Create app.py

app.py

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+import gradio as gr
+
+# Specify the filename for your PKL file
+filename = 'sentiment_model.pkl'
+
+# Open the file in write binary ('wb') mode and save the model using pickle.dump()
+with open(filename, 'wb') as file:
+    pickle.dump(nb_classifier, file)
+
+def predict_sentiment(text_input):
+    text_vector = vectorizer.transform([text])
+    prediction = nb_classifier.predict(text_vector)
+    return "Positive" if prediction == 1 else "Negative"
+
+# Test sample
+
+with gr.Blocks(theme="compact") as demo:
+    gr.Markdown("predict_sentiment")
+    #chatbot_window = gr.Chatbot(label="")
+    with gr.Row():
+        # Corrected 'lable' to 'label'
+        text_input = gr.Textbox(label="Write the Review", placeholder="Enter your sentiment", show_label=False)
+        # Create a Textbox to display the output
+        output_box = gr.Textbox(label="Sentiment Prediction")
+        # Submit the input to the function and display the output in the output_box
+        text_input.submit(fn=predict_sentiment, inputs=text_input, outputs=output_box)
+demo.launch(share=True