Spaces:
Build error
Build error
File size: 4,624 Bytes
3e7ec50 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
import streamlit as st
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import load_model
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import pickle
import joblib
# Load models and tokenizers
model = load_model('rnn_lstm_final.h5')
loaded_model = joblib.load("my_rnn_model.joblib")
with open("tokenizer_and_sequences.pkl", "rb") as f:
tokenizer, data = pickle.load(f)
model1 = AutoModelForSequenceClassification.from_pretrained('punjabiSentimentAnalysis')
tokenizer1 = AutoTokenizer.from_pretrained('punjabiSentimentAnalysis')
model_summ = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
tokenizer_summ = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS",
do_lower_case=False, use_fast=False, keep_accents=True)
bos_id = tokenizer_summ._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer_summ._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer_summ._convert_token_to_id_with_added_voc("<pad>")
# Define helper functions
def is_valid_punjabi_text(text):
english_alphabet = set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ")
numbers = set("0123456789")
punctuation = set("!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~")
for char in text:
if char in english_alphabet or char in numbers or char in punctuation:
return False
return True
def predict_sentiment(text, model, tokenizer):
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=-1).item()
return "Negative" if predicted_class == 0 else "Positive"
def summarize(text):
input_ids = tokenizer_summ(f"{text} </s> <2pa>", add_special_tokens=False, return_tensors="pt",
padding=True).input_ids
model_output = model_summ.generate(input_ids, use_cache=True, no_repeat_ngram_size=3, num_beams=5,
length_penalty=0.8, max_length=20, min_length=1, early_stopping=True,
pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id,
decoder_start_token_id=tokenizer_summ._convert_token_to_id_with_added_voc("<2pa>"))
decoded_output = tokenizer_summ.decode(model_output[0], skip_special_tokens=True,
clean_up_tokenization_spaces=False)
return decoded_output
def process_input(text):
a = [text]
a = tokenizer.texts_to_sequences(a)
a = np.array(a)
a = pad_sequences(a, padding='post', maxlen=100)
a = a.reshape((a.shape[0], a.shape[1], 1))
prediction = model.predict(np.array(a))
for row in prediction:
element1 = row[0]
element2 = row[1]
return "Negative" if element1 > element2 else "Positive"
# Streamlit app
st.title("Indic Sentence Summarization & Sentiment Analysis")
st.header("Insightful Echoes: Crafting Summaries with Sentiments (for ਪੰਜਾਬੀ Text)")
model_choice = st.selectbox("Select the Model", ["Indic-Bert", "RNN"])
summarize_before_sentiment = st.checkbox("Summarize before analyzing sentiment")
user_input = st.text_area("Enter some text here")
if st.button("Analyze Sentiment"):
if not is_valid_punjabi_text(user_input):
st.warning("Please enter valid Punjabi text.")
else:
sentiment_output = ""
if summarize_before_sentiment:
summarized_text = summarize(user_input)
sentiment_bert = predict_sentiment(summarized_text, model1, tokenizer1)
sentiment_output = f'Sentiment (Indic-BERT): {sentiment_bert}\nSummary: {summarized_text}'
else:
sentiment_bert = predict_sentiment(user_input, model1, tokenizer1)
sentiment_output = f'Sentiment (Indic-BERT): {sentiment_bert}'
if model_choice == "RNN":
sentiment_rnn = process_input(user_input)
sentiment_output += f"\nSentiment (Bidirectional LSTM): {sentiment_rnn}"
if summarize_before_sentiment:
summarized_text_rnn = summarize(user_input)
sentiment_output += f"\nSummary (Bidirectional LSTM): {summarized_text_rnn}"
st.text_area("Sentiment Output", sentiment_output, height=200)
|