import os from paddleocr import PaddleOCR from PIL import Image import gradio as gr import requests import re from simple_salesforce import Salesforce import pandas as pd import matplotlib.pyplot as plt from io import BytesIO from fuzzywuzzy import process import kaleido # Ensure kaleido is imported # Attribute mappings: readable names to Salesforce API names ATTRIBUTE_MAPPING = { "Productname": "Productname__c", "Colour": "Colour__c", "Motortype": "Motortype__c", "Frequency": "Frequency__c", "Grossweight": "Grossweight__c", "Ratio": "Ratio__c", "MotorFrame": "Motorframe__c", "Model": "Model__c", "Speed": "Speed__c", "Quantity": "Quantity__c", "Voltage": "Voltage__c", "Material": "Material__c", "Type": "Type__c", "Horsepower": "Horsepower__c", "Consignee": "Consignee__c", "LOT": "LOT__c", "Stage": "Stage__c", "Outlet": "Outlet__c", "Serialnumber": "Serialnumber__c", "HeadSize": "Headsize__c", "Deliverysize": "Deliverysize__c", "Phase": "Phase__c", "Size": "Size__c", "MRP": "MRP__c", "Usebefore": "Usebefore__c", "Height": "Height__c", "MaximumDischarge Flow": "Maximumdischargeflow__c", "DischargeRange": "Dischargeflow__c", "Assembledby": "Manufacturer__c", "Manufacturedate": "Manufacturedate__c", "Companyname": "Companyname__c", "Customercarenumber": "Customercarenumber__c", "SellerAddress": "Selleraddress__c", "Selleremail": "Selleremail__c", "GSTIN": "GSTIN__c", "Totalamount": "Totalamount__c", "Paymentstatus": "Paymentstatus__c", "Paymentmethod": "Paymentstatus__c", "Invoicedate": "Manufacturedate__c", "Warranty": "Warranty__c", "Brand": "Brand__c", "Motorhorsepower": "Motorhorsepower__c", "Power": "Power__c", "Motorphase": "Motorphase__c", "Enginetype": "Enginetype__c", "Tankcapacity": "Tankcapacity__c", "Head": "Head__c", "Usage/Application": "Usage_Application__c", "Volts": "volts__c", "Hertz": "Hertz__c", "Frame": "frame__c", "Mounting": "Mounting__c", "Tollfreenumber": "Tollfreenumber__c", "Pipesize": "Pipesize__c", "Manufacturer": "Manufacturer__c", "Office": "Office__c", "SRnumber": "SRnumber__c", "TypeOfEndUse": "TypeOfEndUse__c", "Model Name": "Model_Name_Number__c", "coolingmethod": "coolingmethod__c" } # List of product names to match PRODUCT_NAMES = [ "Centrifugal mono block pump", "SINGLE PHASE MOTOR STARTER", "EasyPact EZC 100", "Openwell Submersible Pumpset", "Electric Motor", "Self Priming Pump", "Control panel for single phase submersible pumps", "MOTOR", "Submersible pump set", "Fusion submersible pump set", "DCT", "Shock proof water proof", "CG COMMERCIAL MOTORS", "Fusion", "control panel for single phase submerisible pumps", "single phase digital starter dry run and timer panel", "5HP AV1 XL Kirloskar Pump", "Phase stainless steel submersible pump", "Submersible pump", "WB15X", "Vtype self priming pump", "SP SHINE DISC", "havells submersible pump", "Havells open well Submersible pump", "Bertolini pump CK3 90pp", "WPA 772 Water Pump Assy", "bertolini TTL triplex high pressure plunger pumps", "Generic plunger high pressure pump", "Apple Normal, Banana", "Cast Iron KSb centrifugal pump", "5.5kw Water Pump", "KSB reliable i line centrifuged pumps", "Apple Normal, Orange, Banana", "Positive API 6745 hydraulic diaphragm pump", "1/2 inch Fuel Hose Pipe", "Kirloskar Water Pump", "Rotodel motor pump", "PVC Electrical Insulation Materials", "Electric kirloskar domestic water pump", "Electrical Insulation Materials", "sellowell motor pump", "bhupathi submersible pump set", "Flowshine Submersible pump set", "Index submersible pump", "Wintoss Plastic Electric Switch Board", "Electric 18 watt ujagar cooler pump", "Generator Service", "LG WM FHT1207ZWL, LG REF GL-S292RSCY", "Water tank, Filters, Water Pump", "MS Control Submersible Panel", "Centrifugal Monoblock Pumps", "Electric Motor with Pump BodyBlue and White", "Various