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app.py

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+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import one_hot
+import pickle
+import emoji
+
+# Streamlit app title
+st.title('Unveiling Sentiment A Deep Dive into Sentiment Analysis :koala:')
+
+# Function to load model and predict sentiment
+def predict_sentiment(custom_data):
+    try:
+        # Load the trained model
+        model = load_model('sentiment_analysis_model.h5')
+
+        # Load the one-hot encoding information
+        with open('one_hot_info_1.pkl', 'rb') as handle:
+            one_hot_info = pickle.load(handle)
+
+        vocab_size = one_hot_info['vocab_size']
+        max_len = one_hot_info['max_len']
+
+        # Define labels with emojis
+        labels_with_emojis = {
+            'Positive': '😊',
+            'Neutral': '😐',
+            'Negative': '😔'
+        }
+
+        # One-hot encode each tweet
+        one_hot_texts = [one_hot(text, vocab_size) for text in custom_data]
+
+        # Pad the sequences
+        padded_texts = pad_sequences(one_hot_texts, padding='pre', maxlen=max_len)
+
+        # Predict the sentiments for all tweets
+        predictions = model.predict(np.array(padded_texts))
+
+        # Convert predictions to class labels and probabilities
+        predicted_sentiments = []
+        for prediction in predictions:
+            sentiment = np.argmax(prediction)
+            sentiment_label = list(labels_with_emojis.keys())[sentiment]
+            sentiment_emoji = labels_with_emojis[sentiment_label]
+            sentiment_probabilities = {label: round(prob, 4) for label, prob in zip(labels_with_emojis.keys(), prediction)}
+            predicted_sentiments.append((sentiment_label, sentiment_emoji, sentiment_probabilities))
+
+        return predicted_sentiments
+
+    except Exception as e:
+        st.error(f"Error during prediction: {e}")
+        return None
+
+# Streamlit UI
+user_input = st.text_area("Please enter the tweet you'd like analyzed::whale:")
+
+if st.button('Analyze'):
+    if user_input.strip():  # Check if input is not empty
+        # Remove emojis and replace with their description
+        user_input = emoji.demojize(user_input)
+        
+        # Split input by newlines to handle multiple tweets
+        tweets = user_input.split('\n')
+        
+        # Predict sentiment for custom data
+        predicted_sentiments = predict_sentiment(tweets)
+
+        if predicted_sentiments is not None:
+            # Display results for each tweet
+            st.write("## Predicted Sentiments:")
+            for i, (sentiment_label, sentiment_emoji, sentiment_probabilities) in enumerate(predicted_sentiments):
+                st.write(f"Tweet {i+1}: {sentiment_label} {sentiment_emoji}")
+                st.write("Probabilities:")
+                for label, prob in sentiment_probabilities.items():
+                    st.write(f"{label}: {prob:.4f}")
+    else:
+        st.write("Please enter tweet(s) to analyze.")

one_hot_info_1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1d34f42c0fb38af36bddc4c84d3c8eba4adebca82c91ab1f813fd0d351eacc20
+size 44

sentiment_analysis_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:57343b58f31bacf0b3e699ad590c3dff23e6cf50622d01a8582e282da223939b
+size 55246272