app.py

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 = {
    "Product name": "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
                product_name_field = "Productname__c"

                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)

                    # Extract product name explicitly
                    product_name = match_product_name(extracted_text)
                    if product_name and product_name_field in valid_fields:
                        attributes[product_name_field] = product_name

                    # Convert extracted keys to match Salesforce API field names
                    mapped_attributes = {}
                    for key, value in attributes.items():
                        field_name_sf = key.replace(" ", "_") + "__c"  # Convert to Salesforce format
                        if field_name_sf in valid_fields:
                            mapped_attributes[field_name_sf] = value  # Only keep valid fields

                    mapped_attributes[field_name] = quantity  # Ensure Quantity is added

                    if not mapped_attributes:
                        return "No valid attributes found to export."

                    # Creating a new record with only valid attributes
                    sf_object.create(mapped_attributes)
                    return f"✅ Record created in {object_name} with extracted valid attributes and Quantity: {quantity}."

                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)