Update app.py

app.py

@@ -1,6 +1,5 @@
-# main program
-# hi
 import gradio as gr
+import random
 import openpyxl
 from openpyxl.styles import Font, Alignment
 import tempfile
@@ -9,6 +8,8 @@ from PIL import Image
 import cv2
 import numpy as np
 import os
+from gradio_calendar import Calendar
+import datetime
 
 excel_tempfile_state = gr.State()
 roll_number_state = gr.State()
@@ -176,10 +177,10 @@ def task1(Examination, Date_Of_Exam, Program, Branch, Course, Name_Of_Faculty, A
 
 #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.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"),
@@ -213,15 +214,13 @@ def predict_and_crop(image_np, api_key, project_name, model_version, confidence=
     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]
+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))
-    base_image[0:base_height, 0:new_width] = resized_cropped_img
-    # cv2.imwrite(output_image_path, base_image)
-    return base_image
+    return resized_cropped_img
 
 # Function to convert string to integer based on confidence level
 def convert_str_int(var, conf):
@@ -273,10 +272,10 @@ def append_to_workbook(cells_data, excel_file_path):
 
 #All Functions for interface 2
 def task2(image_np):
-  api_key = "LimDLja7HF1Asuk3vfSd"
+  api_key = "UyAumhQJOJpo7vUu3LaK"
   project_name = "marks_table_detection_lbrce"
   model_version = 1
-  base_image_path = "base_img.jpg"
+  base_image_path = "base_img.png"
   temp_image_path = "temp_image.jpg"
   output_image_path = "merged_image.jpg"
 
@@ -289,7 +288,7 @@ def task2(image_np):
   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)
+  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)]
@@ -316,27 +315,23 @@ def task2(image_np):
   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"),
+    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",
+    # 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)
+demo.launch(share=True,debug=True)