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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,69 @@
+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
+
+# Load the trained model
+model = load_model('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.")
+

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b1dea5dd297c98e8d9303fd7fa13086a42ff26943d2050b6514d7a816b6cf67
+size 4137733

requirements.txt

@@ -0,0 +1,4 @@
+streamlit==1.17.0
+pandas==2.1.0
+tensorflow==2.14.0
+nltk==3.8.1