Spaces:
Sleeping
Sleeping
File size: 12,633 Bytes
2406f66 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 4946383 b687b19 8cbd013 2b0bde5 4946383 7ccf8f2 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 d1f1696 8cbd013 06b173c 8cbd013 06b173c 8cbd013 06b173c d1f1696 8cbd013 06b173c 8cbd013 06b173c 8cbd013 06b173c 8cbd013 06b173c d1f1696 06b173c d1f1696 06b173c d1f1696 06b173c d1f1696 06b173c d1f1696 06b173c d1f1696 7ccf8f2 06b173c 4946383 8cbd013 06b173c 353fd37 8cbd013 06b173c 8cbd013 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 4946383 8cbd013 06b173c 4946383 8cbd013 74ec8c4 06b173c 8cbd013 06b173c 887d87f 06b173c 887d87f 4946383 06b173c 4946383 887d87f 06b173c 887d87f 06b173c 887d87f 06b173c 887d87f 06b173c 8cbd013 4946383 8cbd013 06b173c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 |
from transformers import pipeline
from lang_trans.arabic import buckwalter
from difflib import SequenceMatcher
import pandas as pd
import gradio as gr
import time
# Gradio application for learning Noorani Qaida
p = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-arabic")
# Read excel file and store it in a dictionary
def read_excel_data(file_path, sheet_name):
data_list = []
try:
# Read the Excel file
df = pd.read_excel(file_path, sheet_name=sheet_name)
# Iterate over the rows
for index, row in df.iterrows():
test = row['test']
correct = row['correct']
close = row['close']
wrong = row['wrong']
image = row["image"]
# Store close and wrong values as a tuple
data_list.append((test, correct, close, wrong, image))
return data_list
except Exception as e:
print(f"Error reading Excel file: {e}")
# List of sample texts to read
excel_file_path = "ASR_live_test.xlsx"
sheet_name= 'sample_test(threshold=75%)'
full_test_list = read_excel_data(excel_file_path, sheet_name)
# Similarity function
def similar(a, b):
return SequenceMatcher(None, a, b).ratio()
def transcribe(audio, reference_text):
time.sleep(1)
text = p(audio)["text"]
text = buckwalter.untrans(text)
state = text
if state is not None:
if similar(reference_text, state) > 0.75:
score = "correct"
elif similar(reference_text, state) > 0.50 and similar(reference_text, state) < 0.75:
score = "close"
else:
score = "wrong"
return state, score
else :
print(" Null Object")
# Function to retrieve the list of unique categories
def get_unique_tests(data_list):
tests=[]
tests = [test for test, _, _, _, _ in data_list if test not in tests]
return tests
# Function to retrieve close, wrong and image values for a given correct value
def get_values_image_for_test(tests, test):
for t, correct, close, wrong, image in tests:
if t == test:
return correct, close, wrong, image
# Counting completed tests
def completed_tests(test):
global completed_tests_list
if completed_tests_list is None:
completed_tests_list = []
if test and test not in completed_tests_list :
completed_tests_list.append(test)
# elif test and test in completed_tests_list:
# gr.Warning("Test alreday done! Please select another test.")
total_completed_tests = len(completed_tests_list)
return total_completed_tests, completed_tests_list
# Tests distribution over users
import random
def assign_tests(num_tests):
global remaining_tests
assigned_tests = []
tests = get_unique_tests(full_test_list)
# Shuffle tests
random.shuffle(tests)
# Updating remaining_tests
if (len(remaining_tests) == 0) and (len(assigned_tests) == 0):
remaining_tests = set(tests)
# else:
# remaining_tests -= set(assigned_tests)
# Last remaining tests are assigned to the last user
if num_tests > len(remaining_tests):
assigned_tests = remaining_tests
# Ensure we still have enough tests
if len(remaining_tests) == 0:
print("***** Tests Completed *****")
pass#return ["Tests Completed"]
# Select tests that are not already assigned
assigned_tests = [test for test in tests if test in remaining_tests][:num_tests]
# Remove assigned tests from the set of all available tests
remaining_tests -= set(assigned_tests)
return assigned_tests
# Tracking user progress
def test_progress(test):
global completed_tests_list
global len_assigned_test
# Get total completed tests
total_completed_tests, completed_tests_list = completed_tests(test)
# Test completed to be displayed to the user
completed_tests_text = "\n".join(completed_tests_list)
# Calculate how many tests are remaining
total_remaining_tests = len_assigned_test - total_completed_tests
if total_remaining_tests == 0:
completed_tests_list.clear()
progress_text = f"Congratulations! You have completed all tests"
else:
progress_text = f"Completed tests {total_completed_tests} . Remaining {total_remaining_tests}"
return progress_text, completed_tests_text
# Authentication function
def update_message(request: gr.Request):
return f"Welcome, {request.username}"
# Gradio apps
# Get test categories
data_list = full_test_list
image_path="all_imgs/"
completed_tests_list = []
remaining_tests = []
# Get a set of tests for each user
num_tests = 10
assigned_tests = assign_tests(num_tests)
len_assigned_test = len(assigned_tests)
print(assigned_tests)
# Flagging
callback = gr.CSVLogger()
# CSS
css = """
h1 {
text-align: center;
color: #5756BB;
display:block;
}
p {
text-align: left;
display:block;
}
p.thick {
font-weight: bold;
}
.drop_color {background-color: #e0eaff}
img {
height: auto;
width: auto;
margin-left: auto;
margin-right: auto;
display: block;
}
.row {
height: 90px;
width: auto;
}
progress_text {
text-align: center;
ont-weight: bold;
}
"""
js = """
function change_color(){
const test_text = document.getElementById("#test_color");
const selectedtest = dropdown.options[dropdown.selectedIndex];
selectedtest.style.color = "green";
}
"""
demo = gr.Blocks(theme=gr.themes.Soft(), css=css)
with demo :
# Add app title
gr.Markdown(
"""
# Noorani Qaida Test Interface"""
)
# User authentication
with gr.Row():
m = gr.Markdown()
logout_button = gr.Button("Logout", link="/logout", scale=0, variant="primary")
demo.load(update_message, None, m)
# Add app description
gr.Markdown(
"""
Select a category then a test from the list, read the text aloud, get the transcription and save it.
