software engineer draft

app.py

@@ -5,29 +5,17 @@ import tensorflow as tf
 model = tf.keras.models.load_model("sentimentality.h5")
 
 def predict_sentiment(text):
-    """
-    This function takes a text input and returns the predicted sentiment using a saved model.
+    # preprocess input text
+    processed_text = preprocess(text)
     
-    Parameters:
-    text (str): The input text to be analyzed
+    # predict sentiment
+    prediction = model.predict([processed_text])[0][0]
+    sentiment = 'positive' if prediction >= 0.5 else 'negative'
     
-    Returns:
-    str: The predicted sentiment of the input text (either "positive" or "negative")
-    """
-    # Preprocess the input text
-    text = preprocess_text(text)
-    
-    # Make the prediction using the loaded model
-    prediction = model.predict([text])[0][0]
-    
-    # Return the predicted sentiment
-    if prediction > 0.5:
-        return "positive"
-    else:
-        return "negative"
+    return sentiment
 
-# Create the Gradio interface
-iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs="text")
+iface = gr.Interface(fn=predict_sentiment, 
+                     inputs=gr.inputs.Textbox(label='Input Text'), 
+                     outputs=gr.outputs.Label(label='Sentiment Prediction'))
 
-# Run the interface
 iface.launch()