_to_sequences([text])
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text_sequence = pad_sequences(text_sequence, maxlen=100)
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# Make a prediction using the trained model
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predicted_rating = model.predict(text_sequence)[0]
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if np.argmax(predicted_rating) == 0:
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return 'Negative'
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elif np.argmax(predicted_rating) == 1:
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return 'Neutral'
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else:
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return 'Positive'
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st.title('Sentiment Analysis')
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comment = st.text_area('Enter your comment:')
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if st.button('Analyze Sentiment'):
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if 'model' in globals() and 'tokenizer' in globals():
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sentiment = predict_sentiment(comment)
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st.write(f'Sentiment: {sentiment}')
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else:
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st.error("Model or tokenizer not loaded properly.")
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index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Sentiment Analysis</title>
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</head>
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<body>
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<h1>Sentiment Analysis</h1>
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<form id="commentForm">
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<label for="comment">Enter your comment:</label><br>
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<textarea id="comment" name="comment" rows="4" cols="50"></textarea><br><br>
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<input type="submit" value="Analyze Sentiment">
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</form>
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<p id="result"></p>
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<script>
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document.getElementById('commentForm').addEventListener('submit', function(event) {
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event.preventDefault();
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const comment = document.getElementById('comment').value;
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fetch('/predict', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({ comment: comment }),
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})
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.then(response => response.json())
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.then(data => {
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document.getElementById('result').innerText = 'Sentiment: ' + data.sentiment;
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});
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});
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</script>
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</body>
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</html>
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requirements.txt
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streamlit
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tensorflow
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joblib
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numpy
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flask
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sentiment_analysis_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:f9ddfd21899724f70867f400fc4769f91072b25f095f8556712e24c0cf229f3e
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size 6448856
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tokenizer.pickle
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version https://git-lfs.github.com/spec/v1
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oid sha256:522f4e3a9e107b2f6e045ae4f90fadbb8055d21045ce42cbaa960b060a7b8b15
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size 2111402
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