Repair and Maintenance Parts", "Earthmax Pump", "Water Tank, Filters, Water Pump", "Centrifugal Water Pump for Agriculture", "mono block pumps" ] # Salesforce credentials SALESFORCE_USERNAME = "venkatramana@sandbox.com" SALESFORCE_PASSWORD = "Venkat12345@" SALESFORCE_SECURITY_TOKEN = "GhcJJmjBEefdnukJoz4CAQlR" # Initialize PaddleOCR ocr = PaddleOCR(use_angle_cls=True, lang='en') # 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 match product name using fuzzy matching def match_product_name(extracted_text): best_match = None best_score = 0 for line in extracted_text.split("\n"): match, score = process.extractOne(line, PRODUCT_NAMES) if score > best_score: best_match = match best_score = score return best_match if best_score >= 70 else None # Threshold of 70 for a match # Function to extract attributes and their values def extract_attributes(extracted_text): attributes = {} for readable_attr, sf_attr in ATTRIBUTE_MAPPING.items(): pattern = rf"{re.escape(readable_attr)}[:\-]?\s*(.+)" match = re.search(pattern, extracted_text, re.IGNORECASE) if match: attributes[readable_attr] = match.group(1).strip() return attributes # Function to filter attributes for valid Salesforce fields def filter_valid_attributes(attributes, valid_fields): return {ATTRIBUTE_MAPPING[key]: value for key, value in attributes.items() if ATTRIBUTE_MAPPING[key] in valid_fields} #📊 Function to interact with Salesforce based on mode and type def interact_with_salesforce(mode, entry_type, quantity, extracted_text): try: sf = Salesforce( username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN ) # Mapping mode and entry_type to Salesforce object and field object_name = None field_name = None if mode == "Entry": if entry_type == "Sales": object_name = "VENKATA_RAMANA_MOTORS__c" field_name = "Quantity__c" elif entry_type == "Non-Sales": object_name = "UNBILLING_DATA__c" field_name = "TotalQuantity__c" # Get valid fields from Salesforce object schema sf_object = sf.__getattr__(object_name) schema = sf_object.describe() valid_fields = {field["name"] for field in schema["fields"]} # Extract attributes from the extracted text attributes = extract_attributes(extracted_text) # Ensure Product Name is extracted and mapped correctly product_name = match_product_name(extracted_text) # Match product name using fuzzy logic if not product_name: return "❌ Product Name could not be extracted from the image. Please check the image." # Assign Product Name to the correct Salesforce field if entry_type == "Sales": attributes["Product_Name__c"] = product_name # For VENKATA_RAMANA_MOTORS__c elif entry_type == "Non-Sales": attributes["Productname__c"] = product_name # For UNBILLING_DATA__c # Convert extracted keys to match Salesforce API field names mapped_attributes = {} for key, value in attributes.items(): sf_field_name = ATTRIBUTE_MAPPING.get(key, key.replace(" ", "_") + "__c") # Convert to Salesforce format if sf_field_name in valid_fields: mapped_attributes[sf_field_name] = value # Only keep valid fields # Ensure Product Name and Quantity are added to mapped attributes mapped_attributes["Product_Name__c"] = product_name # Product Name field mapped_attributes[field_name] = quantity # Quantity field # Ensure at least one valid field exists if not mapped_attributes: return "❌ No valid attributes found to export." # Creating a new record with all valid attributes sf_object.create(mapped_attributes) return f"✅ Record created in {object_name} with extracted valid attributes, Product Name: {product_name}, and Quantity: {quantity}." except Exception as e: return f"❌ Error interacting with Salesforce: {str(e)}" elif mode == "Exit": if entry_type == "Sales": object_name = "Inventory_Management__c" field_name = "Quantity_Sold__c" elif entry_type == "Non-Sales": object_name = "Un_Billable__c" field_name = "Sold_Out__c" # Extract product name product_name = match_product_name(extracted_text) if not product_name: return "Product name could not be matched from the extracted text." query = f"SELECT Id, {field_name} FROM {object_name} WHERE Product_Name__c = '{product_name}' LIMIT 1" response = sf.query(query) if response["records"]: record_id = response["records"][0]["Id"] updated_quantity = quantity sf.