Update app.py

app.py

@@ -1,37 +1,30 @@
-#Import required libraries
-import pickle
 import gradio as gr
-import gradio.inputs
-import pandas as pd
-import numpy as np
 import tensorflow as tf 
 from tensorflow.keras.preprocessing.sequence import pad_sequences
-from huggingface_hub.keras_mixin import from_pretrained_keras
-
-#Loading the tokenizer
-with open('tokenizer.pickle', 'rb') as f:
-    tokenizer = pickle.load(f)
-   
-
-
-def predict_sentiment(text):
-    sentiment = ["I guess, I liked the movie, but I'm not sure it's my favorite."]
-    sequence_test = tokenizer.texts_to_sequences([text])
-    padded_test = pad_sequences(sequence_test, maxlen= 64)
-    text=padded_test
-    model = from_pretrained_keras("keras-io/bidirectional-lstm-imdb")
-    X = [text for _ in range(len(model.input))]
-    a=model.predict(X)
-    return sentiment[np.around(a, decimals=0).argmax(axis=1)[0]]
-description = "Provide an opinion regarding a movie as input and this app will suggest what the underlying sentiment is. "
+import pickle
+from huggingface_hub import from_pretrained_keras
 
+model = from_pretrained_keras("keras-io/bidirectional-lstm-imdb")
 
-#Gradio app     
-iface = gr.Interface(predict_sentiment,
-                     inputs= gradio.inputs.Textbox( lines=1, placeholder=None, default="", label=None),
-                     outputs='text',
-                     title="Sentiment Analysis of Movie Reviews",
-                     description=description,
-                     theme="grass")
-iface.launch(enable_queue = True, inline=False, share = True)
+with open('tokenizer.pickle', 'rb') as file:
+    tokenizer = pickle.load(file)
     
+def decide(text):
+  tokenized_text = tokenizer.texts_to_sequences([text])
+  padded_tokens = pad_sequences(tokenized_text, maxlen= 200)
+  result = model.predict(padded_tokens, verbose=0)
+  if result[:] < 0.5 :
+    output = "negative"
+  else:
+    output = "positive"
+  return output
+  
+example_sentence_1 = "I hate the movie, they made no effort in making the movie. Waste of time!"
+example_sentence_2 = "Awesome movie! Loved the way in which the hero acted."
+examples = [[example_sentence_1], [example_sentence_2]]
+  
+description = "Write out a movie review to know the sentiment."
+
+gr.Interface(decide, inputs= gr.inputs.Textbox( lines=1, placeholder=None, default="", label=None), outputs='text', examples=examples,
+             title="Sentiment analysis of movie reviews",description=description, allow_flagging="auto", 
+             flagging_dir='flagging records').launch(inline=False, share = True)