fn8

app.py

@@ -1,14 +1,5 @@
 import gradio as gr
 import tensorflow as tf
-import numpy as np
-
-# Define a function to preprocess the text input
-def preprocess(text):
-    tokenizer = tf.keras.preprocessing.text.Tokenizer()
-    tokenizer.fit_on_texts([text])
-    text = tokenizer.texts_to_sequences([text])
-    text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=500, padding='post', truncating='post')
-    return text
 
 # Load the pre-trained model
 model = tf.keras.models.load_model('sentimentality.h5')
@@ -16,33 +7,27 @@ model = tf.keras.models.load_model('sentimentality.h5')
 # Define a function to make a prediction on the input text
 def predict_sentiment(text):
     # Preprocess the text
-    text = preprocess(text)
+    tokenizer = tf.keras.preprocessing.text.Tokenizer()
+    tokenizer.fit_on_texts([text])
+    text = tokenizer.texts_to_sequences([text])
+    text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=500, padding='post', truncating='post')
     # Make a prediction using the loaded model
     proba = model.predict(text)[0]
     # Normalize the probabilities
     proba /= proba.sum()
-    # Get the predicted sentiment label
-    sentiment_label = ['Positive', 'Negative', 'Neutral'][np.argmax(proba)]
-    # Determine the color based on the sentiment label
-    if sentiment_label == 'Positive':
-        color = '#2a9d8f'
-    elif sentiment_label == 'Negative':
-        color = '#e76f51'
-    else:
-        color = '#264653'
-    # Return the sentiment label and color
-    return {'label': sentiment_label, 'color': color}
+    # Return the probability distribution
+    return {"Positive": float(proba[0]), "Negative": float(proba[1]), "Neutral": float(proba[2])}
 
-# Define the Gradio interface
+# Create a Gradio interface
 iface = gr.Interface(
     fn=predict_sentiment,
-    inputs=gr.inputs.Textbox(label='Enter text here'),
-    outputs=gr.outputs.Label(label='Sentiment', value='Neutral', 
-                             font_size=30, font_family='Arial', 
-                             background_color='#f8f8f8', 
-                             color='black'),
-    title='SENTIMENT ANALYSIS'
+    inputs=gr.inputs.Textbox(label="Enter text here", lines=5, placeholder="Type here to analyze sentiment..."),
+    outputs=gr.outputs.Label(label="Sentiment", default="Neutral", font_size=30)
 )
 
+# Add the possible classes to the output plot
+classes = ["Positive", "Negative", "Neutral"]
+iface.outputs[0].choices = classes
+
 # Launch the interface
 iface.launch()