Update app.py

app.py

@@ -1,426 +1,112 @@
 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
+import plotly.graph_objects as go
+import re
 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"
-}
+# Load Salesforce Credentials Securely
+SALESFORCE_USERNAME = os.getenv("SALESFORCE_USERNAME")
+SALESFORCE_PASSWORD = os.getenv("SALESFORCE_PASSWORD")
+SALESFORCE_SECURITY_TOKEN = os.getenv("SALESFORCE_SECURITY_TOKEN")
 
-# 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"
-]
+# Initialize PaddleOCR
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
-#📌 Salesforce credentials
-SALESFORCE_USERNAME = "venkatramana@sandbox.com"
-SALESFORCE_PASSWORD = "Venkat12345@"
-SALESFORCE_SECURITY_TOKEN = "GhcJJmjBEefdnukJoz4CAQlR"
+# Product names list for fuzzy matching
+PRODUCT_NAMES = ["Honda Activa", "Yamaha R15", "Suzuki Access", "Hero Splendor"]
 
-#🧠 Initialize PaddleOCR
-ocr = PaddleOCR(use_angle_cls=True, lang='en')
+# Salesforce Object Mapping
+OBJECT_MAPPING = {
+    "Entry_Sales": ("VENKATA_RAMANA_MOTORS__c", "Quantity__c"),
+    "Entry_NonSales": ("UNBILLING_DATA__c", "TotalQuantity__c"),
+    "Exit_Sales": ("Inventory_Management__c", "Quantity_Sold__c"),
+    "Exit_NonSales": ("Un_Billable__c", "Sold_Out__c"),
+}
 
-# Function to extract text using PaddleOCR
+# Extract text from image
 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
+    return "\n".join([line[1][0] for line in result[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
+# Fuzzy match product name
+def match_product_name(text):
+    match, score = process.extractOne(text, PRODUCT_NAMES)
+    return match if score >= 70 else "Unknown"
 
-    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
+# Salesforce Interaction
 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:
+        sf = Salesforce(username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN)
+        obj_key = f"{mode}_{entry_type}"
+        object_name, field_name = OBJECT_MAPPING.get(obj_key, (None, None))
+        
+        if not object_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 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",
-                "Modal_Name__c": "Model Name",
-                "Current_Stocks__c": "Current Stocks"
-            })
-            # Remove unnecessary columns
-            df = df[["Product Name", "Model Name", "Current Stocks"]]
-            return df
+        query = f"SELECT Id, {field_name} FROM {object_name} WHERE Product_Name__c = '{product_name}' LIMIT 1"
+        response = sf.query(query)
+        
+        if mode == "Exit" and response["records"]:
+            record = response["records"][0]
+            record_id = record["Id"]
+            new_quantity = max(0, int(record[field_name]) - quantity)
+            sf.__getattr__(object_name).update(record_id, {field_name: new_quantity})
+            return f"Updated {object_name}: New {field_name} = {new_quantity}"
         else:
-            return None
+            sf.__getattr__(object_name).create({"Product_Name__c": product_name, field_name: quantity})
+            return f"✅ Data exported to {object_name}."
     except Exception as e:
-        return None
+        return f"❌ Salesforce Error: {str(e)}"
 
-# Function to generate a bar graph from Salesforce data
-def generate_bar_graph(df):
-    try:
-        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 img
-    except Exception as e:
-        return None
-
-# Unified function to handle image processing and Salesforce interaction
+# Process image and update Salesforce
 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)
+    return f"Extracted Text:\n{extracted_text}\n\n{message}"
 
