Create app.py

app.py

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+import tensorflow as tf
+from tensorflow.keras import layers, models
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import gradio as gr
+
+# Load the IMDb dataset
+imdb = tf.keras.datasets.imdb
+vocab_size = 10000
+maxlen = 100
+(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocab_size)
+X_train = pad_sequences(X_train, maxlen=maxlen)
+X_test = pad_sequences(X_test, maxlen=maxlen)
+
+# Define the model
+model = models.Sequential([
+    layers.Embedding(vocab_size, 16, input_length=maxlen),
+    layers.GlobalAveragePooling1D(),
+    layers.Dense(16, activation='relu'),
+    layers.Dense(1, activation='sigmoid')
+])
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+model.fit(X_train, y_train, epochs=10, batch_size=512, validation_data=(X_test, y_test), verbose=1)
+
+# Save the model
+model.save("sentiment_analysis_model.h5")
+
+# Function to predict sentiment
+def predict_sentiment(text):
+    tokenizer = Tokenizer(num_words=vocab_size)
+    tokenizer.fit_on_texts([text])
+    sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequence, maxlen=maxlen)
+    
+    prediction = model.predict(padded_sequence)[0][0]
+    sentiment = "Positive" if prediction >= 0.5 else "Negative"
+    confidence = round(prediction, 4)
+    
+    return sentiment, confidence
+
+# Gradio Interface
+def gradio_predict(text):
+    sentiment, confidence = predict_sentiment(text)
+    return f"Sentiment: {sentiment}, Confidence: {confidence:.4f}"
+
+# Create Gradio Interface
+interface = gr.Interface(fn=gradio_predict, 
+                         inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."), 
+                         outputs="text",
+                         title="Sentiment Analysis",
+                         description="Enter a movie review or any text to analyze its sentiment.")
+
+# Launch the Gradio Interface
+interface.launch()