Update app.py

app.py

@@ -1,79 +1,93 @@
-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.")
+import os
+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
+
+# Set TensorFlow to use only CPU
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+# Streamlit app title
+st.title('Unveiling Sentiment: A Deep Dive into Sentiment Analysis 🐨')
+
+# Function to load model and predict sentiment
+def predict_sentiment(custom_data):
+    try:
+        # Load the trained model from the new format
+        model_path = 'saved_model_format'
+        if not os.path.exists(model_path):
+            st.error(f"Model file not found: {model_path}")
+            return None
+        model = load_model(model_path)
+        st.write("Model loaded successfully.")
+        
+        # Load the one-hot encoding information
+        one_hot_info_path = 'one_hot_info_1.pkl'
+        if not os.path.exists(one_hot_info_path):
+            st.error(f"One-hot info file not found: {one_hot_info_path}")
+            return None
+        with open(one_hot_info_path, 'rb') as handle:
+            one_hot_info = pickle.load(handle)
+        st.write("One-hot info loaded successfully.")
+
+        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 🐋")
+
+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.")