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 plotly.graph_objects as go
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_field_name = "Product_Name__c"  # Correct field for product name in the object
        model_field_name = "Modal_Name__c"  # Correct field for model name in the object

        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"
        elif mode == "Exit":
            if entry_type == "Sales":
                object_name = "Inventory_Management__c"
                product_field_name = "Product_Name__c"
                model_field_name = "Modal_Name__c"
                field_name = "Quantity_Sold__c"
            elif entry_type == "Non-Sales":
                object_name = "Un_Billable__c"
                product_field_name = "Product_Name__c"
                model_field_name = "Model_Name__c"
                field_name = "Sold_Out__c"

        if not object_name or not field_name:
            return "Invalid mode or entry type."

        # Get valid fields for the specified Salesforce object
        sf_object = sf.__getattr__(object_name)
        schema = sf_object.describe()
        valid_fields = {field["name"] for field in schema["fields"]}

        # Extract product name or model number
        product_name = match_product_name(extracted_text)
        attributes = extract_attributes(extracted_text)

        if not product_name:
            return "Product name could not be matched from the extracted text."

        attributes["Product name"] = product_name

        if mode == "Exit":
            query = f"SELECT Id, {field_name} FROM {object_name} WHERE {product_field_name} = '{product_name}' OR {model_field_name} = '{attributes.get('Model Name', '')}' LIMIT 1"
            response = sf.query(query)

            if response["records"]:
                record_id = response["records"][0]["Id"]
                updated_quantity = quantity  # Overwrite the quantity, don't add
                sf_object.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}."
        else:
            filtered_attributes = filter_valid_attributes(attributes, valid_fields)
            filtered_attributes[field_name] = quantity
            sf_object.create(filtered_attributes)
            return f"✅ Data successfully exported to Salesforce object {object_name}."

    except Exception as e:
        return f"❌ Error interacting with Salesforce: {str(e)}"

# Function to pull data from Salesforce MotorDataAPI
def pull_data_from_motor_api():
    try:
        sf = Salesforce(
            username=SALESFORCE_USERNAME,
            password=SALESFORCE_PASSWORD,
            security_token=SALESFORCE_SECURITY_TOKEN
        )
        motor_data = sf.apexecute("MotorDataAPI/", method="GET")
        return motor_data  # API returns the list of records
    except Exception as e:
        return f"Error pulling data from MotorDataAPI: {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 bar graph using Plotly with Tooltip, Hover Effect, and Zooming
        fig = go.Figure()
        fig.add_trace(go.Bar(
            x=df['Product Name'],
            y=df['Current Stocks'],
            marker=dict(color='blue'),
            hoverinfo='x+y',
            hovertemplate='<b>Product Name:</b> %{x}<br><b>Current Stocks:</b> %{y}<extra></extra>',
            text=df['Current Stocks'],
            textposition='outside'
        ))
       
        fig.update_layout(
            title="Current Stocks of Products",
            xaxis=dict(title="Product Name", tickangle=-45),
            yaxis=dict(title="Stock Quantity"),
            hovermode='x',
            dragmode='zoom',
            showlegend=False
        )
       
        return "Data successfully retrieved.", df, excel_path, fig
    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=False),
            ],
            outputs=[
                gr.Text(label="Extracted Text"),
                gr.Text(label="Result")
            ],
            title="Image Text Extraction",
            description="Upload an image and extract text using OCR."
        ),
        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.Plot(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)