Update app.py

app.py

@@ -6,15 +6,14 @@ import io
 import base64
 from datetime import datetime
 import pytz
-from simple_salesforce import Salesforce
-import logging
 import numpy as np
+import logging
 import os
 
 # Set up logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-# Configure Tesseract path for Hugging Face
+# Configure Tesseract path
 try:
     pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
     pytesseract.get_tesseract_version()  # Test Tesseract availability
@@ -22,51 +21,12 @@ try:
 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)
+        # Enhance contrast with CLAHE
         clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
         contrast = clahe.apply(gray)
         # Reduce noise with Gaussian blur
@@ -79,7 +39,7 @@ def preprocess_image(img_cv):
         return sharpened
     except Exception as e:
         logging.error(f"Image preprocessing failed: {str(e)}")
-        return gray
+        return img_cv
 
 def detect_roi(img_cv):
     """Detect the region of interest (ROI) containing the weight display."""
@@ -156,46 +116,13 @@ def process_image(img):
         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)
+        return f"{weight} (Confidence: {confidence:.2f}%)", ist_time, 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)}"
+    return f"{weight} kg (Confidence: {confidence:.2f}%)", ist_time, None, gr.update(visible=True), gr.update(visible=True)
 
 # Gradio Interface
 with gr.Blocks(title="⚖️ Auto Weight Logger") as demo:
@@ -210,29 +137,18 @@ with gr.Blocks(title="⚖️ Auto Weight Logger") as demo:
         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
+        outputs=[output_weight, timestamp, snapshot]
     )
 
     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.
+    - Review the detected weight and try again if it's incorrect.
     """)
 
 if __name__ == "__main__":