import sys
import os
import io
import gradio as gr
import json
import requests
from PIL import Image
from flask import request
import sqlite3
from datetime import datetime, timedelta
# Initialize SQLite database
css = """
.example-image img{
display: flex; /* Use flexbox to align items */
justify-content: center; /* Center the image horizontally */
align-items: center; /* Center the image vertically */
height: 300px; /* Set the height of the container */
object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
}
.example-image img{
display: flex; /* Use flexbox to align items */
text-align: center;
justify-content: center; /* Center the image horizontally */
align-items: center; /* Center the image vertically */
height: 350px; /* Set the height of the container */
object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
}
.markdown-success-container {
background-color: #F6FFED;
padding: 20px;
margin: 20px;
border-radius: 1px;
border: 2px solid green;
text-align: center;
}
.markdown-fail-container {
background-color: #FFF1F0;
padding: 20px;
margin: 20px;
border-radius: 1px;
border: 2px solid red;
text-align: center;
}
.block-background {
# background-color: #202020; /* Set your desired background color */
border-radius: 5px;
}
"""
# Initialize SQLite database
conn = sqlite3.connect("ip_requests.db")
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS requests (
ip_address TEXT PRIMARY KEY,
count INTEGER,
last_request TIMESTAMP
)
""")
conn.commit()
def track_requests(ip_address):
now = datetime.now()
cursor.execute("SELECT count, last_request FROM requests WHERE ip_address=?", (ip_address,))
result = cursor.fetchone()
if result:
count, last_request = result
last_request = datetime.strptime(last_request, "%Y-%m-%d %H:%M:%S")
if now - last_request > timedelta(days=1):
count = 0
else:
count = 0
count += 1
cursor.execute("""
INSERT OR REPLACE INTO requests (ip_address, count, last_request)
VALUES (?, ?, ?)
""", (ip_address, count, now.strftime("%Y-%m-%d %H:%M:%S")))
conn.commit()
return count
screenReplayThreshold = 0.5
portraitReplaceThreshold = 0.5
printedCopyThreshold = 0.5
def find_key_in_dict(d, target_key):
for key, value in d.items():
if key == target_key:
return value
elif isinstance(value, dict): # If the value is a dictionary, search recursively
result = find_key_in_dict(value, target_key)
if result is not None:
return result
return None
def json_to_html_table(data, image_keys):
html = "
"
for key, value in data.items():
if isinstance(value, dict):
html += f"{key} |
"
for sub_key, sub_value in value.items():
if sub_key in image_keys:
html += f"{sub_key} |  |
"
else:
html += f"{sub_key} | {sub_value} |
"
else:
if key in image_keys:
html += f"{key} |  |
"
else:
html += f"{key} | {value} |
"
html += "
"
return html
def check_liveness(frame):
if frame is None:
liveness_result = f""""""
return [liveness_result, {"status": "error", "result": "select image file!"}]
img_bytes = io.BytesIO()
Image.open(frame).save(img_bytes, format="JPEG")
img_bytes.seek(0)
url = "https://api.cortex.cerebrium.ai/v4/p-4f1d877e/my-first-project/check-liveness/"
try:
files = [
('file', ('image.jpg', img_bytes, 'image/jpeg'))
]
headers = {
'Authorization': 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJwcm9qZWN0SWQiOiJwLTRmMWQ4NzdlIiwiaWF0IjoxNzM5MjM2NjA5LCJleHAiOjIwNTQ4MTI2MDl9.0LH0iOnqHZfKTH4GF5iTZ4qNj5vylCryo8rBnErljsq2qD2cpVTetCqhKtnbstTUEjuv6MAJw9jt58z-QNJfYLK9sJcnBhawTR3iM2Ap_bFyjlzg2LbgkwRPjUVJkkcuCRhBKyebXwvqQBvWyOtMq6UekauumbmYBRbA2-T4u343YD4tO2xIfsTsTXznALp1SechjRuys-3xo3ZQbUs05_p38fOFucKI-abc91Eq6sIOkLFjYEM68yuV0UBWl-OSpPu66e0SClroAVlKFDMPS9MY0Jr7X1pBYX4jew6vozj9D8Y-HS-KkdPFqJ7HrZOfQd0wGUgYJHyC58yReWXaRQ',
