Update app.py

app.py

@@ -10,113 +10,13 @@ import matplotlib.pyplot as plt
 from io import BytesIO
 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 credentials
 SALESFORCE_USERNAME = "venkatramana@sandbox.com"
 SALESFORCE_PASSWORD = "Venkat12345@"
 SALESFORCE_SECURITY_TOKEN = "GhcJJmjBEefdnukJoz4CAQlR"
 
-#🧠 Initialize PaddleOCR
-ocr = PaddleOCR(
-    use_angle_cls=False,  # Disable angle classification to reduce memory
-    lang='en',
-    det_model_dir=None,
-    rec_model_dir=None,
-    cls_model_dir=None
-)
-
+# Initialize PaddleOCR
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
 # Function to extract text using PaddleOCR
 def extract_text(image):
@@ -126,365 +26,95 @@ def extract_text(image):
         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):
+# Function to fetch data from Salesforce and format it into a DataFrame
+def fetch_salesforce_data():
     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"]
-                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
-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"
+        query = "SELECT Product_Name__c, Modal_Name__c, Current_Stocks__c FROM MotorDataAPI__c"
         response = sf.query_all(query)
-       
-        records = response.get("records", [])
-        if not records:
-            return "No data found in Salesforce.", None, None, None
-       
+        records = response["records"]
         df = pd.DataFrame(records)
-        df = df.drop(columns=['attributes'], errors='ignore')
-       
-        df.rename(columns={
+        df = 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
-        fig = go.Figure()
-        fig.add_trace(go.Bar(
-            x=df['Product Name'],
-            y=df['Current Stocks'],
-            marker=dict(color='blue'),
-            hoverinfo='x+y',
-            hovertemplate='<b>Product Name:</b> %{x}<br><b>Current Stocks:</b> %{y}<extra></extra>',
-            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[["Product Name", "Model Name", "Current Stocks"]]
     except Exception as e:
-        return f"Error fetching data: {str(e)}", None, None, None
+        return None
 
+# Function to generate a bar graph from Salesforce data
+def generate_salesforce_bar_chart(df):
+    try:
+        plt.figure(figsize=(12, 6))
+        df.plot(kind='bar', x="Product Name", y="Current Stocks", legend=False)
+        plt.title("Stock Distribution by Product Name")
+        plt.xlabel("Product Name")
+        plt.ylabel("Current Stocks")
+        plt.xticks(rotation=45, ha="right")
+        plt.tight_layout()
+        buffer = BytesIO()
+        plt.savefig(buffer, format="png")
+        buffer.seek(0)
+        return Image.open(buffer)
+    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
-
-
-import gradio as gr
-import numpy as np
-from PIL import Image
+    
+    return f"Extracted Text:\n{extracted_text}\n\nProcessing Completed.", None
 
 # Function to fetch and return Salesforce data
-def generate_salesforce_data():
-    df = format_salesforce_data()
+def get_salesforce_visualization():
+    df = fetch_salesforce_data()
     if df is not None:
         table_component = df.to_html(index=False)
-        bar_graph_image = generate_bar_graph(df)
-        return table_component, bar_graph_image
+        bar_chart_image = generate_salesforce_bar_chart(df)
+        return table_component, bar_chart_image
     return "<p>No Data Available</p>", None
 
