Upload 2 files

app.py

@@ -0,0 +1,122 @@
+import streamlit as st
+from random import random
+from spacy.lang.en.stop_words import STOP_WORDS
+#import en_core_web_sm
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+import torch
+import re
+from string import punctuation
+from heapq import nlargest
+#import spacy_streamlit
+import configparser
+import random
+import spacy
+import gtts
+import os
+os.exec(python -m spacy download en_core_web_sm)
+nlp = spacy.load("en_core_web_sm")
+#nlp= en_core_web_sm.load()
+stopwords = list(STOP_WORDS)
+punctuation = punctuation + "\n"
+model_name = 'google/pegasus-xsum'
+torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+tokenizer = PegasusTokenizer.from_pretrained(model_name)
+model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
+def abst_summary(src_text):
+    batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest',return_tensors='pt')
+    translated = model.generate(**batch)
+    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+    return tgt_text
+
+def word_frequency(doc):
+    word_frequencies = {}
+    for word in doc:
+        if word.text.lower() not in stopwords:
+            if word.text.lower() not in punctuation:
+                if word.text not in word_frequencies.keys():
+                    word_frequencies[word.text] = 1
+                else:
+                    word_frequencies[word.text] += 1
+    return word_frequencies
+
+def sentence_score(sentence_tokens, word_frequencies):
+    sentence_score = {}
+    for sent in sentence_tokens:
+        for word in sent:
+            if word.text.lower() in word_frequencies.keys():
+                if sent not in sentence_score.keys():
+                    sentence_score[sent] = word_frequencies[word.text.lower()] 
+                else:
+                    sentence_score[sent] += word_frequencies[word.text.lower()]
+    return sentence_score
+
+def get_summary(text):
+
+    #text = re.sub(f"[{re.escape(punctuation)}]", "", text)
+    text = re.sub(r"<.*?>", "", text)
+    text = re.sub(r"[.*?]", "", text)
+    text = re.sub(r"https?://\S+", " ", text)
+    text = re.sub(r"\b[0-9]+\b\s*", " ", text)
+    doc = nlp(text)
+    
+    word_frequencies = word_frequency(doc)
+    
+    for word in word_frequencies.keys():
+        word_frequencies[word] = word_frequencies[word] / max(word_frequencies.values())
+    sentence_tokens = [sent for sent in doc.sents]
+    sentence_scores = sentence_score(sentence_tokens, word_frequencies)
+    
+    select_length = int(len(sentence_tokens)*0.10)
+    if select_length < 1:
+        select_length =1
+    #print(len(sentence_tokens)*0.10)
+    summary  = nlargest(select_length, sentence_scores, key=sentence_scores.get)
+    summary = [word.text  for word in summary]
+    summary = " ".join(summary)
+    #print("sums up:",summary)
+    return summary
+
+st.set_page_config(
+     page_title="Audio summarizer Web App",
+     layout="wide",
+     initial_sidebar_state="expanded"
+)
+st.title("Audio Summaries")
+col1, col2 = st.columns(2)
+
+with col1:
+    text_ = st.text_area(label="Enter Your Text or story", height=350, placeholder="Enter Your Text or story or your article iit can be of any length")
+
+if st.button("Get Summary"):
+    ex_summary = get_summary(text_)
+    ab_summary = abst_summary(text_)
+    print(ab_summary)
+    try:
+        with col2:
+            st.text_area(label="Extractive Text Summarization (Summary length :{}, Actual Text :{})".format(len(ex_summary),len(text_)),
+                        value=ex_summary,
+                        height=350)
+            #st.text(summary)
+            if len(ex_summary)>0:
+                ex_tts = gtts.gTTS(ex_summary)
+                ex_tts.save("ex_summary.wav")
+                ex_audio_file = open('ex_summary.wav', 'rb')
+                ex_audio_bytes = ex_audio_file.read()
+                data = ex_audio_bytes
+                st.audio(data, format="audio/wav", start_time=0, sample_rate=None)
+            
+            st.text_area(label="Abstractive Text Summarization (Summary length :{}, Actual Text :{})".format(len(ab_summary[0]),len(text_)),
+                        value=ab_summary[0],
+                        height=350)
+            #st.text(summary)
+            if len(ab_summary[0])>0:
+                ab_tts = gtts.gTTS(ab_summary[0])
+                ab_tts.save("ab_summary.wav")
+                ab_audio_file = open('ab_summary.wav', 'rb')
+                ab_audio_bytes = ab_audio_file.read()
+                data = ab_audio_bytes
+                st.audio(data, format="audio/wav", start_time=0, sample_rate=None)
+    
+
+    except NameError:
+        pass

requirements.txt.txt

@@ -0,0 +1,7 @@
+streamlit==1.16.0
+transformers
+torchaudio==0.13.1
+torchvision==0.14.1
+spacy
+gTTS==2.3.0
+gensim==4.2.0