# 'Content-Type': 'application/json'
}
result = requests.post(url=url, files=files, headers=headers)
except:
liveness_result = f""""""
return [liveness_result, {"status": "error", "result": "failed to open file!"}]
print("the result is", result)
if result.ok:
json_result = result.json()
if json_result.get("resultCode") == "Error":
liveness_result = f""""""
return [liveness_result, {"status": "error", "result": "server error!"}]
if 'data' in json_result:
data = json_result['data']
print("the result data is is",data)
if data["IsLive"] :
liveness_result = f""""""
json_output = {"Is Live": "Success",
"document Type": data["DocumentType"],
# "Printed Cutout Check": "Failed" if printedCopy < printedCopyThreshold else "Success"
}
# Update json_result with the modified process_results
return [liveness_result, json_output]
else:
liveness_result = f""""""
json_output = {"Is Live": "Failed",
"document Type": data["DocumentType"],
# "Printed Cutout Check": "Failed" if printedCopy < printedCopyThreshold else "Success"
}
# Update json_result with the modified process_results
return [liveness_result, json_output]
liveness_result = f""""""
return [liveness_result, {"status": "error", "result": "document not found!"}]
else:
liveness_result = f""""""
return [liveness_result, {"status": "error", "result": f"{result.text}"}]
def idcard_recognition(frame1):
ip_address = request.remote_addr
request_count = track_requests(ip_address)
print("you have exceeded the daily limit of 5 requests", request_count)
if request_count > 3:
print("you have exceeded the daily limit of 5 requests")
return "You have exceeded the daily limit of 5 requests."
url = "https://api.cortex.cerebrium.ai/v4/p-4f1d877e/my-first-project/process-image/"
# url = "https://edreesi-ocr-api.hf.space/process-image/"
files = None
if frame1 is not None:
# Open the image using Pillow
img = Image.open(frame1).convert("RGB") # Convert to RGB to remove alpha channels
img_bytes = io.BytesIO()
# Save the image in JPEG format with consistent quality
img.save(img_bytes, format="JPEG", quality=95, optimize=True, exif=b"") # Strip EXIF metadata
img_bytes.seek(0) # Reset the file pointer
# Log the file size for debugging
print("Gradio File Size:", len(img_bytes.getvalue()), "bytes")
# Prepare the files payload
files = [
('file', ('image.jpg', img_bytes, 'image/jpeg'))
]
else:
return ['', None, None]
# headers = {"X-RapidAPI-Key": os.environ.get("API_KEY")}
headers = {}
# r = requests.post(url=url, files=files, headers=headers)
# r = requests.request("POST", url, headers=headers, data={}, files=files)
payload = json.dumps({"prompt": "your value here"})
headers = {
'Authorization': 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJwcm9qZWN0SWQiOiJwLTRmMWQ4NzdlIiwiaWF0IjoxNzM5MjM2NjA5LCJleHAiOjIwNTQ4MTI2MDl9.0LH0iOnqHZfKTH4GF5iTZ4qNj5vylCryo8rBnErljsq2qD2cpVTetCqhKtnbstTUEjuv6MAJw9jt58z-QNJfYLK9sJcnBhawTR3iM2Ap_bFyjlzg2LbgkwRPjUVJkkcuCRhBKyebXwvqQBvWyOtMq6UekauumbmYBRbA2-T4u343YD4tO2xIfsTsTXznALp1SechjRuys-3xo3ZQbUs05_p38fOFucKI-abc91Eq6sIOkLFjYEM68yuV0UBWl-OSpPu66e0SClroAVlKFDMPS9MY0Jr7X1pBYX4jew6vozj9D8Y-HS-KkdPFqJ7HrZOfQd0wGUgYJHyC58yReWXaRQ',
# 'Content-Type': 'application/json'
}
r = requests.request("POST", url, headers=headers, data={}, files=files)
# print(r.text)
print("Status Code:", r.status_code)
