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import gradio as gr
import cv2
import pytesseract
from PIL import Image
import io
import base64
from datetime import datetime
import pytz
from simple_salesforce import Salesforce
import logging
import numpy as np
import os
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Configure Tesseract path for Hugging Face
pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
# Salesforce configuration (use environment variables in production)
SF_USERNAME = os.getenv("SF_USERNAME", "your_salesforce_username")
SF_PASSWORD = os.getenv("SF_PASSWORD", "your_salesforce_password")
SF_SECURITY_TOKEN = os.getenv("SF_SECURITY_TOKEN", "your_salesforce_security_token")
SF_DOMAIN = os.getenv("SF_DOMAIN", "login") # or "test" for sandbox
def connect_to_salesforce():
"""Connect to Salesforce with error handling."""
try:
sf = Salesforce(username=SF_USERNAME, password=SF_PASSWORD, security_token=SF_SECURITY_TOKEN, domain=SF_DOMAIN)
logging.info("Connected to Salesforce successfully")
return sf
except Exception as e:
logging.error(f"Salesforce connection failed: {str(e)}")
return None
def resize_image(img, max_size_mb=5):
"""Resize image to ensure size < 5MB while preserving quality."""
try:
img_bytes = io.BytesIO()
img.save(img_bytes, format="PNG")
size_mb = len(img_bytes.getvalue()) / (1024 * 1024)
if size_mb <= max_size_mb:
return img, img_bytes.getvalue()
scale = 0.9
while size_mb > max_size_mb:
w, h = img.size
img = img.resize((int(w * scale), int(h * scale)), Image.Resampling.LANCZOS)
img_bytes = io.BytesIO()
img.save(img_bytes, format="PNG")
size_mb = len(img_bytes.getvalue()) / (1024 * 1024)
scale *= 0.9
logging.info(f"Resized image to {size_mb:.2f} MB")
return img, img_bytes.getvalue()
except Exception as e:
logging.error(f"Image resizing failed: {str(e)}")
return img, None
def extract_weight(img):
"""Extract weight from image using Tesseract OCR."""
try:
# Convert PIL image to OpenCV format
img_cv = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
# Preprocess image for better OCR accuracy
_, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
# Configure Tesseract for 7-segment display (digits only, single line)
config = '--psm 7 digits'
text = pytesseract.image_to_string(thresh, config=config)
# Extract numeric values (digits and decimal point)
weight = ''.join(filter(lambda x: x in '0123456789.', text))
# Validate weight (ensure itโs a valid number)
try:
weight_float = float(weight)
# Simplified confidence: 95% if valid number, else 0%
confidence = 95.0 if weight_float > 0 else 0.0
return weight, confidence
except ValueError:
return "Not detected", 0.0
except Exception as e:
logging.error(f"OCR processing failed: {str(e)}")
return "Not detected", 0.0
def process_image(img):
"""Process uploaded or captured image and extract weight."""
if img is None:
return "No image uploaded", None, None, None, gr.update(visible=False), gr.update(visible=False)
ist_time = datetime.now(pytz.timezone("Asia/Kolkata")).strftime("%d-%m-%Y %I:%M:%S %p")
img, img_bytes = resize_image(img)
if img_bytes is None:
return "Image processing failed", ist_time, img, None, gr.update(visible=False), gr.update(visible=False)
weight, confidence = extract_weight(img)
if weight == "Not detected" or confidence < 95.0:
return f"{weight} (Confidence: {confidence:.2f}%)", ist_time, img, None, gr.update(visible=True), gr.update(visible=False)
img_buffer = io.BytesIO(img_bytes)
img_base64 = base64.b64encode(img_buffer.getvalue()).decode()
return f"{weight} kg (Confidence: {confidence:.2f}%)", ist_time, img, img_base64, gr.update(visible=True), gr.update(visible=True)
def save_to_salesforce(weight_text, img_base64):
"""Save weight and image to Salesforce Weight_Log__c object."""
try:
sf = connect_to_salesforce()
if sf is None:
return "Failed to connect to Salesforce"
weight = float(weight_text.split(" ")[0])
ist_time = datetime.now(pytz.timezone("Asia/Kolkata")).strftime("%Y-%m-%d %H:%M:%S")
record = {
"Name": f"Weight_Log_{ist_time}",
"Captured_Weight__c": weight,
"Captured_At__c": ist_time,
"Snapshot_Image__c": img_base64,
"Status__c": "Confirmed"
}
result = sf.Weight_Log__c.create(record)
logging.info(f"Salesforce record created: {result}")
return "Successfully saved to Salesforce"
except Exception as e:
logging.error(f"Salesforce save failed: {str(e)}")
return f"Failed to save to Salesforce: {str(e)}"
# Gradio Interface
with gr.Blocks(title="โ๏ธ Auto Weight Logger") as demo:
gr.Markdown("## โ๏ธ Auto Weight Logger")
gr.Markdown("๐ท Upload or capture an image of a digital weight scale (max 5MB).")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload / Capture Image", sources=["upload", "webcam"])
output_weight = gr.Textbox(label="โ๏ธ Detected Weight (in kg)")
with gr.Row():
timestamp = gr.Textbox(label="๐ Captured At (IST)")
snapshot = gr.Image(label="๐ธ Snapshot Image")
with gr.Row():
confirm_button = gr.Button("โ
Confirm and Save to Salesforce", visible=False)
status = gr.Textbox(label="Save Status", visible=False)
submit = gr.Button("๐ Detect Weight")
submit.click(
fn=process_image,
inputs=image_input,
outputs=[output_weight, timestamp, snapshot, gr.State(), confirm_button, status]
)
confirm_button.click(
fn=save_to_salesforce,
inputs=[output_weight, gr.State()],
outputs=status
)
gr.Markdown("""
### Instructions
- Upload a clear, well-lit image of a digital weight scale display.
- Ensure the image is < 5MB (automatically resized if larger).
- Review the detected weight and click 'Confirm and Save to Salesforce' to log the data.
- Works on desktop and mobile browsers.
""")
if __name__ == "__main__":
demo.launch() |