import gradio as gr import random import openpyxl from openpyxl.styles import Font, Alignment import tempfile from roboflow import Roboflow from PIL import Image import cv2 import numpy as np import os # import dw from gradio_calendar import Calendar import datetime excel_tempfile_state = gr.State() roll_number_state = gr.State() sno_state = gr.State() roll_number_state.value=1 sno_state.value=1 str_nums = {"zero":0,"one":1,"two":2,"three":3,"four":4,"five":5,"six":6,"seven":7,"eight":8,"nine":9} def task1(Examination, Date_Of_Exam, Program, Branch, Course, Name_Of_Faculty, Academic_Year): with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as temp_file: workbook = openpyxl.Workbook() sheet = workbook.active # Define font styles font_size_20 = Font(name="Times New Roman", size=20, bold=True) font_size_18 = Font(name="Times New Roman", size=18, bold=True) font_size_16 = Font(name="Times New Roman", size=16, bold=True) font_size_12_bold = Font(name="Times New Roman", size=12, bold=True) # Define alignment alignment_center = Alignment(horizontal="center") # Merge cells for first two rows sheet.merge_cells('A1:P1') sheet.merge_cells('A2:P2') sheet['A1'].value = "LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING" sheet['A2'].value = "(AUTONOMOUS)" sheet['A1'].font = font_size_20 sheet['A2'].font = font_size_20 sheet['A1'].alignment = alignment_center sheet['A2'].alignment = alignment_center # Merge cells for third row sheet.merge_cells('A3:P3') sheet['A3'].value = "DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING" sheet['A3'].font = font_size_18 sheet['A3'].alignment = alignment_center # Merge cells for fourth row sheet.merge_cells('A4:P4') sheet['A4'].value = "MID DESCRIPTIVE MARKS LIST" sheet['A4'].font = font_size_16 sheet['A4'].alignment = alignment_center # Merge cells for fifth row sheet.merge_cells('A5:H5') sheet.merge_cells('I5:P5') sheet['A5'].value = f"Examination : {Examination}" sheet['I5'].value = f"Date of Exam : {Date_Of_Exam}" sheet['A5'].font = font_size_12_bold sheet['I5'].font = font_size_12_bold # Merge cells for sixth row sheet.merge_cells('A6:H6') sheet.merge_cells('I6:P6') sheet['A6'].value = f"Program : {Program}" sheet['I6'].value = f"Branch : {Branch}" sheet['A6'].font = font_size_12_bold sheet['I6'].font = font_size_12_bold # Merge cells for seventh row sheet.merge_cells('A7:H7') sheet.merge_cells('I7:P7') sheet['A7'].value = f"Course : {Course}" sheet['I7'].value = "Maximum Marks : 15" sheet['A7'].font = font_size_12_bold sheet['I7'].font = font_size_12_bold # Merge cells for eighth row sheet.merge_cells('A8:H8') sheet.merge_cells('I8:P8') sheet['A8'].value = f"Name of the Faculty: {Name_Of_Faculty}" sheet['I8'].value = f"Academic Year : {Academic_Year}" sheet['A8'].font = font_size_12_bold sheet['I8'].font = font_size_12_bold ##part two # Merge cells for SNo sheet.merge_cells('A9:A10') sheet['A9'] = "SNo" sheet['A9'].font = font_size_12_bold sheet['A9'].alignment = alignment_center # Add data for Regd Num sheet.merge_cells('B9:B10') sheet['B9'] = "Regd Num" sheet['B9'].font = font_size_12_bold sheet['B9'].alignment = alignment_center # Add data for Q. No 1 sheet.merge_cells('C9:F9') sheet['C9'] = "Q. No 1" sheet['C9'].font = font_size_12_bold sheet['C9'].alignment = alignment_center sheet['C10'] = "A" sheet['C10'].font = font_size_12_bold sheet['C10'].alignment = alignment_center sheet['D10'] = "B" sheet['D10'].font = font_size_12_bold sheet['D10'].alignment = alignment_center sheet['E10'] = "C" sheet['E10'].font = font_size_12_bold sheet['E10'].alignment = alignment_center sheet['F10'] = "D" sheet['F10'].font = font_size_12_bold sheet['F10'].alignment = alignment_center # Add data for Q. No 2 sheet.merge_cells('G9:J9') sheet['G9'] = "Q. No 