Update app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import seaborn as sns
+from nltk.corpus import stopwords
+from wordcloud import WordCloud, STOPWORDS
+import re
+import nltk
+nltk.download('punkt')
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import LabelEncoder
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.initializers import Orthogonal
+
+# Load the dataset
+df = pd.read_csv("Twitter_Data.csv")
+
+# Load the model with custom objects
+custom_objects = {
+    'Orthogonal': Orthogonal,
+    # Add other custom objects if needed
+}
+
+model = load_model('sentiment_analysis_model.h5', custom_objects=custom_objects)
+
+# Preprocess the data
+def preprocess_text(text):
+    text = re.sub(r'http\S+', '', text)  # remove URLs
+    text = re.sub(r'[^A-Za-z0-9 ]+', '', text)  # remove special characters
+    text = text.lower()  # convert to lowercase
+    return text
+
+df['clean_text'] = df['text'].apply(preprocess_text)
+
+# Tokenization and padding
+tokenizer = Tokenizer()
+tokenizer.fit_on_texts(df['clean_text'])
+max_length = 100
+vocab_size = len(tokenizer.word_index) + 1
+
+def predict_sentiment(text):
+    text = preprocess_text(text)
+    sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequence, maxlen=max_length)
+    prediction = model.predict(padded_sequence)
+    sentiment = np.argmax(prediction)
+    sentiment_labels = {0: 'Negative', 1: 'Neutral', 2: 'Positive'}
+    return sentiment_labels[sentiment]
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="text",
+    title="Sentiment Analysis",
+    description="Enter text to predict its sentiment"
+)
+
+if __name__ == "__main__":
+    iface.launch()