changed gradio interface

app.py

@@ -2,29 +2,30 @@ import tensorflow as tf
 import gradio as gr
 import numpy as np
 
-# Load the custom model
-model = tf.keras.models.load_model("sentiment.h5")
+# Load the saved model
+model = load_model('sentimentality.h5')
 
-# Define a function to preprocess the input text
-def preprocess_text(text):
+# Define the function to preprocess the text
+def preprocess(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
+    sequences = tokenizer.texts_to_sequences(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)
-    # Postprocess the scores to obtain the sentiment label
-    label = "Positive" if np.round(scores) == 1 else "Negative"
-    return label
+    # Pad the sequences to a fixed length of 30
+    padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=30, padding='post')
+
+    return np.array(padded_sequences)
+
+# Define the function to make predictions on the text
+def sentiment_analysis(text):
+    preprocessed_text = preprocess([text])
+    prediction = model.predict(preprocessed_text)[0][0]
+    
+    if prediction >= 0.5:
+        return "positive"
+    else:
+        return "negative"
 
 # Define the Gradio interface
 interface = gr.Interface(