ok , so my model was giving negative answer fro both good and bad , so now I ma testing another model.

app.py

@@ -39,7 +39,7 @@ with open('tokenizer.json', 'r', encoding='utf-8') as f:
 
 tokenizer = tokenizer_from_json(tokenizer_config)
 
-model = tf.keras.models.load_model("sentimentality.h5", custom_objects={'f1':f1, 'recall': recall, 'precision': precision, 'accuracy':accuracy})
+model = tf.keras.models.load_model("model_RNN.h5", custom_objects={'f1':f1, 'recall': recall, 'precision': precision, 'accuracy':accuracy})
 
 def get_sentiment(text):
     global model
@@ -51,8 +51,8 @@ def get_sentiment(text):
     text = [stemmer.lemmatize(word) for word in text]
     text = ' '.join(text)
     text = tokenizer.texts_to_sequences([text])[0]
-    text += [0] * (30 - len(text))
-    text = np.array(text).reshape(-1, 30)
+    text += [0] * (200 - len(text))
+    text = np.array(text).reshape(-1, 200)
     x = model.predict(text).tolist()[0][0]
     return ('Positive' if x >= 0.5 else 'negative') + ' sentiment!'