Create app.py

app.py

@@ -0,0 +1,341 @@
+# main program
+import gradio as gr
+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
+
+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.components.Textbox(label="Examination"),
+    gr.components.Textbox(label="Date Of Exam"),
+    gr.components.Textbox(label="Program"),
+    gr.components.Textbox(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)
+    model = project.version(model_version).model
+    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  # Return both cropped image and original image
+
+# Function to resize and insert cropped image
+def resize_and_insert(cropped_image, base_image_path, output_image_path):
+    base_image = cv2.imread(base_image_path)
+    base_height, base_width = base_image.shape[:2]
+    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))
+    base_image[0:base_height, 0:new_width] = resized_cropped_img
+    # cv2.imwrite(output_image_path, base_image)
+    return base_image
+
+# 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 = "LimDLja7HF1Asuk3vfSd"
+  project_name = "marks_table_detection_lbrce"
+  model_version = 1
+  base_image_path = "/content/drive/MyDrive/base_img.jpg"
+  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
+
+  # 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, base_image_path, 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 = []
+  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
+
+# # interface two
+# iface2 = gr.Interface(
+#     fn=task2,
+#     inputs="image",
+#     outputs="text",
+#     title="Automating Examination Mark Entry with Deep Learning"
+# )
+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")
+    ],
+    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)