app.py

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
try:
    pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
    pytesseract.get_tesseract_version()  # Test Tesseract availability
    logging.info("Tesseract is available")
except Exception as e:
    logging.error(f"Tesseract not found or misconfigured: {str(e)}")

# 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 preprocess_image(img_cv):
    """Preprocess image for OCR: enhance contrast, reduce noise, and apply adaptive thresholding."""
    try:
        # Convert to grayscale
        gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
        # Enhance contrast with CLAHE (Contrast Limited Adaptive Histogram Equalization)
        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
        contrast = clahe.apply(gray)
        # Reduce noise with Gaussian blur
        blurred = cv2.GaussianBlur(contrast, (5, 5), 0)
        # Apply adaptive thresholding for better binary image representation
        thresh = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
        # Sharpen the image to bring out more details in the numbers
        kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
        sharpened = cv2.filter2D(thresh, -1, kernel)
        return sharpened
    except Exception as e:
        logging.error(f"Image preprocessing failed: {str(e)}")
        return gray

def detect_roi(img_cv):
    """Detect the region of interest (ROI) containing the weight display."""
    try:
        # Convert to grayscale for edge detection
        gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
        # Apply edge detection
        edges = cv2.Canny(gray, 50, 150)
        # Find contours
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        if not contours:
            logging.warning("No contours detected for ROI")
            return img_cv  # Return full image if no contours found
        
        # Find the largest contour (assuming it’s the display)
        largest_contour = max(contours, key=cv2.contourArea)
        x, y, w, h = cv2.boundingRect(largest_contour)
        # Add padding to the detected region to ensure weight is fully captured
        padding = 10
        x = max(0, x - padding)
        y = max(0, y - padding)
        w = min(img_cv.shape[1] - x, w + 2 * padding)
        h = min(img_cv.shape[0] - y, h + 2 * padding)
        roi = img_cv[y:y+h, x:x+w]
        logging.info(f"ROI detected at ({x}, {y}, {w}, {h})")
        return roi
    except Exception as e:
        logging.error(f"ROI detection failed: {str(e)}")
        return img_cv

def extract_weight(img):
    """Extract weight from image using Tesseract OCR with multiple PSM modes."""
    try:
        if img is None:
            logging.error("No image provided for OCR")
            return "Not detected", 0.0
        
        # Convert PIL image to OpenCV format
        img_cv = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
        # Detect ROI
        roi_img = detect_roi(img_cv)
        # Preprocess the ROI
        processed_img = preprocess_image(roi_img)
        
        # Try multiple PSM modes for better detection
        psm_modes = [
            ('--psm 7 digits', 'Single line, digits only'),
            ('--psm 6 digits', 'Single block, digits only'),
            ('--psm 10 digits', 'Single character, digits only'),
            ('--psm 8 digits', 'Single word, digits only')
        ]
        
        for config, desc in psm_modes:
            text = pytesseract.image_to_string(processed_img, config=config)
            logging.info(f"OCR attempt with {desc}: Raw text = '{text}'")
            weight = ''.join(filter(lambda x: x in '0123456789.', text.strip()))
            try:
                weight_float = float(weight)
                if weight_float >= 0:  # Allow zero weights
                    confidence = 95.0  # Simplified confidence for valid numbers
                    logging.info(f"Weight detected: {weight} (Confidence: {confidence:.2f}%)")
                    return weight, confidence
            except ValueError:
                logging.warning(f"Invalid number format: {weight}")
                continue
        
        logging.error("All OCR attempts failed to detect a valid weight")
        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:
        logging.error("No image provided")
        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:
        logging.error("Image resizing failed")
        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:
        logging.warning(f"Weight detection failed: {weight} (Confidence: {confidence:.2f}%)")
        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()
    logging.info(f"Weight detected successfully: {weight} kg")
    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:
            logging.error("Salesforce connection failed")
            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="⚖️ Auto Weight Logger") as demo:
    gr.Markdown("## ⚖️ Auto Weight Logger")
    gr.Markdown("📷 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="⚖️ Detected Weight (in kg)")

    with gr.Row():
        timestamp = gr.Textbox(label="🕒 Captured At (IST)")
        snapshot = gr.Image(label="📸 Snapshot Image")

    with gr.Row():
        confirm_button = gr.Button("✅ Confirm and Save to Salesforce", visible=False)
        status = gr.Textbox(label="Save Status", visible=False)

    submit = gr.Button("🔍 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 (7-segment font preferred).
    - 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 weight detection fails, check the image for glare, low contrast, or non-numeric characters and try again.
    """)

if __name__ == "__main__":
    demo.launch()