| import numpy as np |
| import pickle |
| import streamlit as st |
| import tensorflow as tf |
| from transformers import AutoTokenizer,TFBertModel |
| with open('tokenizer.pickle', 'rb') as handle: |
| tokenizer = pickle.load(handle) |
| import transformers |
| model_load = tf.keras.models.load_model('optimized_model_2.2.h5', custom_objects={"TFBertModel": transformers.TFBertModel}) |
|
|
| encoded_dict={ |
| 'Negative':0, |
| 'Somewhat Negative':1, |
| 'Neutral':2, |
| 'Somewhat Positive':3, |
| 'Positive':4 |
| } |
| def get_max_ac(d): |
| return max(d, key = d.get) |
| def predict(model,tokenizer,input_text): |
| x_val = tokenizer( |
| text=input_text, |
| add_special_tokens=True, |
| max_length=70, |
| truncation=True, |
| padding='max_length', |
| return_tensors='tf', |
| return_token_type_ids = False, |
| return_attention_mask = True, |
| verbose = True) |
| output_dict={} |
| validation = model.predict({'input_ids':x_val['input_ids'],'attention_mask':x_val['attention_mask']})*100 |
| for key , value in zip(encoded_dict.keys(),validation[0]): |
| output_dict[key]=value |
| return output_dict |
| st.title('Sentiment Analysis on Movie Reviews! ! ') |
| st.info("This application aims to classify the movies reviews. Write a simple sentence (not more than 70 words).") |
| input_text = st.text_area('Enter Text Below (maximum 70 words):', height=100) |
| submit=st.button('Predict') |
| if submit: |
| st.subheader("Probabilities :") |
| with st.spinner(text="This may take a moment..."): |
| output=predict(model_load,tokenizer,input_text) |
| result=get_max_ac(output) |
| for key,probab in output.items(): |
| st.write(key,probab) |
| |
| st.subheader("Result :") |
| st.write('Movie Review is : ',result) |
|
|
