Update app.py

app.py

@@ -2,6 +2,13 @@ from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
 import gradio as gr
 import cv2
 import easyocr
+from simple_salesforce import Salesforce
+
+# Salesforce credentials
+Salesforce_User_Name = '[email protected]'  # Your Salesforce username
+Salesforce_Password = 'Sathkrutha@06'
+SALESFORCE_INSTANCE_URL = 'https://sathkruthatechsolutions63-dev-ed.develop.lightning.force.com'
+SALESFORCE_ACCESS_TOKEN = 'UnByPih7PWmoWLzRuRyFrXzw'
 
 # Initialize EasyOCR reader for text extraction
 reader = easyocr.Reader(['en'])
@@ -10,6 +17,9 @@ reader = easyocr.Reader(['en'])
 model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-large-uncased")
 tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-large-uncased")
 
+# Salesforce Connection Setup
+sf = Salesforce(username=Salesforce_User_Name, password=Salesforce_Password, security_token=SALESFORCE_ACCESS_TOKEN)
+
 # Function to extract text using EasyOCR and process with LayoutLM
 def extract_patient_info(image):
     # Convert the uploaded image to RGB (required by LayoutLM)
@@ -23,12 +33,42 @@ def extract_patient_info(image):
     inputs = tokenizer(extracted_text, return_tensors="pt")
     outputs = model(**inputs)
 
-    # For this example, we return the extracted text (you can further process LayoutLM's output)
+    # Here, extracted_text is already available from EasyOCR, we can extract relevant details
+    details = extract_details_from_text(extracted_text)
+    
+    # Create a record in Salesforce using the extracted details
+    create_salesforce_record(details)
+
+    # Return the extracted text
     return extracted_text
 
+# Function to extract details from the extracted text (use regex or other methods to extract)
+def extract_details_from_text(extracted_text):
+    # Simple example of extracting details, customize this according to the format of the text
+    details = {}
+    details['Name'] = "Shanthi"  # Here, add the logic to extract the actual name
+    details['Age'] = "39"        # Similarly, extract age, gender, and phone number
+    details['Gender'] = "Female"
+    details['Phone Number'] = "9955337097"
+    
+    return details
+
+# Function to create a record in Salesforce
+def create_salesforce_record(details):
+    data = {
+        'Name__c': details['Name'],
+        'Age__c': int(details['Age']),
+        'Gender__c': details['Gender'],
+        'Phone_Number__c': details['Phone Number']
+    }
+    
+    # Create a new record in Salesforce
+    sf.Patient_Registration__c.create(data)
+    print("Salesforce record created successfully!")
+
 # Gradio interface setup
 with gr.Blocks() as demo:
-    gr.Markdown("### OCR Using LayoutLM Pretrained Model with EasyOCR")
+    gr.Markdown("### OCR Using LayoutLM Pretrained Model with EasyOCR and Salesforce Integration")
 
     # Image upload component
     image_input = gr.Image(type="numpy", label="Upload Image")