Add application file

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing.text import text_to_word_sequence
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from gensim.models import KeyedVectors
+from tensorflow.keras.models import load_model
+import gensim.downloader as api
+
+# Load the pre-trained Word2Vec model
+word2vec_transfer = api.load("glove-wiki-gigaword-100")
+
+# Define the function to embed a sentence with the pre-trained Word2Vec model
+def embed_sentence_with_TF(word2vec, sentence):
+    embedded_sentence = []
+    for word in sentence:
+        if word in word2vec:
+            embedded_sentence.append(word2vec[word])
+    return np.array(embedded_sentence)
+
+# Define the function to preprocess a new movie review
+def preprocess_review(review):
+    # Tokenize the review
+    review = text_to_word_sequence(review)
+    # Embed the review with the pre-trained Word2Vec model
+    review_embedded = embed_sentence_with_TF(word2vec_transfer, review)
+    # Pad the embedded review
+    review_padded = pad_sequences([review_embedded], dtype='float32', padding='post', maxlen=200)
+    return review_padded
+
+# Load the trained model
+model = load_model('my_model.h5')
+
+def predict_sentiment(review):
+    # Preprocess the review
+    review_padded = preprocess_review(review)
+    # Predict the sentiment
+    sentiment = model.predict(review_padded)[0][0]
+    if sentiment > 0.5:
+        return "Positive"
+    elif sentiment == 0.5:
+        return "Neutral"
+    else:
+        return "Negative"
+
+# Create a Gradio interface
+inputs = gr.inputs.Textbox(lines=5, label="Input Text")
+outputs = gr.outputs.Textbox(label="Sentiment")
+title = "Sentiment Analysis"
+description = "Enter a text and get the sentiment prediction."
+gr.Interface(fn=predict_sentiment, inputs=inputs, outputs=outputs, title=title, description=description).launch(share=True)