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app.py
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app.py
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from flask import Flask, render_template, request
import spacy
from spacy.lang.en.stop_words import STOP_WORDS
from heapq import nlargest
from bs4 import BeautifulSoup
import speech_recognition as sr
from moviepy.editor import VideoFileClip
from pydub import AudioSegment
from googletrans import Translator,LANGUAGES
from transformers import pipeline
import os
nlp = spacy.load("en_core_web_sm")
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'uploads'
if not os.path.exists(app.config['UPLOAD_FOLDER']):
os.makedirs(app.config['UPLOAD_FOLDER'])
summarizer = pipeline("summarization", model="t5-base", revision="main")
def txt_summarizer(raw_docx):
stopwords = list(STOP_WORDS)
docx = nlp(raw_docx)
word_frequencies = {}
for word in docx:
if word.text.lower() not in stopwords:
if word.text.lower() not in word_frequencies.keys():
word_frequencies[word.text.lower()] = 1
else:
word_frequencies[word.text.lower()] += 1
maximum_frequency = max(word_frequencies.values())
for word in word_frequencies.keys():
word_frequencies[word] = (word_frequencies[word] / maximum_frequency)
sentence_list = [sentence for sentence in docx.sents]
sentence_scores = {}
for sent in sentence_list:
for word in sent:
if word.text.lower() in word_frequencies.keys():
if sent not in sentence_scores.keys():
sentence_scores[sent] = word_frequencies[word.text.lower()]
else:
sentence_scores[sent] += word_frequencies[word.text.lower()]
summarized_sentences = nlargest(7, sentence_scores, key=sentence_scores.get)
final_sentences = [w.text for w in summarized_sentences]
summary = ' '.join(final_sentences)
return summary
@app.route('/')
def home():
return render_template('index.html')
@app.route('/summarize', methods=['POST'])
def summarize():
raw_doc = request.form["text"]
print("Received input:", raw_doc) # Check if the input is received
summary = txt_summarizer(raw_doc)
return render_template('index.html',text=raw_doc,summary=summary)
@app.route('/summarize1', methods=['POST'])
def summarize1():
uploaded_file = request.files['file']
soup = BeautifulSoup(uploaded_file, 'html.parser')
# Remove all <a> tags (links)
for a_tag in soup.find_all('a'):
a_tag.extract()
# Remove all <img> tags (images)
for img_tag in soup.find_all('img'):
img_tag.extract()
# Extract text from modified HTML
text = soup.get_text(separator='\n', strip=True)
summary = txt_summarizer(text)
return render_template('index.html', summary1=summary)
@app.route('/summarize2', methods=['POST'])
def summarize2():
uploaded_file = request.files['file']
# Transcribe the exported audio file
recognizer = sr.Recognizer()
with sr.AudioFile(uploaded_file) as source:
audio_data = recognizer.record(source) # Record the entire audio file
text = recognizer.recognize_google(audio_data)
summary = txt_summarizer(text)
return render_template('index.html', summary2=summary)
@app.route('/summarize_translate', methods=['POST'])
def summarize_translate():
if request.method == 'POST':
text = request.form['text']
target_language = request.form['target_language']
# Perform summarization
summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
summarized_text = summary[0]['summary_text']
# Perform translation with error handling
translator = Translator()
try:
translation = translator.translate(summarized_text, dest=target_language)
translated_text = translation.text
except Exception as e:
translated_text = f"Translation Error: {str(e)}"
return render_template('index.html', summarized_text=summarized_text, translated_text=translated_text)
@app.route('/upload', methods=['POST'])
def upload_file():
if request.method == 'POST':
video_file = request.files['video']
if video_file:
video_path = os.path.join(app.config['UPLOAD_FOLDER'], video_file.filename)
video_file.save(video_path)
audio_path = os.path.join(app.config['UPLOAD_FOLDER'], 'audio.wav')
try:
video = VideoFileClip(video_path)
audio = video.audio
audio.write_audiofile(audio_path)
except Exception as e:
return f'Error extracting audio: {str(e)}'
# Transcribe audio to text using SpeechRecognition
transcribed_text = transcribe_audio(audio_path)
# Perform text summarization
summarized_text = summarize_text(transcribed_text)
return render_template('index.html', summarized_text=summarized_text)
return 'Upload failed'
def transcribe_audio(audio_path):
recognizer = sr.Recognizer()
audio_file = sr.AudioFile(audio_path)
with audio_file as source:
audio_data = recognizer.record(source)
transcribed_text = recognizer.recognize_google(audio_data)
return transcribed_text
def summarize_text(text):
summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
summarized_text = summary[0]['summary_text']
return summarized_text
if __name__ == '__main__':
app.run(debug=True)