import os from paddleocr import PaddleOCR from PIL import Image, ImageEnhance import gradio as gr import pandas as pd import re from simple_salesforce import Salesforce # Attributes to match in the image ATTRIBUTES = [ "Product name", "Colour", "Motortype", "Frequency", "Grossweight", "Ratio", "MotorFrame", "Model", "Speed", "Quantity", "Voltage", "Material", "Type", "Horsepower", "Consignee", "LOT", "Stage", "Outlet", "Serialnumber", "HeadSize", "Deliverysize", "Phase", "Size", "MRP", "Usebefore", "Height", "MaximumDischarge Flow", "DischargeRange", "Assembledby", "Manufacturedate", "Companyname", "Customercarenumber", "SellerAddress", "Selleremail", "GSTIN", "Totalamount", "Paymentstatus", "Paymentmethod", "Invoicedate", "Warranty", "Brand", "Motorhorsepower", "Power", "Motorphase", "Enginetype", "Tankcapacity", "Head", "Usage/Application", "Volts", "Hertz", "Frame", "Mounting", "Tollfreenumber", "Pipesize", "Manufacturer", "Office", "Size", "Ratio", "SRnumber", "volts", "weight", "frame", "TypeOfEndUse", "Model Name", "cooling method", ] # Salesforce credentials SALESFORCE_USERNAME = "venkatramana@sandbox.com" SALESFORCE_PASSWORD = "Venkat12345@" SALESFORCE_SECURITY_TOKEN = "GhcJJmjBEefdnukJoz4CAQlR" # Initialize PaddleOCR ocr = PaddleOCR(use_angle_cls=True, lang='en') # Environment variable for the Excel file path EXCEL_FILE_PATH = os.getenv("EXCEL_FILE_PATH", "DataStorage.xlsx") # Function to extract text using PaddleOCR def extract_text(image): result = ocr.ocr(image) extracted_text = [] for line in result[0]: extracted_text.append(line[1][0]) return "\n".join(extracted_text) # Function to find attributes and their values def find_attributes(text): structured_data = {} for attr in ATTRIBUTES: pattern = rf"{re.escape(attr)}[:\-]?\s*(.+)" # Match the attribute and capture its value match = re.search(pattern, text, re.IGNORECASE) if match: structured_data[attr] = match.group(1).strip() return structured_data # Function to sanitize numeric values def sanitize_numeric(value): try: # If the value is already numeric, return it as-is if isinstance(value, (int, float)): return value if '/' in value: # Handle fraction strings like "1/2" numerator, denominator = value.split('/') return float(numerator) / float(denominator) sanitized = re.sub(r'[^\d\.\-]', '', value) # Remove non-numeric characters return float(sanitized) if sanitized else None except (ValueError, ZeroDivisionError): return None # Function to save structured data to the constant Excel file def save_to_excel(data): if not data: return "No data to save." if not os.path.exists(EXCEL_FILE_PATH): df = pd.DataFrame([data]) df.to_excel(EXCEL_FILE_PATH, index=False, engine="openpyxl") else: existing_df = pd.read_excel(EXCEL_FILE_PATH, engine="openpyxl") new_data_df = pd.DataFrame([data]) updated_df = pd.concat([existing_df, new_data_df], ignore_index=True) updated_df.to_excel(EXCEL_FILE_PATH, index=False, engine="openpyxl") return EXCEL_FILE_PATH # Function to update stock in Inventory Management def update_stock_in_inventory_management(data, quantity): try: # Initialize Salesforce connection sf = Salesforce( username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN, ) # Query the existing stock for the product product_name = data.get("Product name") query = f"SELECT Id, Current_Stocks__c FROM Inventory_Management__c WHERE Product_Name__c = '{product_name}' LIMIT 1" result = sf.query(query) if result["totalSize"] == 0: return f"Product '{product_name}' not found in Inventory Management." product_id = result["records"][0]["Id"] current_stock = result["records"][0].get("Current_Stocks__c", 0) # Ensure current_stock is not None current_stock = current_stock or 0 # Deduct stock by the quantity updated_stock = max(0, current_stock - quantity) # Ensure stock doesn't go negative # Update the stock in Salesforce sf.Inventory_Management__c.update(product_id, {"Current_Stocks__c": updated_stock}) return f"Stock updated successfully. {quantity} units deducted from '{product_name}'. Remaining stock: {updated_stock}." except Exception as e: return f"Error updating stock in Inventory Management: {str(e)}" # Function to add stock in VENKATA_RAMANA_MOTORS__c def add_stock_to_venkataramana(data, quantity): try: # Initialize Salesforce connection sf = Salesforce( username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN, ) # Target Salesforce object object_name = "VENKATA_RAMANA_MOTORS__c" sf_object = sf.__getattr__(object_name) # Fetch valid field names from the object schema schema = sf_object.describe() valid_fields = {field["name"] for field in schema["fields"]} # Prepare the record with valid fields record = { "Productname__c": data.get("Product name"), "Quantity__c": sanitize_numeric(quantity), } # Filter record to include only valid fields filtered_record = {k: v for k, v in record.items() if k in valid_fields and v is not None} # Add stock to Salesforce sf_object.create(filtered_record) return f"Data successfully added to {object_name}." except Exception as e: return f"Error adding stock to VENKATA_RAMANA_MOTORS__c: {str(e)}" # Function to process image for the entry interface def process_entry_image(image, quantity): try: extracted_text = extract_text(image) attributes = find_attributes(extracted_text) attributes["Quantity"] = quantity # Include quantity in attributes if attributes: numbered_output = "\n".join( [f"{i + 1}. {key}: {value}" for i, (key, value) in enumerate(attributes.items())] ) # Save to Excel file_path = save_to_excel(attributes) # Add stock to VENKATA_RAMANA_MOTORS__c salesforce_message = add_stock_to_venkataramana(attributes, quantity) return f"{numbered_output}\n\n{salesforce_message}", file_path else: return "No attributes found with values in the image.", None except Exception as e: return f"Error during processing: {str(e)}", None # Function to process image for the exit interface def process_exit_image(image, quantity): try: extracted_text = extract_text(image) attributes = find_attributes(extracted_text) attributes["Quantity"] = quantity # Include quantity in attributes if attributes: numbered_output = "\n".join( [f"{i + 1}. {key}: {value}" for i, (key, value) in enumerate(attributes.items())] ) # Save to Excel file_path = save_to_excel(attributes) # Deduct stock in Inventory Management stock_message = update_stock_in_inventory_management(attributes, quantity) return f"{numbered_output}\n\n{stock_message}", file_path else: return "No attributes found with values in the image.", None except Exception as e: return f"Error during processing: {str(e)}", None # Gradio Interfaces entry_interface = gr.Interface( fn=process_entry_image, inputs=[ gr.Image(type="numpy"), gr.Number(label="Quantity", value=1, interactive=True), ], outputs=[ gr.Text(label="Image Data Viewer"), gr.File(label="Data Storage Manager") ], title="Entry Interface - VENKATARAMANA MOTORS", description="Process images and add stock to VENKATA_RAMANA_MOTORS__c." ) exit_interface = gr.Interface( fn=process_exit_image, inputs=[ gr.Image(type="numpy"), gr.Number(label="Quantity", value=1, interactive=True), ], outputs=[ gr.Text(label="Image Data Viewer"), gr.File(label="Data Storage Manager") ], title="Exit Interface - VENKATARAMANA MOTORS", description="Process images and deduct stock from Inventory Management." ) if __name__ == "__main__": gr.TabbedInterface([entry_interface, exit_interface], ["Entry", "Exit"]).launch(share=True)