Update app.py

app.py

@@ -1,44 +1,52 @@
-import streamlit as st
-import numpy as np
-import pickle
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Tokenizer
-
-# Load the model, tokenizer, and label map
-model = load_model('sentiment_model.h5')
-
-with open('tokenizer.pkl', 'rb') as file:
-    tokenizer = pickle.load(file)
-
-with open('label_map.pkl', 'rb') as file:
-    label_map = pickle.load(file)
-
-# Define max length
-max_len = 100
-
-def preprocess_text(text, tokenizer, max_len):
-    sequence = tokenizer.texts_to_sequences([text])
-    padded_sequence = pad_sequences(sequence, maxlen=max_len)
-    return padded_sequence
-
-def predict_sentiment(text, model, tokenizer, max_len, label_map):
-    processed_text = preprocess_text(text, tokenizer, max_len)
-    prediction = model.predict(processed_text)
-    predicted_class = np.argmax(prediction, axis=1)[0]
-    predicted_label = label_map[predicted_class]
-    return predicted_label
-
-# Streamlit app
-st.title("Sentiment Analysis App")
-
-st.write("Enter a text to predict its sentiment:")
-
-text_input = st.text_area("Text", "Type your text here...")
-
-if st.button('Predict'):
-    if text_input:
-        predicted_sentiment = predict_sentiment(text_input, model, tokenizer, max_len, label_map)
-        st.write(f"The predicted sentiment for the text is: {predicted_sentiment}")
-    else:
-        st.write("Please enter some text.")
+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load the model and tokenizer
+model = load_model('sentiment_model.h5')
+with open('tokenizer.pkl', 'rb') as file:
+    tokenizer = pickle.load(file)
+with open('label_map.pkl', 'rb') as file:
+    label_map = pickle.load(file)
+
+def preprocess_text(text, tokenizer, max_len):
+    sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequence, maxlen=max_len)
+    return padded_sequence
+
+def predict_sentiment(text, model, tokenizer, max_len, label_map):
+    processed_text = preprocess_text(text, tokenizer, max_len)
+    prediction = model.predict(processed_text)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    predicted_label = label_map[predicted_class]
+    return predicted_label
+
+# Streamlit app
+def main():
+    st.title("Sentiment Analysis")
+    st.write("Enter a text to predict its sentiment.")
+
+    # Input text from user
+    input_text = st.text_area("Input Text", "Type your text here...")
+
+    if st.button("Predict Sentiment"):
+        if input_text:
+            max_len = 100  # Set this to the max length used during training
+            sentiment = predict_sentiment(input_text, model, tokenizer, max_len, label_map)
+            st.write(f"The predicted sentiment for the text is: **{sentiment}**")
+        else:
+            st.write("Please enter some text to analyze.")
+
+    st.header("Sample Texts")
+    st.write("<span style='color:green; font-weight:bold'>Positive:</span> Going to finish up Borderlands 2 today.", unsafe_allow_html=True)
+    st.write("<span style='color:yellow; font-weight:bold'>Neutral:</span> Check out this epic streamer", unsafe_allow_html=True)
+    st.write("<span style='color:red; font-weight:bold'>Negative:</span> The biggest disappointment of my life came a year ago.", unsafe_allow_html=True)
+    st.write("<span style='color:cyan; font-weight:bold'>Irrelevant:</span> Stupid 19-year-olds who write bad poetry need to get away from the computer and talk to real people who don't believe in vampires.", unsafe_allow_html=True)
+
+
+
+
+if __name__ == "__main__":
+    main()