Create app.py

app.py

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+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)
+