Create ocr_engine.py

ocr_engine.py

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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 "❌ No image provided", "", None, gr.update(visible=True)
+    try:
+        result = extract_weight(image)
+        weight, raw_text = result if isinstance(result, tuple) else (result, "")
+
+        print("🧠 Final OCR Result:", weight)
+        print("🔤 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"❌ 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) or "kg"
+        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) or "kg"
+            except:
+                return f"❌ 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;'>📷 Auto Weight Logger</h1>
+    <p style='text-align: center;'>Upload or capture a digital weight image. Detects weight using AI OCR.</p>
+    <hr style='border: 1px solid #ddd;' />
+    """)
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="📁 Upload or Capture Image")
+
+    detect_btn = gr.Button("🚀 Detect Weight")
+
+    with gr.Row():
+        weight_out = gr.Textbox(label="📦 Detected Weight", placeholder="e.g., 75.5 kg", show_copy_button=True)
+        time_out = gr.Textbox(label="🕒 Captured At (IST)", placeholder="e.g., 2025-07-01 12:00:00")
+
+    snapshot = gr.Image(label="📸 Snapshot Preview")
+    retake_btn = gr.Button("🔁 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()