2" sheet['G9'].font = font_size_12_bold sheet['G9'].alignment = alignment_center sheet['G10'] = "A" sheet['G10'].font = font_size_12_bold sheet['G10'].alignment = alignment_center sheet['H10'] = "B" sheet['H10'].font = font_size_12_bold sheet['H10'].alignment = alignment_center sheet['I10'] = "C" sheet['I10'].font = font_size_12_bold sheet['I10'].alignment = alignment_center sheet['J10'] = "D" sheet['J10'].font = font_size_12_bold sheet['J10'].alignment = alignment_center # Add data for Q. No 3 sheet.merge_cells('K9:N9') sheet['K9'] = "Q. No 3" sheet['K9'].font = font_size_12_bold sheet['K9'].alignment = alignment_center sheet['K10'] = "A" sheet['K10'].font = font_size_12_bold sheet['K10'].alignment = alignment_center sheet['L10'] = "B" sheet['L10'].font = font_size_12_bold sheet['L10'].alignment = alignment_center sheet['M10'] = "C" sheet['M10'].font = font_size_12_bold sheet['M10'].alignment = alignment_center sheet['N10'] = "D" sheet['N10'].font = font_size_12_bold sheet['N10'].alignment = alignment_center # Add data for Total(30M) sheet.merge_cells('O9:O10') sheet['O9'] = "Total(30M)" sheet['O9'].font = font_size_12_bold sheet['O9'].alignment = alignment_center # Add data for (Total 15 M) sheet.merge_cells('P9:P10') sheet['P9'] = "(Total 15 M)" sheet['P9'].font = font_size_12_bold sheet['P9'].alignment = alignment_center workbook.save(temp_file.name) excel_tempfile_state.value = temp_file.name print(excel_tempfile_state.value) return temp_file.name #configuring interface 1 inputs = [ gr.Dropdown(["I Mid","II Mid"], value=["I Mid", "II Mid"], label="Examination"), Calendar(type="date", label="Date Of Examination"), gr.Dropdown(["B-Tech R20","M-Tech R20","MBA R20","B-Tech R17","M-Tech R17","MBA-R17"], value=["B-Tech R20","M-Tech R20","MBA R20","B-Tech R17","M-Tech R17","MBA-R17"], label="Program"), gr.Dropdown(["ASE","AI&DS","Civil","CSE","CSE(AI&ML)","ECE","EEE","IT","MECH","MBA"], value=["ASE","AI&DS","Civil","CSE","CSE(AI&ML)","ECE","EEE","IT","MECH","MBA"], label="Branch"), gr.components.Textbox(label="Course"), gr.components.Textbox(label="Name Of Faculty"), gr.components.Textbox(label="Academic Year"), ] # interface one iface1 = gr.Interface( fn=task1, inputs=inputs, outputs="file", title="Automating Examination Mark Entry with Deep Learning" ) def predict_and_crop(image_np, api_key, project_name, model_version, confidence=40, overlap=30): img = Image.fromarray(image_np) rf = Roboflow(api_key=api_key) project = rf.workspace().project(project_name) print(f"Project: {project}") model = project.version(model_version).model print(f"Model: {model}") if model is None: raise ValueError(f"Model version {model_version} not found in project {project_name}.") corners = model.predict(image_np, confidence=confidence, overlap=overlap).json() predictions = corners["predictions"][0] prediction = {key: int(value) for key, value in predictions.items() if key in ['x', 'y', 'width', 'height']} x1 = int(prediction['x'] - prediction['width'] / 2) y1 = int(prediction['y'] - prediction['height'] / 2) x2 = int(prediction['x'] + prediction['width'] / 2) y2 = int(prediction['y'] + prediction['height'] / 2) cropped_img = img.crop((x1, y1, x2, y2)) cropped_img_np = np.array(cropped_img) h, w, c = cropped_img_np.shape if h > w: cropped_img_np = cv2.rotate(cropped_img_np, cv2.ROTATE_90_CLOCKWISE) return cropped_img_np, img # Function to resize and insert cropped image def resize_and_insert(cropped_image, output_image_path): # base_image = cv2.imread(base_image_path) base_height, base_width = (460, 1158) base_aspect_ratio = base_width / base_height new_width = int(base_height * base_aspect_ratio) resized_cropped_img = cv2.resize(cropped_image, (new_width, base_height)) return resized_cropped_img # Function to convert string