Update app.py

app.py

@@ -1,38 +1,250 @@
+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 simple_salesforce import Salesforce
 from io import BytesIO
-from PIL import Image
+from fuzzywuzzy import process
+
+# 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"
 
-# Function to pull data from Salesforce and format it into a DataFrame
-def format_salesforce_data():
+# 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"]
+                current_quantity = response["records"][0].get(field_name, 0) or 0
+                updated_quantity = max(0, current_quantity + quantity)
+                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:
-        # Connecting to Salesforce
         sf = Salesforce(
             username=SALESFORCE_USERNAME,
             password=SALESFORCE_PASSWORD,
             security_token=SALESFORCE_SECURITY_TOKEN
         )
-        # Query to get product data from Inventory_Management__c
-        query = "SELECT Product_Name__c, Model_Name__c, Current_Stocks__c FROM Inventory_Management__c"
-        data = sf.query(query)
-        
-        if 'records' in data:
-            # Converting Salesforce data to a pandas DataFrame
-            df = pd.DataFrame(data['records'])
-            # Renaming the columns for better readability
+        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 format Salesforce data into a DataFrame with readable headers
+def format_salesforce_data():
+    try:
+        data = pull_data_from_motor_api()
+        if isinstance(data, list):
+            df = pd.DataFrame(data)
             df = df.rename(columns={
                 "Product_Name__c": "Product Name",
-                "Model_Name__c": "Model Name",
+                "Modal_Name__c": "Model Name",
                 "Current_Stocks__c": "Current Stocks"
             })
-            # Returning only relevant columns
+            # Remove unnecessary columns
             df = df[["Product Name", "Model Name", "Current Stocks"]]
             return df
         else:
@@ -43,7 +255,6 @@ def format_salesforce_data():
 # Function to generate a bar graph from Salesforce data
 def generate_bar_graph(df):
     try:
-        # Generating a bar graph for current stock distribution
         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")
@@ -51,8 +262,6 @@ def generate_bar_graph(df):
         ax.set_ylabel("Current Stocks")
         plt.xticks(rotation=45, ha="right", fontsize=10)
         plt.tight_layout()
-        
-        # Saving the plot into a buffer and converting it to an image
         buffer = BytesIO()
         plt.savefig(buffer, format="png")
         buffer.seek(0)
@@ -61,6 +270,23 @@ def generate_bar_graph(df):
     except Exception as e:
         return 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():
     df = format_salesforce_data()
@@ -68,19 +294,32 @@ def app():
     bar_graph_component = None
 
     if df is not None:
-        # Converting the DataFrame to an HTML table
         table_component = df.to_html(index=False)
-        # Generating a bar graph image
         bar_graph_image = generate_bar_graph(df)
         if bar_graph_image:
             bar_graph_component = bar_graph_image
 
     with gr.Blocks() as demo:
-        with gr.Tab("📊 Salesforce Data Export"):
+        with gr.Tab("📦 𝑷𝑹𝑶𝑪𝑬𝑺𝑺 𝑰𝑴𝑨𝑮𝑬"):
+            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="🏢 VENKATARAMANA MOTORS",
+                description="📦 Inventory Management System"
+            )
+
+        with gr.Tab("📊 SALESFORCE DATA EXPORT"):
             gr.Markdown("## 📊 Salesforce Data Export")
-            # Displaying the table
             table_output = gr.HTML(value=table_component if table_component else "No Data Available")
-            # Displaying the bar graph
             graph_output = gr.Image(value=bar_graph_component if bar_graph_component else None, type="pil")
 
     return demo