Upload 4 files

.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
+lstm.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,102 @@
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, Dropout, Dense
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import numpy as np
+import pickle
+
+# Global configuration for text processing
+max_sequence_length = 100  # Maximum length of input sequences
+embedding_dim = 100       # Dimension of word embeddings
+
+def create_model(vocab_size):
+    """
+    Creates a Bidirectional LSTM model for sentiment analysis
+    
+    Args:
+        vocab_size: Size of the vocabulary (number of unique words + 1)
+    
+    Returns:
+        Compiled Keras model
+    """
+    model = Sequential([
+        Input(shape=(max_sequence_length,)),
+        Embedding(input_dim=vocab_size, output_dim=embedding_dim),  # Word embedding layer
+        Bidirectional(LSTM(128, return_sequences=False)),           # Bidirectional LSTM
+        Dropout(0.5),                                              # Dropout for regularization
+        Dense(64, activation='relu'),                              # Dense hidden layer
+        Dropout(0.5),                                              # Additional dropout
+        Dense(3, activation='softmax')                             # Output layer (3 classes)
+    ])
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+@st.cache_resource
+def load_model_and_tokenizer():
+    """
+    Loads the pretrained model and tokenizer
+    
+    Returns:
+        tuple: (model, tokenizer)
+    """
+    # Load the tokenizer from pickle file
+    with open('tokenizer.pickle', 'rb') as handle:
+        tokenizer = pickle.load(handle)
+    
+    # Create and load model weights
+    vocab_size = len(tokenizer.word_index) + 1
+    model = create_model(vocab_size)
+    model.load_weights('lstm.keras')
+    return model, tokenizer
+
+def preprocess_text(text, tokenizer):
+    """
+    Preprocesses input text for model prediction
+    
+    Args:
+        text: Input text string
+        tokenizer: Keras tokenizer object
+    
+    Returns:
+        Padded sequence ready for model input
+    """
+    sequences = tokenizer.texts_to_sequences([text])
+    return pad_sequences(sequences, maxlen=max_sequence_length)
+
+def main():
+    """Main function for the Streamlit app"""
+    st.title("Sentiment Analyzer")
+    
+    try:
+        # Load model and tokenizer
+        model, tokenizer = load_model_and_tokenizer()
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return
+    
+    # Text input area
+    text = st.text_area("Enter text to analyze:", height=150)
+    
+    if st.button("Analyze"):
+        if text:
+            # Process input and make prediction
+            processed_text = preprocess_text(text, tokenizer)
+            prediction = model.predict(processed_text)
+            sentiments = ['Negative', 'Neutral', 'Positive']
+            result = sentiments[np.argmax(prediction)]
+            
+            # Display results
+            st.write(f"Detected sentiment: **{result}**")
+            
+            # Show probability distribution
+            probabilities = prediction[0]
+            for sent, prob in zip(sentiments, probabilities):
+                st.progress(float(prob))
+                st.write(f"{sent}: {prob:.2%}")
+        else:
+            st.warning("Please enter text to analyze.")
+
+if __name__ == "__main__":
+    main()

lstm.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:220e8b73da07021e2f69a0f30b727203550f516d22fec5b4a6dc77fb23eb00f2
+size 134187206

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+tensorflow
+numpy

tokenizer.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d5fbd6c4709795b7258e837f7c96bb5265e03e747f5b741c178c213ebf2175b
+size 4803481