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",
    "H.P.": "H_p__c"
}

# List of product names to match
PRODUCT_NAMES = [
    "Fusion", "Agroking",  "CG commercial motors", "Jaguar",  "Gaurav"
]

# Salesforce credentials
SALESFORCE_USERNAME = "Venkatramana@sandbox.com"
SALESFORCE_PASSWORD = "Seta12345@"
SALESFORCE_SECURITY_TOKEN = "Drl0jchCwLBfvX4ODMeFDksP"
domain='login'

# 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, attributes):
    try:
        sf = Salesforce(
            username=SALESFORCE_USERNAME,
            password=SALESFORCE_PASSWORD,
            security_token=SALESFORCE_SECURITY_TOKEN
        )

        object_name = None
        field_name = None
        field_names = []
        product_field_name = "Productname__c"
        model_field_name = "Model__c"
        stage_field_name = "Stage__c"
        hp_field_name = "H_p__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"
        elif mode == "Exit":
            if entry_type == "Sales":
                object_name = "Inventory_Management__c"
                field_names = ["Quantity_Sold__c", "soldstock__c"]
            elif entry_type == "Non-Sales":
                object_name = "Un_Billable__c"
                field_names = ["Sold_Out__c", "soldstock__c"]

        if not object_name or (not field_name and not field_names):
            return "Invalid mode or entry type."

        sf_object = sf.__getattr__(object_name)
        schema = sf_object.describe()
        valid_fields = {field["name"] for field in schema["fields"]}

        filtered_attributes = filter_valid_attributes(attributes, valid_fields)

        if mode == "Exit":
            query_conditions = [f"{product_field_name} = '{attributes['Product name']}'"]
            if "Model Name" in attributes and attributes["Model Name"]:
                query_conditions.append(f"{model_field_name} = '{attributes['Model Name']}'")
            if "Stage" in attributes and attributes["Stage"]:
                query_conditions.append(f"{stage_field_name} = '{attributes['Stage']}'")
            if "H.P." in attributes and attributes["H.P."]:
                query_conditions.append(f"{hp_field_name} = '{attributes['H.P.']}'")

            query = f"SELECT Id, {', '.join(field_names)} FROM {object_name} WHERE {' AND '.join(query_conditions)} LIMIT 1"
            response = sf.query(query)

            if response["records"]:
                record_id = response["records"][0]["Id"]
                updated_fields = {field: quantity for field in field_names}
                sf_object.update(record_id, updated_fields)
                return f"✅ Updated record for product '{attributes['Product name']}' in {object_name}. Updated fields: {updated_fields}."
            else:
                return f"❌ No matching record found for product '{attributes['Product name']}' in {object_name}."

        else:
            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 handle edited attributes and export to Salesforce
def export_to_salesforce(mode, entry_type, quantity, edited_df):
    try:
        # Convert edited DataFrame back to dictionary
        edited_attributes = dict(zip(edited_df["Attribute"], edited_df["Value"]))
        
        # Export to Salesforce
        message = interact_with_salesforce(mode, entry_type, quantity, edited_attributes)
        return message
    except Exception as e:
        return f"❌ Error exporting to Salesforce: {str(e)}"

# ... [unchanged imports and setup code above]

# Function to process image, extract attributes, and allow editing
def process_image(image, mode, entry_type, quantity):
    extracted_text = extract_text(image)
    if not extracted_text:
        # Return defaults matching: Text, Dataframe, Text
        return "No text detected in the image.", pd.DataFrame(columns=["Attribute", "Value"]), ""

    product_name = match_product_name(extracted_text)
    attributes = extract_attributes(extracted_text)
    if product_name:
        attributes["Product name"] = product_name

    # Ensure fixed attributes are present
    for fixed_attr in ["Stage", "H.P.", "Product name", "Model"]:
        if fixed_attr not in attributes:
            attributes[fixed_attr] = ""

