fn10

app.py

@@ -1,27 +1,38 @@
-import gradio as gr
 import tensorflow as tf
+import gradio as gr
 import numpy as np
 
-# Load the pre-trained model
-model = tf.keras.models.load_model('sentimentality.h5')
+# Load the custom model
+model = tf.keras.models.load_model("sentiment.h5")
 
-# 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')
+# Define a function to preprocess the input text
+def preprocess_text(text):
+    # Tokenize the text
+    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
+    tokenizer.fit_on_texts(text)
+    text = tokenizer.texts_to_sequences(text)
+    # Pad the sequences to a fixed length
+    max_len = 30
+    text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=max_len)
     return text
 
-def sentiment_analysis(text):
+# Define a function to get the sentiment scores from the model
+def get_sentiment_scores(text):
+    # Preprocess the text
+    text = preprocess_text(text)
+    # Get the sentiment scores from the model
     scores = model.predict(text)
-    return scores
+    # Postprocess the scores to obtain the sentiment label
+    label = "Positive" if np.round(scores) == 1 else "Negative"
+    return label
 
-iface = gr.Interface(
-    fn=sentiment_analysis, 
-    inputs=gr.Textbox(placeholder="Enter a positive or negative sentence here..."), 
-    outputs="label", 
-    interpretation="default",
-    examples=[["This is wonderful!"]])
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=get_sentiment_scores,
+    inputs=gr.inputs.Textbox(placeholder="Enter a positive or negative sentence here..."),
+    outputs=gr.outputs.Textbox(label="Sentiment Label"),
+    examples=[["This is wonderful!"], ["I hate this product."]]
+)
 
-iface.launch()
+# Launch the interface
+interface.launch()