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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()
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