print("r Body:", r.text)
# r = requests.post(url=url, files=files, headers=headers)
print('the result is', r.json())
images = None
rawValues = {}
image_table_value = ""
result_table_dict = {
'portrait':'',
'type':'',
'score':'',
# 'countryName':'',
'FullName':'',
'Gender':'',
'PlaceOfBirth':'',
'DateOfBirth':'',
'IssuanceCenter':'',
'IdentityNumber':'',
'DateOfIssue':'',
'DateOfExpiry':'',
}
if 'data' in r.json():
data = r.json()['data']
for key, value in data.items():
if key == 'faceImage':
# Assign faceImage to the portrait field
result_table_dict['portrait'] = value
elif key == 'barcodeImage':
# Add barcodeImage to the result dictionary
result_table_dict['barcodeImage'] = value
else:
# Add other fields to the result dictionary
result_table_dict[key] = value
# Generate HTML for images
image_table_value = ""
if 'barcodeImage' in data:
image_table_value += (
""
f"barcodeImage | "
f" | "
"
"
)
# Generate the final HTML table for images
images = (
""
""
"Field | "
"Image | "
"
"
f"{image_table_value}"
"
"
)
# Prepare raw values for JSON output
for key, value in r.json().items():
if key == 'data':
if 'faceImage' in value:
del value['faceImage']
if 'barcodeImage' in value:
del value['barcodeImage']
rawValues[key] = value
else:
rawValues[key] = value
# Generate the result HTML table
result = json_to_html_table(result_table_dict, {'portrait', 'barcodeImage'})
json_result = json.dumps(rawValues, indent=6)
return [result, json_result, images]
def launch_demo():
with gr.Blocks(css=css) as demo:
gr.Markdown(
f"""
📘 Product Documentation
🏠 Visit Recognito
"""
)
with gr.Tabs():
with gr.Tab("ID Document Recognition"):
with gr.Row():
with gr.Column(scale=6):
with gr.Row():
with gr.Column(scale=6):
id_image_input1 = gr.Image(type='filepath', label='ID Card Image', elem_classes="example-image")
# with gr.Column(scale=3):
# id_image_input2 = gr.Image(type='filepath', label='Back', elem_classes="example-image")
# with gr.Row():
# id_examples = gr.Examples(
# examples=[['examples/1_f.png', 'examples/1_b.png'],
# ['examples/2_f.png', 'examples/2_b.png'],
# ['examples/3_f.png', 'examples/3_b.png'],
# ['examples/4.png', None]],
# inputs=[id_image_input1, id_image_input1],
# outputs=None,
# fn=idcard_recognition
# )
with gr.Blocks():
with gr.Column(scale=4, min_width=400, elem_classes="block-background"):
id_recognition_button = gr.Button("ID Card Recognition", variant="primary", size="lg")
with gr.Tab("Key Fields"):
id_result_output = gr.HTML()
with gr.Tab("Raw JSON"):
json_result_output = gr.JSON()
with gr.Tab("Images"):
image_result_output = gr.HTML()
id_recognition_button.click(idcard_recognition, inputs=id_image_input1, outputs=[id_result_output, json_result_output, image_result_output])
with gr.Tab("Id Card Liveness Detection"):
with gr.Row():
with gr.Column(scale=1):
id_image_input = gr.Image(label="Image", type='filepath', elem_classes="example-image")
gr.Examples(examples=['examples/1_f.png', 'examples/2_f.png', 'examples/3_f.png', 'examples/4.png'], inputs=id_image_input)
with gr.Blocks():
with gr.Column(scale=1, elem_classes="block-background"):
check_liveness_button = gr.Button("Check Document Liveness", variant="primary", size="lg")
liveness_result = gr.Markdown("")
json_output = gr.JSON()
check_liveness_button.click(check_liveness, inputs=id_image_input, outputs=[liveness_result, json_output])
gr.HTML('
')
demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)
if __name__ == '__main__':
launch_demo()