From f4590e7d55df5246d7394ee9e3aa206862abcd70 Mon Sep 17 00:00:00 2001
From: Nisita-M <131473392+Nisita-M@users.noreply.github.com>
Date: Mon, 30 Dec 2024 10:36:06 +0530
Subject: [PATCH] Update app.py
---
app.py | 182 ++++++++++++++++++++++++++++++++++++++-------------------
1 file changed, 121 insertions(+), 61 deletions(-)
diff --git a/app.py b/app.py
index ca5fd73..0e66356 100644
--- a/app.py
+++ b/app.py
@@ -6,12 +6,21 @@
import pytesseract
from pytesseract import Output
from PIL import Image
-from flask import Flask, render_template, request
+from flask import Flask, render_template, request ,redirect , url_for , session
+from flask_session import Session
+# from flask_session import FileSystemSessionInterface
import PyPDF2
+from io import BytesIO
+import uuid
+import os
+
+nltk.download('words')
+nltk.download('punkt')
+nltk.download('stopwords')
+
+UPLOAD_FOLDER = 'uploads'
+
-# nltk.download('words')
-# nltk.download('punkt')
-# nltk.download('stopwords')
def preprocess_text(text):
text = re.sub(r'[^\w\s]', '', text)
@@ -52,15 +61,17 @@ def readable_summary(text):
# Specify the Tesseract executable path
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
+
app = Flask(__name__)
# nltk.download('words')
# nltk.download('punkt')
# nltk.download('stopwords')
+app.secret_key ='supra_me'
def extract_text_from_pdf(pdf_file):
text = ""
- pdf_reader = PdfFileReader(pdf_file)
+ pdf_reader = PyPDF2.PdfFileReader(pdf_file)
for page_number in range(pdf_reader.numPages):
page = pdf_reader.getPage(page_number)
text += page.extractText()
@@ -68,73 +79,122 @@ def extract_text_from_pdf(pdf_file):
@app.route('/')
def index():
- return render_template('index.html')
+ return render_template('index.ejs')
@app.route('/process_file', methods=['POST'])
def process_file():
+ session['questions']=[]
+ session['answers']=[]
+
# Get the choice of the user (image or pdf)
file_type = request.form['file_type']
-
+ session['file_type'] = file_type
+
if file_type == 'image':
# Example: Get the uploaded image file
uploaded_file = request.files['file']
-
- # Perform OCR on the image
- with Image.open(uploaded_file) as image:
- txt = pytesseract.image_to_string(image)
-
- elif file_type == 'pdf':
+ name=uploaded_file.filename
+ if uploaded_file.filename == '':
+ return 'No selected file', 400
+ file_id = str(uuid.uuid4()) # Generate a unique ID for the file
+ file_path = os.path.join(UPLOAD_FOLDER, file_id)
+ uploaded_file.save(file_path)
+ session['file_path'] = file_path
+ # print(uploaded_file)
+
+
+
+ if file_type == 'pdf':
# Example: Get the uploaded PDF file
uploaded_file = request.files['file']
-
+ name=uploaded_file.filename
+ if uploaded_file.filename == '':
+ return 'No selected file', 400
+ file_id = str(uuid.uuid4()) # Generate a unique ID for the file
+ file_path = os.path.join(UPLOAD_FOLDER, file_id)
+ uploaded_file.save(file_path)
+ session['file_path'] = file_path
+ print(name)
+ return redirect(url_for('quest',filename=name))
+
+@app.route('/quest/<filename>',methods=['GET'])
+def quest(filename):
# Extract text from the PDF using PyPDF2
- pdf_reader = PyPDF2.PdfReader(uploaded_file)
-
- # Get the number of pages in the PDF
- num_pages =num_pages = len(pdf_reader.pages)
-
- # Initialize an empty string to store the extracted text
- txt = ""
-
- # Iterate through all pages
- for page_num in range(num_pages):
- # Get the page
- page = pdf_reader.pages[page_num]
-
- # Extract text from the page
- etext = page.extract_text()
-
- # Append the text to the result string
- txt+=etext
-
- sample_text = txt
- # Preprocess text using the loaded function
- preprocessed_text = preprocess_text(sample_text)
-
- # Divide documents into sentences using the loaded function
- sentences = divide_documents(preprocessed_text)
-
- # Generate summaries using the loaded function
- summaries = generate_summaries(sentences)
-
- # Check if the user wants to view the summary
- show_summary = request.form.get('show_summary')
-
- if show_summary:
- # Use the loaded function to generate a readable summary
- summary = readable_summary(sample_text)
- else:
- summary = None
-
- # Example: Get the question from the user
- user_question = request.form['question']
-
- # Use the loaded QA pipeline
- answer = qa_pipeline(question=user_question, context=sample_text)
-
- # Render the result on the webpage
- return render_template('result.html', question=user_question, answer=answer['answer'], summary=summary)
+ # print("Meowwww")
+
+ return render_template("question.ejs",filename=filename)
+ # uploaded_file
+
+@app.route('/quest/<filename>',methods=['POST'])
+def upload(filename):
+ print("broo")
+ items=session.get('questions')
+ print("Session before appending:", items)
+ print('hello')
+ file_path = session.get('file_path')
+
+ if file_path is None or not os.path.exists(file_path):
+ return "File not found", 400
+
+ if session.get('file_type') == 'pdf':
+ with open(file_path, 'rb') as file:
+ file_data = file.read()
+ pdf_reader = PyPDF2.PdfReader(BytesIO(file_data))
+
+ # Get the number of pages in the PDF
+ num_pages =num_pages = len(pdf_reader.pages)
+
+ # Initialize an empty string to store the extracted text
+ txt = ""
+
+ # Iterate through all pages
+ for page_num in range(num_pages):
+ # Get the page
+ page = pdf_reader.pages[page_num]
+
+ # Extract text from the page
+ etext = page.extract_text()
+
+ # Append the text to the result string
+ txt+=etext
+
+ if session.get('file_type') == 'image':
+ # Perform OCR on the image
+ with Image.open(file_path) as image:
+ txt = pytesseract.image_to_string(image)
+
+ sample_text = txt
+ if sample_text == '':
+ sample_text = "No answers found"
+ # Preprocess text using the loaded function
+ preprocessed_text = preprocess_text(sample_text)
+
+ # Divide documents into sentences using the loaded function
+ sentences = divide_documents(preprocessed_text)
+
+ # Generate summaries using the loaded function
+ summaries = generate_summaries(sentences)
+
+ # Check if the user wants to view the summary
+ show_summary = request.form.get('show_summary')
+
+ if show_summary:
+ # Use the loaded function to generate a readable summary
+ summary = readable_summary(sample_text)
+ else:
+ summary = None
+
+ # Example: Get the question from the user
+ user_question = request.form['question']
+ session['questions'].append(user_question)
+ session.modified = True
+ print("Session after appending:", session.get('questions'))
+ # Use the loaded QA pipeline
+ answer = qa_pipeline(question=user_question, context=sample_text)
+ session['answers'].append(answer['answer'])
+
+ # Render the result on the webpage
+ return render_template('question.ejs', question=session.get('questions'), answer=session.get('answers'), summary=summary,filename=filename,len=len(session.get('questions')))
if __name__ == '__main__':
app.run(debug=True)
-