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sample_sentiment_analysis / src /streamlit_app.py

Update src/streamlit_app.py

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import streamlit as st
import pandas as pd
import re
import tensorflow as tf
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import load_model
import nltk

# Use /tmp for NLTK data (writable in Hugging Face Spaces)
nltk_data_dir = "/tmp/nltk_data"
nltk.data.path.append(nltk_data_dir)

# Download the stopwords and punkt resources
nltk.download('stopwords', download_dir=nltk_data_dir)
nltk.download('punkt', download_dir=nltk_data_dir)

# Load the trained model
model = load_model('src/model.keras')

# Load stopwords
stpwds_id = list(set(stopwords.words('english')))

# Text preprocessing function
def text_preprocessing(text):
    # Case folding
    text = text.lower()

    # Mention removal
    text = re.sub("@[A-Za-z0-9_]+", " ", text)

    # Hashtags removal
    text = re.sub("#[A-Za-z0-9_]+", " ", text)

    # Newline removal (\n)
    text = re.sub(r"\\n", " ",text)

    # Whitespace removal
    text = text.strip()

    # URL removal
    text = re.sub(r"http\S+", " ", text)
    text = re.sub(r"www.\S+", " ", text)

    # Non-letter removal (such as emoticons, symbols, etc.)
    text = re.sub("[^A-Za-z\s']", " ", text)

    # Tokenization
    tokens = word_tokenize(text)

    # Stopwords removal
    tokens = [word for word in tokens if word not in stpwds_id]

    # Combining Tokens
    text = ' '.join(tokens)

    return text

# Define the Streamlit interface
st.title('Sentiment Analysis App')

# Get user input
user_input = st.text_area("Enter the text for sentiment analysis:")

if st.button('Analyze'):
    if user_input:
        # Preprocess the input text
        processed_text = text_preprocessing(user_input)
        prediction = model.predict([[processed_text]])
        sentiment = "Positive" if prediction[0] > 0.5 else "Negative"

        # Display the result
        st.write(f"Sentiment: {sentiment}")
    else:
        st.write("Please enter some text.")

	import streamlit as st
	import pandas as pd
	import re
	import tensorflow as tf
	from nltk.corpus import stopwords
	from nltk.tokenize import word_tokenize
	from tensorflow.keras.preprocessing.text import Tokenizer
	from tensorflow.keras.preprocessing.sequence import pad_sequences
	from tensorflow.keras.models import load_model
	import nltk

	# Use /tmp for NLTK data (writable in Hugging Face Spaces)
	nltk_data_dir = "/tmp/nltk_data"
	nltk.data.path.append(nltk_data_dir)

	# Download the stopwords and punkt resources
	nltk.download('stopwords', download_dir=nltk_data_dir)
	nltk.download('punkt', download_dir=nltk_data_dir)

	# Load the trained model
	model = load_model('src/model.keras')

	# Load stopwords
	stpwds_id = list(set(stopwords.words('english')))

	# Text preprocessing function
	def text_preprocessing(text):
	# Case folding
	text = text.lower()

	# Mention removal
	text = re.sub("@[A-Za-z0-9_]+", " ", text)

	# Hashtags removal
	text = re.sub("#[A-Za-z0-9_]+", " ", text)

	# Newline removal (\n)
	text = re.sub(r"\\n", " ",text)

	# Whitespace removal
	text = text.strip()

	# URL removal
	text = re.sub(r"http\S+", " ", text)
	text = re.sub(r"www.\S+", " ", text)

	# Non-letter removal (such as emoticons, symbols, etc.)
	text = re.sub("[^A-Za-z\s']", " ", text)

	# Tokenization
	tokens = word_tokenize(text)

	# Stopwords removal
	tokens = [word for word in tokens if word not in stpwds_id]

	# Combining Tokens
	text = ' '.join(tokens)

	return text

	# Define the Streamlit interface
	st.title('Sentiment Analysis App')

	# Get user input
	user_input = st.text_area("Enter the text for sentiment analysis:")

	if st.button('Analyze'):
	if user_input:
	# Preprocess the input text
	processed_text = text_preprocessing(user_input)
	prediction = model.predict([[processed_text]])
	sentiment = "Positive" if prediction[0] > 0.5 else "Negative"

	# Display the result
	st.write(f"Sentiment: {sentiment}")
	else:
	st.write("Please enter some text.")