codex codes

app.py

@@ -1,41 +1,33 @@
 import gradio as gr
-import numpy as np
 import tensorflow as tf
-from tensorflow.keras.datasets import imdb
 
-word_to_index = imdb.get_word_index()
+# Load the saved model
+model = tf.keras.models.load_model("sentimentality.h5")
 
-# Load the pre-trained model from file
-loaded_model = tf.keras.models.load_model('sentimentality.h5')
-
-# Define a function to make predictions
-def predict_sentiment(user_input, number_of_words, words_per_view):
-    # Encode the input text
-    words = user_input.split()
-    encoded_word = np.zeros(words_per_view).astype(int)
-    encoded_word[words_per_view - len(words) - 1] = 1
-    for i, word in enumerate(words):
-        index = words_per_view - len(words) + i
-        encoded_word[index] = word_to_index.get(word, 0) + 3
-    encoded_word = np.expand_dims(encoded_word, axis=0)
+def predict_sentiment(text):
+    """
+    This function takes a text input and returns the predicted sentiment using a saved model.
+    
+    Parameters:
+    text (str): The input text to be analyzed
+    
+    Returns:
+    str: The predicted sentiment of the input text (either "positive" or "negative")
+    """
+    # Preprocess the input text
+    text = preprocess_text(text)
     
-    # Make the prediction
-    prediction = loaded_model.predict(encoded_word)[0][0]
+    # Make the prediction using the loaded model
+    prediction = model.predict([text])[0][0]
     
-    # Return the sentiment label
+    # Return the predicted sentiment
     if prediction > 0.5:
-        return "Positive"
+        return "positive"
     else:
-        return "Negative"
+        return "negative"
 
 # Create the Gradio interface
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=["textbox", gr.inputs.NumberRange(minimum=1, maximum=10000, default=3000, label="Number of words"), gr.inputs.NumberRange(minimum=1, maximum=100, default=30, label="Words per view")],
-    outputs="text",
-    title="Sentiment Analysis",
-    description="Enter a text and get the sentiment prediction"
-)
+iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs="text")
 
-# Launch the interface
-iface.launch()
+# Run the interface
+iface.launch()