"""
)
# Assign value to each step
def get_test_text(test):
correct, close, wrong, image = get_values_image_for_test(data_list, test)
correct_text = gr.Textbox(label="Step 1", value=correct)
close_text = gr.Textbox(label="Step 2", value=close)
wrong_text = gr.Textbox(label="Step 3", value=wrong)
image = gr.Image(value=(image_path+str(image)+".jpg"))
return correct_text, close_text, wrong_text, image
def update_completed_test(test):
_, completed_tests = gr.TextArea(completed_tests(test))
return completed_tests
with gr.Row():
# First big column
with gr.Column(scale=1):
# User progress
progress_text = gr.Textbox(value="Completed tests 0. Remaining 10", interactive=False, show_label=False, container=True, elem_id="progress_text", elem_classes="drop_color")
textarea_completed = gr.TextArea(info=" Your completed tests will appear here ", interactive=False, rtl=True, container=True, show_label=False, elem_classes="drop_color")
# Test selection
tests_list = assigned_tests
test_dropdown = gr.Dropdown(tests_list, label="Tests", info="Select a test", scale=1, elem_id="test_color", elem_classes="drop_color")
test_dropdown.select(test_progress, test_dropdown, outputs=[progress_text, textarea_completed])
# Second big column
with gr.Column(scale=3):
# First row for image
with gr.Row():
image = gr.Image(elem_id="img", elem_classes="row", container=False, show_label=True, show_download_button=False, show_share_button=False)
# Second row for steps
with gr.Row():
# First sub-column
with gr.Column():
correct_text = gr.Textbox(label= "Step 1", info="Correct Text", interactive=False, rtl=True)
correct_audio_record = gr.Audio(sources="microphone" ,type="filepath", label="Record Audio", show_label=False)
correct_trans_text = gr.Textbox(info="Transcription", interactive=False, rtl=True, show_label=False)
correct_score = gr.Textbox(label="Score", visible= False)
correct_audio_record.stop_recording(fn=transcribe, inputs=[correct_audio_record, correct_text], outputs=[correct_trans_text, correct_score])
# Second sub-column
with gr.Column():
close_text = gr.Textbox(label="Step 2", info="Close Text", interactive=False, rtl=True)
close_audio_record = gr.Audio(sources="microphone", type="filepath", label="Record Audio", show_label=False)
close_trans_text = gr.Textbox(info="Transcription", interactive=False, rtl=True, show_label=False)
close_score = gr.Textbox(label="Score", visible= False)
close_audio_record.stop_recording(fn=transcribe, inputs=[close_audio_record, close_text], outputs=[close_trans_text, close_score])
# Third sub-column
with gr.Column():
wrong_text = gr.Textbox(label="Step 3", info="Wrong Text", interactive=False, rtl=True)
wrong_audio_record = gr.Audio(sources="microphone" ,type="filepath", label="Record Audio", show_label=False)
wrong_trans_text = gr.Textbox(info="Transcription", interactive=False, rtl=True, show_label=False)
wrong_score = gr.Textbox(label="Score", visible= False)
wrong_audio_record.stop_recording(fn=transcribe, inputs=[wrong_audio_record, wrong_text], outputs=[wrong_trans_text, wrong_score])
# Row for flag
with gr.Row():
#user_name = gr.Request.username
def save_outputs(correct_text, correct_trans_text, correct_score, correct_audio_record,
close_text, close_trans_text, close_score, close_audio_record,
wrong_text, wrong_trans_text, wrong_score, wrong_audio_record):
if any(len(text)==0 for text in [correct_trans_text, close_trans_text, wrong_trans_text]):
return gr.Warning("Please complete the test before saving")
return (lambda *args: callback.flag(args, username="randa"))
flag_btn = gr.Button(value="Save this test", variant="primary", scale=2)
# Setup the components to flag
callback.setup([correct_text, correct_trans_text, correct_score, correct_audio_record,
close_text, close_trans_text, close_score, close_audio_record,
wrong_text, wrong_trans_text, wrong_score, wrong_audio_record], "flagged_data")
# We can choose which components to flag
flag_btn.click(save_outputs,
[correct_text, correct_trans_text, correct_score, correct_audio_record,
close_text, close_trans_text, close_score, close_audio_record,
wrong_text, wrong_trans_text, wrong_score, wrong_audio_record],
None, preprocess=False)
# Update test values according to the selected test
test_dropdown.input(get_test_text, test_dropdown, [correct_text, close_text, wrong_text, image])
# Clear the transcription after selecting a new test
test_dropdown.select(lambda: [None]*10, None,
outputs=[correct_text, close_text, wrong_text, correct_audio_record, close_audio_record, wrong_audio_record, correct_trans_text, close_trans_text, wrong_trans_text, image],
queue=False)
#demo.launch(inbrowser=True)
if __name__ == "__main__":
demo.launch(auth=[("randa", "randa"), ("randa1", "randa1"),("randa2", "randa2"), ("randa3", "randa3"),
("karim1", "karim1"), ("karim2", "karim2"), ("karim3", "karim3"),
("yassir1", "yassir1"), ("yassir2", "yassir2"), ("yassir3", "yassir3"),
("mehdi", "mehdi")],
share=True, inbrowser=True) |