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| import streamlit as st | |
| from transformers import pipeline | |
| from datasets import Dataset, DatasetDict, load_dataset | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification,AutoConfig | |
| from datasets import load_metric | |
| from transformers import TrainingArguments, Trainer | |
| def sentiment(comment): | |
| model = pipeline("text-classification", model="JethroDD/twitter-roberta-base-sentiment-CustomModel_imdb") | |
| outputs = model(comment) | |
| result_text =outputs[0]["label"] | |
| if result_text == "LABEL_1": | |
| result_text = "Positive" | |
| elif result_text == "LABEL_0": | |
| result_text = "Negative" | |
| print(f"The predicted class is {result_text}") | |
| return result_text | |
| # TextSummary | |
| def txtsummary(text): | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| summary_text = summarizer(text)[0]["summary_text"] | |
| return summary_text | |
| # result2Audio | |
| def text2audio(result_text): | |
| speaker = pipeline("text-to-audio", model="Matthijs/mms-tts-eng") | |
| audio_data = speaker(result_text) | |
| return audio_data | |
| def main(): | |
| st.set_page_config(page_title="Movie Review Analysor", page_icon="🎬") | |
| st.header("Movie Review Analysor") | |
| text_box = st.text_input("Please paste your review here:",on_change=None) | |
| # text_box = "If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.<br /><br />One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).<br /><br />One might better spend one's time staring out a window at a tree growing.<br /><br />" | |
| if st.button("Analyse!"): | |
| # stage 1 | |
| st.text('Processing sentiment analysis...') | |
| sentiment_result = sentiment(text_box) | |
| st.write(sentiment_result) | |
| # stage 2 | |
| st.text('Summarying review...') | |
| summary_text = txtsummary(text_box) | |
| st.write(summary_text) | |
| # stage 3 | |
| final_text = "The sentiment of this review is {}. There is the summary of this comments: {}".format(sentiment_result,summary_text) | |
| audio_result = text2audio(final_text) | |
| st.audio(audio_result['audio'], | |
| format="audio/wav", | |
| start_time=0, | |
| sample_rate = audio_result['sampling_rate']) | |
| st.text('Press buttom to play result!') | |
| main() |