__getattr__(object_name).update(record_id, {field_name: updated_quantity}) return f"✅ Updated record for product '{product_name}' in {object_name}. New {field_name}: {updated_quantity}." else: return f"❌ No matching record found for product '{product_name}' in {object_name}." except Exception as e: return f"❌ Error interacting with Salesforce: {str(e)}" # Function to pull structured data from Salesforce and display as a table def pull_data_from_salesforce(): try: sf = Salesforce( username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN ) query = "SELECT Product_Name__c, Modal_Name__c, Current_Stocks__c FROM Inventory_Management__c LIMIT 100" response = sf.query_all(query) records = response.get("records", []) if not records: return "No data found in Salesforce.", None, None, None df = pd.DataFrame(records) df = df.drop(columns=['attributes'], errors='ignore') # Rename columns for better readability df.rename(columns={ "Product_Name__c": "Product Name", "Modal_Name__c": "Model Name", "Current_Stocks__c": "Current Stocks" }, inplace=True) excel_path = "salesforce_data.xlsx" df.to_excel(excel_path, index=False) # Generate interactive vertical bar graph using Matplotlib fig, ax = plt.subplots(figsize=(12, 8)) df.plot(kind='bar', x="Product Name", y="Current Stocks", ax=ax, legend=False) ax.set_title("Stock Distribution by Product Name") ax.set_xlabel("Product Name") ax.set_ylabel("Current Stocks") plt.xticks(rotation=45, ha="right", fontsize=10) plt.tight_layout() buffer = BytesIO() plt.savefig(buffer, format="png") buffer.seek(0) img = Image.open(buffer) return "Data successfully retrieved.", df, excel_path, img except Exception as e: return f"Error fetching data: {str(e)}", None, None, None # Unified function to handle image processing and Salesforce interaction def process_image(image, mode, entry_type, quantity): extracted_text = extract_text(image) if not extracted_text: return "No text detected in the image.", None product_name = match_product_name(extracted_text) attributes = extract_attributes(extracted_text) if product_name: attributes["Product name"] = product_name # Interact with Salesforce message = interact_with_salesforce(mode, entry_type, quantity, extracted_text) numbered_output = "\n".join([f"{key}: {value}" for key, value in attributes.items()]) return f"Extracted Text:\n{extracted_text}\n\nAttributes and Values:\n{numbered_output}", message # Gradio Interface def app(): return gr.TabbedInterface([ gr.Interface( fn=process_image, inputs=[ gr.Image(type="numpy", label="📄 Upload Image"), gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry"), gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales"), gr.Number(label="🔢 Quantity", value=1, interactive=True), ], outputs=[ gr.Text(label="📝 Extracted Image Data"), gr.Text(label="🚀 Result") ], title="🏢 Inventory Management", description="📦 Inventory Management System" ), gr.Interface( fn=pull_data_from_salesforce, inputs=[], outputs=[ gr.Text(label="Status"), gr.Dataframe(label="📦 Salesforce Data Table"), gr.File(label="Download Salesforce Data"), gr.Image(label="📉 Stock Distribution Bar Graph") ], title="📊 Salesforce Data Export", description="View, visualize (zoom-in/out), and download Salesforce data (Product Name, Model Name, Current Stocks)." ) ], ["📥 OCR Processing", "📊 Salesforce Data Export"]) if __name__ == "__main__": app().launch(share=True)