Update app.py

app.py

@@ -11,125 +11,430 @@ from fuzzywuzzy import process
 SALESFORCE_USERNAME = os.getenv("SALESFORCE_USERNAME")
 SALESFORCE_PASSWORD = os.getenv("SALESFORCE_PASSWORD")
 SALESFORCE_SECURITY_TOKEN = os.getenv("SALESFORCE_SECURITY_TOKEN")
+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
 
-# Initialize PaddleOCR
-ocr = PaddleOCR(use_angle_cls=True, lang='en')
+#📌 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"
+}
 
-# Product names list for fuzzy matching
-PRODUCT_NAMES = ["Honda Activa", "Yamaha R15", "Suzuki Access", "Hero Splendor"]
+# 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 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"),
-}
+#🧠 Initialize PaddleOCR
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
-# Extract text from image
+# Function to extract text using PaddleOCR
 def extract_text(image):
     result = ocr.ocr(image)
-    return "\n".join([line[1][0] for line in result[0]])
+    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 = {}
 
-# Fuzzy match product name
-def match_product_name(text):
-    match, score = process.extractOne(text, PRODUCT_NAMES)
-    return match if score >= 70 else "Unknown"
+    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()
 
-# Salesforce Interaction
+    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)
-        obj_key = f"{mode}_{entry_type}"
-        object_name, field_name = OBJECT_MAPPING.get(obj_key, (None, None))
-        
-        if not object_name:
+        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)
-        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}"
+        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
         else:
-            sf.__getattr__(object_name).create({"Product_Name__c": product_name, field_name: quantity})
-            return f"✅ Data exported to {object_name}."
+            return None
     except Exception as e:
-        return f"❌ Salesforce Error: {str(e)}"
+        return None
 
-# Process image and update Salesforce
+# 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
 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}"
 
-# Fetch Salesforce Data & Generate Graph
-def fetch_salesforce_data():
-    try:
-        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"
-        
-        # Log the query being executed
-        response = sf.query_all(query)
-        
-        # If no records found
-        if not response['records']:
-            return "No data found in Salesforce."
-
-        df = pd.DataFrame(response["records"]).drop(columns=['attributes'], errors='ignore')
-        df.rename(columns={"Product_Name__c": "Product Name", "Current_Stocks__c": "Stock Quantity"}, inplace=True)
-        
-        # Create a dynamic and interactive bar chart
-        fig = px.bar(df, x="Product Name", y="Stock Quantity", 
-                     title="Stock Distribution",
-                     labels={"Product Name": "Products", "Stock Quantity": "Quantity"},
-                     hover_data=["Product Name", "Stock Quantity"],
-                     color="Stock Quantity",
-                     color_continuous_scale='Viridis')
-        
-        # Make the chart more interactive with hover
-        fig.update_traces(texttemplate='%{x}: %{y}', textposition='outside', hovertemplate='%{x}: %{y} units')
-        
-        # Layout adjustments
-        fig.update_layout(
-            hovermode='closest',
-            xaxis_title="Product",
-            yaxis_title="Stock Quantity",
-            template="plotly_dark",
-            showlegend=False
-        )
-        
-        return fig.to_html(full_html=False)
-    except Exception as e:
-        return f"Error fetching Salesforce data: {str(e)}"
+    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 with Colorful UI Template
+# Gradio Interface
 def app():
-    with gr.Blocks(theme="huggingface") as interface:
-        gr.Markdown("<h1 style='color:#2A9D8F;'>🏢 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", style={"background-color": "#2A9D8F", "color": "white"})
-            
-            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", style={"background-color": "#E9C46A", "color": "white"})
-            salesforce_graph = gr.HTML(label="📉 Stock Graph")
-            fetch_button.click(fn=fetch_salesforce_data, inputs=[], outputs=[salesforce_graph])
-
-    return interface
+    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"])
+    # 🎨 Bright and Gradient UI with Updated Coloring
+css = """
+    body {
+        background: linear-gradient(135deg, #282c34, #4b79a1);
+        font-family: 'Poppins', sans-serif;
+    }
+
+    .gradio-container {
+        border-radius: 20px;
+        padding: 30px;
+        background: rgba(255, 255, 255, 0.2);
+        backdrop-filter: blur(10px);
+        box-shadow: 0px 8px 30px rgba(0, 123, 255, 0.4);
+        border: 2px solid rgba(255, 255, 255, 0.4);
+    }
+
+    .gradio-title {
+        font-size: 48px;
+        font-weight: bold;
+        text-align: center;
+        background: linear-gradient(90deg, #ff7eb3, #ff758c);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        text-shadow: 5px 5px 20px rgba(255, 105, 180, 0.6);
+        margin-bottom: 20px;
+        animation: glow 2s infinite alternate;
+    }
+
+    @keyframes glow {
+        from {
+            text-shadow: 5px 5px 30px rgba(255, 87, 134, 0.6);
+        }
+        to {
+            text-shadow: 6px 6px 40px rgba(255, 54, 90, 1);
+        }
+    }
+
+    .gradio-box {
+        border-radius: 15px;
+        padding: 25px;
+        background: linear-gradient(135deg, #6a11cb, #2575fc);
+        box-shadow: 0px 6px 25px rgba(30, 144, 255, 0.6);
+        border: 2px solid #6a5acd;
+        color: white;
+        font-size: 18px;
+    }
+
+    .gradio-button {
+        border-radius: 12px;
+        padding: 18px 36px;
+        font-size: 20px;
+        font-weight: bold;
+        color: #fff;
+        background: linear-gradient(135deg, #1e90ff, #00bfff);
+        box-shadow: 0px 6px 25px rgba(0, 191, 255, 0.6);
+        transition: all 0.3s ease-in-out;
+    }
+
+    .gradio-button:hover {
+        background: linear-gradient(135deg, #00bfff, #1e90ff);
+        box-shadow: 0px 10px 35px rgba(0, 191, 255, 0.9);
+        transform: scale(1.1);
+    }
+
+    .gradio-input {
+        border-radius: 10px;
+        padding: 16px;
+        font-size: 18px;
+        background: rgba(255, 255, 255, 0.3);
+        border: 2px solid rgba(0, 123, 255, 0.5);
+        color: #fff;
+        transition: 0.3s;
+    }
+
+    .gradio-input:focus {
+        border: 2px solid #1e90ff;
+        outline: none;
+        box-shadow: 0px 5px 20px rgba(30, 144, 255, 0.6);
+    }
+
+    .gradio-output {
+        background: linear-gradient(135deg, #f39c12, #e74c3c);
+        padding: 22px;
+        border-radius: 15px;
+        color: white;
+        font-size: 20px;
+        text-align: center;
+        border: 2px solid #f39c12;
+    }
+
+    .gradio-file {
+        background: linear-gradient(135deg, #8e44ad, #3498db);
+        color: white;
+        padding: 16px;
+        border-radius: 15px;
+        text-align: center;
+        font-size: 18px;
+    }
+"""
+
+
+
 
 if __name__ == "__main__":
-    app().launch(share=True)
+    app().launch(share=True)