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# libraries
from flask import Flask, render_template, request, redirect, url_for, flash, session, send_from_directory
import os
from utility.utils import extract_text_from_images,Data_Extractor,json_to_llm_str,process_extracted_text,process_resume_data
from backup.backup import NER_Model
# Flask App
app = Flask(__name__)
app.secret_key = 'your_secret_key'
app.config['UPLOAD_FOLDER'] = 'uploads/'
UPLOAD_FOLDER = 'static/uploads/'
RESULT_FOLDER = 'static/results/'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULT_FOLDER, exist_ok=True)
if not os.path.exists(app.config['UPLOAD_FOLDER']):
os.makedirs(app.config['UPLOAD_FOLDER'])
@app.route('/')
def index():
uploaded_files = session.get('uploaded_files', [])
return render_template('index.html', uploaded_files=uploaded_files)
@app.route('/upload', methods=['POST'])
def upload_file():
if 'files' not in request.files:
flash('No file part')
return redirect(request.url)
files = request.files.getlist('files') # Get multiple files
if not files or all(file.filename == '' for file in files):
flash('No selected files')
return redirect(request.url)
uploaded_files = []
for file in files:
if file:
filename = file.filename
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
uploaded_files.append(filename)
session['uploaded_files'] = uploaded_files
flash('Files successfully uploaded')
return redirect(url_for('index'))
@app.route('/remove_file')
def remove_file():
uploaded_files = session.get('uploaded_files', [])
for filename in uploaded_files:
os.remove(os.path.join(app.config['UPLOAD_FOLDER'], filename))
session.pop('uploaded_files', None)
flash('Files successfully removed')
return redirect(url_for('index'))
@app.route('/process', methods=['POST'])
def process_file():
uploaded_files = session.get('uploaded_files', [])
if not uploaded_files:
flash('No files selected for processing')
return redirect(url_for('index'))
# Create a list of file paths for the extracted text function
file_paths = [os.path.join(app.config['UPLOAD_FOLDER'], filename) for filename in uploaded_files]
# Extract text from all images
extracted_text,processed_Img = extract_text_from_images(file_paths,RESULT_FOLDER)
# Convert PDF to text
print("extracted_text----------------------------",extracted_text)
print("extracted_text type----------------------------",type(extracted_text))
print("processed_Img----------------------------",processed_Img)
print("processed_Img type----------------------------",type(processed_Img))
try:
# Call the Gemma model API and get the professional data
llmText=json_to_llm_str(extracted_text)
print("llmText---------->",llmText)
LLMdata = Data_Extractor(llmText)
print("LLM data----------------------------",LLMdata)
except Exception as e:
# Handling any exceptions during the process
print(f"An error occurred: {e}")
# Run the backup model in case of an exception
print("Running backup model...")
text=json_to_llm_str(extracted_text)
LLMdata=NER_Model(text)
print("NER data----------------------------",LLMdata)
cont_data=process_extracted_text(extracted_text)
print("cont_data----------------------------",cont_data)
#storing the paresed results
processed_data = process_resume_data(LLMdata,cont_data,extracted_text)
session['processed_data'] = processed_data
session['processed_Img'] = processed_Img
flash('Data processed and analyzed successfully ')
return redirect(url_for('result'))
@app.route('/result')
def result():
processed_data = session.get('processed_data', {})
processed_Img = session.get('processed_Img', {})
return render_template('result.html', data=processed_data,Img=processed_Img)
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
if __name__ == '__main__':
app.run(debug=True) |