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import gradio as gr | |
from PIL import Image | |
from datetime import datetime | |
import pytz | |
from ocr_engine import extract_weight, extract_unit_from_text | |
from simple_salesforce import Salesforce | |
import base64 | |
import re | |
import os | |
# Salesforce credentials | |
SF_USERNAME = "[email protected]" | |
SF_PASSWORD = "autoweight@32" | |
SF_TOKEN = "UgiHKWT0aoZRX9gvTYDjAiRY" | |
# Connect to Salesforce | |
sf = Salesforce(username=SF_USERNAME, password=SF_PASSWORD, security_token=SF_TOKEN) | |
def restore_decimal(text): | |
if re.fullmatch(r"\d{5}", text): | |
return f"{text[:2]}.{text[2:]}" | |
elif re.fullmatch(r"\d{4}", text): | |
return f"{text[:2]}.{text[2:]}" | |
return text | |
def process_image(image): | |
if image is None: | |
return "β No image provided", "", None, gr.update(visible=True) | |
try: | |
result = extract_weight(image) | |
weight, raw_text = result if isinstance(result, tuple) else (result, "") | |
print("π§ Final OCR Result:", weight) | |
print("π€ OCR Raw Text:", raw_text) | |
ist = pytz.timezone('Asia/Kolkata') | |
timestamp = datetime.now(ist).strftime("%Y-%m-%d %H:%M:%S IST") | |
if not weight or (isinstance(weight, str) and weight.startswith("Error")): | |
return f"β OCR Error: {weight}", "", image, gr.update(visible=True) | |
match = re.search(r'(\d{1,3}\.\d{1,3})\s*(kg|g)?', weight) | |
if match: | |
numeric_value = float(match.group(1)) | |
unit = match.group(2) if match.group(2) else extract_unit_from_text(raw_text) | |
else: | |
cleaned = re.sub(r"[^\d]", "", weight) | |
decimal_fixed = restore_decimal(cleaned) | |
try: | |
numeric_value = float(decimal_fixed) | |
unit = extract_unit_from_text(raw_text) | |
except: | |
return f"β Could not extract number | OCR: {weight}", "", image, gr.update(visible=True) | |
image_path = "snapshot.jpg" | |
image.save(image_path) | |
record = sf.Weight_Log__c.create({ | |
"Captured_Weight__c": numeric_value, | |
"Captured_Unit__c": unit, | |
"Captured_At__c": datetime.now(ist).isoformat(), | |
"Device_ID__c": "DEVICE-001", | |
"Status__c": "Confirmed" | |
}) | |
with open(image_path, "rb") as f: | |
encoded_image = base64.b64encode(f.read()).decode("utf-8") | |
content = sf.ContentVersion.create({ | |
"Title": f"Snapshot_{timestamp}", | |
"PathOnClient": "snapshot.jpg", | |
"VersionData": encoded_image | |
}) | |
content_id = sf.query( | |
f"SELECT ContentDocumentId FROM ContentVersion WHERE Id = '{content['id']}'" | |
)['records'][0]['ContentDocumentId'] | |
sf.ContentDocumentLink.create({ | |
"ContentDocumentId": content_id, | |
"LinkedEntityId": record['id'], | |
"ShareType": "V", | |
"Visibility": "AllUsers" | |
}) | |
return f"{numeric_value} {unit}", timestamp, image, gr.update(visible=False) | |
except Exception as e: | |
return f"Error: {str(e)}", "", None, gr.update(visible=True) | |
with gr.Blocks(css=".gr-button {background-color: #2e7d32 !important; color: white !important;}" ) as demo: | |
gr.Markdown(""" | |
<h1 style='text-align: center; color: #2e7d32;'>π· Auto Weight Logger</h1> | |
<p style='text-align: center;'>Upload or capture a digital weight image. Detects weight using AI OCR and logs it into Salesforce.</p> | |
<hr style='border: 1px solid #ddd;' /> | |
""") | |
with gr.Row(): | |
image_input = gr.Image(type="pil", label="π Upload or Capture Image") | |
detect_btn = gr.Button("π Detect Weight") | |
with gr.Row(): | |
weight_out = gr.Textbox(label="π¦ Detected Weight", placeholder="e.g., 75.5 kg", show_copy_button=True) | |
time_out = gr.Textbox(label="π Captured At (IST)", placeholder="e.g., 2025-07-01 12:00:00") | |
snapshot = gr.Image(label="πΈ Snapshot Preview") | |
retake_btn = gr.Button("π Retake / Try Again", visible=False) | |
detect_btn.click(fn=process_image, inputs=image_input, outputs=[weight_out, time_out, snapshot, retake_btn]) | |
retake_btn.click(fn=lambda: ("", "", None, gr.update(visible=False)), | |
inputs=[], outputs=[weight_out, time_out, snapshot, retake_btn]) | |
if __name__ == "__main__": | |
demo.launch() | |