another commit with custom model

app.py

@@ -1,3 +1,37 @@
+from tensorflow import keras
+import tensorflow as tf
+from tensorflow.keras.datasets import imdb
+import numpy as np
 import gradio as gr
 
-gr.Interface.load("models/finiteautomata/bertweet-base-sentiment-analysis",interpretation="default").launch()
+number_of_words = 3000
+words_per_view = 200
+
+loaded_model = tf.keras.models.load_model('sentimentality.h5')
+word_to_index = imdb.get_word_index()
+
+def get_predict(userInputString, model):
+  words = userInputString.split()
+  #print(len(words))
+  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)
+  prediction = model.predict(encoded_word)
+  return prediction
+
+def analyze_sentiment(userInputString):
+  result = get_predict(userInputString, loaded_model)[0][0]
+  if result > 0.5:
+    answer = 'positive review'
+  else: answer = 'negative review'  
+  return answer
+UserInputPage = gr.Interface(
+    fn=analyze_sentiment,
+    inputs = ["text"],
+    outputs=["text"]
+)
+tabbed_Interface = gr.TabbedInterface([UserInputPage], ["Check user input"])
+tabbed_Interface.launch()