-    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
-
-
-def app():
-    df = format_salesforce_data()
-    table_component = None
-    bar_graph_component = None
-
-    if df is not None:
-        table_component = df.to_html(index=False)
-        bar_graph_image = generate_bar_graph(df)
-        if bar_graph_image:
-            bar_graph_component = bar_graph_image
-
-    return gr.TabbedInterface([
-        gr.Interface(
-            fn=process_image,
-            inputs=[
-                gr.Image(type="numpy", label="📄ᴜᴘʟᴏᴀᴅ ɪᴍᴀɢᴇ"),
-                gr.Dropdown(label="📌Mode", choices=["ᴇɴᴛʀʏ", "ᴇxɪᴛ"], 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="ɪᴍᴀɢᴇ ᴅᴀᴛᴀ ᴠɪᴇᴡᴇʀ"),
-                gr.Text(label="📝ＲＥＳＵＬＴ")
-            ],
-            title="𝑽𝑬𝑵𝑲𝑨𝑻𝑨𝑹𝑨𝑴𝑨𝑵𝑨 𝑴𝑶𝑻𝑶𝑹𝑺",
-            description="📦𝐈𝐍𝐕𝐄𝐍𝐓𝐎𝐑𝐘 𝐌𝐀𝐍𝐀𝐆𝐄𝐌𝐄𝐍𝐓"
-        ),
-        gr.Interface(
-            fn=lambda: (table_component, bar_graph_component),
-            inputs=[],
-            outputs=[
-                gr.HTML(label="Salesforce Data Table"),
-                gr.Image(type="pil", label="Stock Distribution Bar Graph")
-            ],
-            title="Salesforce Data",
-            description="View structured Salesforce data as a table and bar graph."
-        )
-    ], ["Processing", "Salesforce Data"])
- 
-      def get_valid_fields(object_name):
+# Fetch Salesforce Data & Generate Graph
+def fetch_salesforce_data():
     try:
-        sf = Salesforce(
-            username=SALESFORCE_USERNAME,
-            password=SALESFORCE_PASSWORD,
-            security_token=SALESFORCE_SECURITY_TOKEN
-        )
-        obj_desc = sf.__getattr__(object_name).describe()
-        return [field["name"] for field in obj_desc["fields"]]
-    except Exception as e:
-        return []
-
-# 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
+        sf = Salesforce(username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN)
+        query = "SELECT Product_Name__c, Current_Stocks__c FROM Inventory_Management__c"
+        records = sf.query_all(query)["records"]
+        df = pd.DataFrame(records).drop(columns=['attributes'], errors='ignore')
+        df.rename(columns={"Product_Name__c": "Product Name", "Current_Stocks__c": "Stock Quantity"}, inplace=True)
         
-        df = pd.DataFrame(records)
-        df = df.drop(columns=['attributes'], errors='ignore')
+        fig = go.Figure(data=[go.Bar(x=df['Product Name'], y=df['Stock Quantity'], text=df['Stock Quantity'], textposition='outside')])
+        fig.update_layout(title="Stock Distribution", xaxis_title="Products", yaxis_title="Quantity", hovermode='x')
         
-        # 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',
-            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
+        return df.to_html(index=False), fig.to_html()
     except Exception as e:
-        return f"Error fetching data: {str(e)}", None, None, None
-
-# Function to process image for OCR extraction
-def process_image(image):
-    extracted_text = extract_text(image)
-    if not extracted_text:
-        return "No text detected in the image."
-    return extracted_text
+        return f"Error fetching Salesforce data: {str(e)}", ""
 
 # Gradio Interface
 def app():
-    return gr.TabbedInterface([
-        gr.Interface(
-            fn=process_image,
-            inputs=[gr.Image(type="numpy", label="Upload Image")],
-            outputs=gr.Text(label="Extracted Text"),
-            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, hover details), and download Salesforce data (Product Name, Model Name, Current Stocks)."
-        )
-    ], ["OCR Processing", "Salesforce Data Export"])
+    with gr.Blocks() as interface:
+        gr.Markdown("<h1>🏢 VENKATARAMANA MOTORS</h1>")
+
+        with gr.Tab("📥 Stock Entry & Processing"):
+            with gr.Row():
+                image_input = gr.Image(type="numpy", label="📄 Upload Image")
+                mode_dropdown = gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry")
+                entry_type_radio = gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales")
+                quantity_input = gr.Number(label="🔢 Quantity", value=1)
+                submit_button = gr.Button("🔍 Process Image")
+            
+            result_output = gr.Text(label="📝 Processed Result")
+            submit_button.click(fn=process_image, inputs=[image_input, mode_dropdown, entry_type_radio, quantity_input], outputs=result_output)
+
+        with gr.Tab("📊 Salesforce Data Overview"):
+            fetch_button = gr.Button("⚡ Fetch Salesforce Data")
+            salesforce_table = gr.HTML(label="📦 Data Table")
+            salesforce_graph = gr.HTML(label="📉 Stock Graph")
+            fetch_button.click(fn=fetch_salesforce_data, inputs=[], outputs=[salesforce_table, salesforce_graph])
+
+    return interface
 
 if __name__ == "__main__":
-    app().launch(share=True)
+    app().launch(share=True)