    # Convert attributes to DataFrame for editing
    df = pd.DataFrame(list(attributes.items()), columns=["Attribute", "Value"])
    return f"Extracted Text:\n{extracted_text}", df, ""

# Function to pull structured data from Salesforce and display as a table
def pull_data_from_salesforce(data_type):
    try:
        sf = Salesforce(
            username=SALESFORCE_USERNAME,
            password=SALESFORCE_PASSWORD,
            security_token=SALESFORCE_SECURITY_TOKEN
        )

        if data_type == "Inventory":
            query = "SELECT Productname__c,Model__c, H_p__c, Stage__c, Current_Stocks__c, soldstock__c FROM Inventory_Management__c LIMIT 100"
        else:
            query = "SELECT Productname__c, Model__c, H_p__c, Stage__c, Current_Stock__c, soldstock__c FROM Un_Billable__c LIMIT 100"

        response = sf.query_all(query)
        records = response.get("records", [])

        if not records:
            # Return defaults: Dataframe, FilePath, Image
            empty_df = pd.DataFrame(columns=["Product Name", "Model", "H.P", "Stage", "Current Stocks", "Sold Stock"])
            return empty_df, "", None

        df = pd.DataFrame(records)
        df = df.drop(columns=['attributes'], errors='ignore')

        # Rename columns for better readability
        df.rename(columns={
            "Productname__c": "Product Name",
            "Model__c": "Model",
            "H_p__c": "H.P",
            "Stage__c": "Stage",
            "Current_Stocks__c": "Current Stocks",
            "Current_Stock__c": "Current Stocks",
            "soldstock__c": "Sold Stock"
        }, inplace=True)

        excel_path = "salesforce_data.xlsx"
        df.to_excel(excel_path, index=False)

        # Generate 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 df, excel_path, img

    except Exception as e:
        # Return safe defaults if something breaks
        empty_df = pd.DataFrame(columns=["Product Name", "Model", "H.P", "Stage", "Current Stocks", "Sold Stock"])
        return empty_df, "", None

# Rest of your code remains unchanged below
# (export_to_salesforce, app(), __main__)

        
        # 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 df, excel_path, img
    except Exception as e:
        return f"Error fetching data: {str(e)}", None, None, None

# Gradio Interface
def app():
    with gr.Blocks() as demo:
        with gr.Tab("📥 OCR Processing"):
            with gr.Row():
                image_input = gr.Image(type="numpy", label="📄 Upload Image")
                mode_input = gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry")
                entry_type_input = gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales")
                quantity_input = gr.Number(label="🔢 Quantity", value=1, interactive=True)
            extract_button = gr.Button("Extract Text and Attributes")
            extracted_text_output = gr.Text(label="📝 Extracted Image Data")
            editable_df_output = gr.Dataframe(label="✏️ Edit Attributes (Key-Value Pairs)", interactive=True)
            ok_button = gr.Button("OK")
            result_output = gr.Text(label="🚀 Result")

        with gr.Tab("📊 Salesforce Data"):
            data_type_input = gr.Dropdown(label="Select Data Type", choices=["Inventory", "Unbilling"], value="Inventory")
            pull_button = gr.Button("Pull Data from Salesforce")
            salesforce_data_output = gr.Dataframe(label="📊 Salesforce Data")
            excel_download_output = gr.File(label="📥 Download Excel")
            graph_output = gr.Image(label="📈 Stock Distribution Graph")

        # Define button actions
        extract_button.click(
            fn=process_image,
            inputs=[image_input, mode_input, entry_type_input, quantity_input],
            outputs=[extracted_text_output, editable_df_output, result_output]
        )
        ok_button.click(
            fn=export_to_salesforce,
            inputs=[mode_input, entry_type_input, quantity_input, editable_df_output],
            outputs=[result_output]
        )
        pull_button.click(
            fn=pull_data_from_salesforce,
            inputs=[data_type_input],
            outputs=[salesforce_data_output, excel_download_output, graph_output]
        )

    return demo

if __name__ == "__main__":
    app().launch(share=True)