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app.py

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+import streamlit as st
+from process import preprocess_text, Get_sentiment
+
+def analyze_sentiment(text):
+    text = preprocess_text(text)
+# print(Review)
+    result = Get_sentiment(text)
+    return result
+
+
+def main():
+    st.title("Sentiment Analysis App")
+    st.write("Enter text below for sentiment analysis:")
+
+    # Text input
+    text_input = st.text_area("Input Text")
+
+    # Button to trigger sentiment analysis
+    if st.button("Analyze"):
+        if text_input:
+            sentiment = analyze_sentiment(text_input)
+            st.write("Sentiment:", sentiment[0])
+        else:
+            st.write("Please enter some text.")
+
+if __name__ == "__main__":
+    main()

process.py

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+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import string
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import tensorflow as tf
+
+# Download NLTK resources (one-time step)
+nltk.download('punkt')
+nltk.download('stopwords')
+
+# Define stopwords and punctuation
+stop_words = set(stopwords.words('english'))
+punctuations = set(string.punctuation)
+
+# Function to preprocess text
+def preprocess_text(text):
+    text = str(text)
+    # Lowercase the text
+    text = text.lower()
+    # Tokenize the text
+    tokens = word_tokenize(text)
+    # Remove stopwords and punctuation
+    tokens = [token for token in tokens if token not in stop_words and token not in punctuations]
+    # Reconstruct the text
+    preprocessed_text = ' '.join(tokens)
+    return preprocessed_text
+
+bert_tokenizer = BertTokenizer.from_pretrained('E:\jupyter\internship assesment\Techdome\Tokenizer')
+ 
+# Load model
+bert_model = TFBertForSequenceClassification.from_pretrained('E:\jupyter\internship assesment\Techdome\Model')
+label = {
+	1: 'positive',
+	0: 'Negative'
+}
+
+def Get_sentiment(Review, Tokenizer=bert_tokenizer, Model=bert_model):
+	# Convert Review to a list if it's not already a list
+	if not isinstance(Review, list):
+		Review = [Review]
+
+	Input_ids, Token_type_ids, Attention_mask = Tokenizer.batch_encode_plus(Review,
+																			padding=True,
+																			truncation=True,
+																			max_length=128,
+																			return_tensors='tf').values()
+	prediction = Model.predict([Input_ids, Token_type_ids, Attention_mask])
+
+	# Use argmax along the appropriate axis to get the predicted labels
+	pred_labels = tf.argmax(prediction.logits, axis=1)
+
+	# Convert the TensorFlow tensor to a NumPy array and then to a list to get the predicted sentiment labels
+	pred_labels = [label[i] for i in pred_labels.numpy().tolist()]
+	return pred_labels

requirements.txt

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+tensorflow
+transformers
+scikit-learn
+streamlit
+nltk