to integer based on confidence level def convert_str_int(var, conf): if conf < 0.5: return " " else: return str_nums[var] def append_to_workbook(cells_data, excel_file_path): workbook = openpyxl.load_workbook(excel_file_path) sheet = workbook.active # Retrieve current SNo and roll number from state sno = sno_state.value rno = roll_number_state.value if rno<10: rno_str = "21761A420"+str(rno) else: rno_str = "21761A42"+str(rno) # Increment SNo and roll number sno_state.value = sno + 1 roll_number_state.value = rno + 1 next_row = sheet.max_row + 1 # Insert roll number and SNo sheet.cell(row=next_row, column=1, value=sno) sheet.cell(row=next_row, column=2, value=rno_str) for col, value in enumerate(cells_data, start=3): sheet.cell(row=next_row, column=col, value=value) marks=[] for i in cells_data: if str(i).isdigit(): marks.append(int(i)) else: marks.append(0) total_30_marks = max(sum(marks[0:2]),sum(marks[2:4]))+max(sum(marks[4:6]),sum(marks[6:8]))+max(sum(marks[8:10]),sum(marks[10:12])) total_15_marks = round(total_30_marks / 2, 2) sheet.cell(row=next_row, column=15, value=total_30_marks) sheet.cell(row=next_row, column=16, value=total_15_marks) # Save the workbook workbook.save(excel_file_path) #All Functions for interface 2 def task2(image_np): api_key = "UyAumhQJOJpo7vUu3LaK" project_name = "marks_table_detection_lbrce" model_version = 1 base_image_path = "base_img.png" temp_image_path = "temp_image.jpg" output_image_path = "merged_image.jpg" #API and requirements for OCR Model rf = Roboflow(api_key="XsMt3y86MNDGihOYcWDY") project = rf.workspace().project("mnist-cjkff") model = project.version(2).model print(f"Model: {model}") # Predict and crop cropped_image, original_image = predict_and_crop(image_np, api_key, project_name, model_version) # Resize and insert result_image = resize_and_insert(cropped_image, output_image_path) # Cell coordinates cell_coordinates = cell_coordinates = [(235, 129), (475, 223), (496, 125), (685, 225), (708, 127), (896, 225), (919, 125), (1140, 217), (232, 253), (473, 346), (500, 249), (687, 347), (708, 250), (896, 346), (920, 249), (1142, 345), (232, 375), (474, 442), (496, 371), (686, 442), (708, 373), (897, 444), (922, 373), (1147, 443)] cells_data = [4,5,'','','','',3,2,1,2,'',''] # qno = 0 # for i in range(0, len(cell_coordinates), 2): # top_left = cell_coordinates[i] # bottom_right = cell_coordinates[i + 1] # cell = result_image[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]] # cell_gray = cv2.cvtColor(cell, cv2.COLOR_BGR2GRAY) # _, thresholded_cell = cv2.threshold(cell_gray, 127, 255, cv2.THRESH_BINARY_INV) # _, temp_thresholded_path = tempfile.mkstemp(suffix=".jpg") # cv2.imwrite(temp_thresholded_path, thresholded_cell) # res = model.predict(temp_thresholded_path).json() # var = res["predictions"][0]["predictions"][0]["class"] # conf = res["predictions"][0]["predictions"][0]["confidence"] # cells_data.append(convert_str_int(var, conf)) # qno += 1 excel_file_path = excel_tempfile_state.value append_to_workbook(cells_data,excel_file_path) print(cells_data) return cropped_image, cells_data, excel_file_path iface2 = gr.Interface( fn=task2, elem_id="my-interface", inputs=[ gr.components.Image(type="numpy", label="Upload Image") ], outputs=[ gr.components.Image(type="numpy", label="Cropped Image"), gr.components.Textbox(label="Detected Marks"), gr.components.File(label="marks_sheet") ], # examples=[['IMG_20240215_210403.jpg'],['IMG_20240215_210530.jpg'],['IMG_20240215_210534.jpg'],['IMG_20240215_210611.jpg']], title="Automating Examination Mark Entry with Deep Learning", theme="huggingface" ) demo = gr.TabbedInterface([iface1, iface2], ["Configure Excel Sheet Data", "Extract marks from Answer Sheets"]) # Run the interface demo.launch(share=True,debug=True)