-import gradio as gr
-
 def app():
-    with gr.Blocks(css="""
-        /* General Styling */
-        .gradio-container { 
-            background: #0b0f29; /* Deep Dark Blue */
-            font-family: 'Poppins', sans-serif;
-            color: white;
-            padding: 10px;
-        }
-
-        h1, h2, h3 {
-            text-align: center;
-            color: white;
-            font-weight: bold;
-            text-shadow: 0px 0px 10px rgba(255,255,255,0.8);
-        }
-
-        /* Cards & Elements */
-        .card {
-            background: rgba(255, 255, 255, 0.1);
-            border-radius: 15px;
-            padding: 20px;
-            width: 100%;
-            max-width: 900px;
-            margin: auto;
-            box-shadow: 0px 0px 25px rgba(0, 255, 255, 0.6);
-            transition: 0.3s;
-        }
-
-        .card:hover {
-            box-shadow: 0px 0px 30px rgba(0, 255, 255, 0.9);
-        }
-
-        /* Buttons */
-        .gradio-button {
-            background: linear-gradient(to right, #ff416c, #ff4b2b);
-            color: white !important;
-            border-radius: 10px !important;
-            font-weight: bold;
-            padding: 12px;
-            transition: all 0.3s ease-in-out;
-            box-shadow: 0px 0px 20px rgba(255, 0, 255, 0.6);
-        }
-
-        .gradio-button:hover {
-            background: linear-gradient(to right, #ff4b2b, #ff416c);
-            box-shadow: 0px 0px 30px rgba(255, 0, 255, 0.9);
-        }
-
-        /* Tabs */
-        .gradio-tab {
-            font-size: 16px;
-            color: white;
-            font-weight: bold;
-            text-shadow: 0px 0px 10px rgba(255, 255, 255, 0.6);
-        }
-
-        /* Scrollable Table */
-        .table-container {
-            width: 100%;
-            max-width: 900px;
-            max-height: 400px;
-            overflow-y: auto;
-            overflow-x: auto;
-            display: flex;
-            justify-content: center;
-            margin: auto;
-        }
-
-        table {
-            width: 100%;
-            border-collapse: collapse;
-            background: rgba(255, 255, 255, 0.1);
-            color: white;
-            min-width: 600px;
-        }
-
-        th, td {
-            padding: 12px;
-            text-align: left;
-            border-bottom: 1px solid rgba(255, 255, 255, 0.2);
-            font-size: 14px;
-        }
-
-        th {
-            background: rgba(0, 255, 255, 0.2);
-        }
-
-        /* Mobile View */
-        @media (max-width: 768px) {
-            .gradio-container {
-                padding: 10px;
-            }
-
-            .card {
-                width: 100%;
-                max-width: 100%;
-                padding: 10px;
-            }
-
-            .table-container {
-                max-height: 350px;
-                overflow-y: scroll;
-            }
-
-            table {
-                width: 100%;
-                min-width: 100%;
-            }
-
-            th, td {
-                font-size: 12px;
-                padding: 8px;
-            }
-        }
-
-    """) as interface:
-
+    with gr.Blocks() as interface:
         gr.Markdown("<h1>🏢 VENKATARAMANA MOTORS</h1>")
 
-        # Tabbed Interface
-        interface = gr.TabbedInterface(
-            [
-                # Tab 1: Stock Entry & Processing
-                gr.Interface(
-                    fn=process_image,  # Replace with your actual function
-                    inputs=[
-                        gr.Image(type="numpy", label="📄 Upload Image", elem_classes="card"),
-                        gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry", elem_classes="card"),
-                        gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales", elem_classes="card"),
-                        gr.Number(label="🔢 Quantity", value=1, interactive=True, elem_classes="card"),
-                    ],
-                    outputs=[
-                        gr.Text(label="📜 Image Data Viewer", interactive=False, elem_classes="card"),
-                        gr.Text(label="📝 Processed Result", interactive=False, elem_classes="card"),
-                    ],
-                    title="Stock Entry & Processing",
-                    description="Upload an image to extract product details and update Salesforce."
-                ),
-
-                # Tab 2: Salesforce Data Visualization
-                gr.Interface(
-                    fn=pull_data_from_salesforce,  # Replace with your actual function
-                    inputs=[],
-                    outputs=[
-                        gr.Text(label="Status", elem_classes="card"),
-                        gr.Dataframe(label="Sales Data Table", elem_classes="card"),
-                        gr.File(label="Download Salesforce Data", elem_classes="card"),
-                        gr.Plot(label="Stock Distribution Bar Graph", elem_classes="card"),
-                    ],
-                    title="Salesforce Data Visualization",
-                    description="View, visualize, and download Salesforce data (Product Name, Model Name, Current Stocks)."
-                )
-            ],
-            ["Stock Entry & Processing", "Salesforce Data Visualization"]
-        )
-
-    return interface
-import gradio as gr
-
-def sample_function(input_text):
-    return f"Processed: {input_text}"
-
-with gr.Blocks() as app:
-    gr.Markdown("## My Gradio App")
-    input_box = gr.Textbox(label="Enter something")
-    output_box = gr.Textbox(label="Output")
-    button = gr.Button("Submit")
-    button.click(sample_function, inputs=input_box, outputs=output_box)
+        with gr.Tab("📥 Stock Entry & Processing"):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("<h3>📌 Upload & Process</h3>")
+                    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, interactive=True)
+                
+                with gr.Column():
+                    gr.Markdown("<h3>📊 Processed Data</h3>")
+                    image_view = gr.Text(label="📜 Image Data Viewer", interactive=False)
+                    result_output = gr.Text(label="📝 Processed Result", interactive=False)
+                    submit_button = gr.Button("🔍 Process Image")
+
+        with gr.Tab("📊 Salesforce Data Overview"):
+            gr.Markdown("<h3>📑 Stock Table</h3>")
+            with gr.Row():
+                salesforce_table = gr.HTML(label="📦 Salesforce Data Table")
+
+            gr.Markdown("<h3>📈 Inventory Analytics</h3>")
+            with gr.Row():
+                salesforce_graph = gr.Image(type="pil", label="📉 Stock Distribution Bar Graph")
+            
+            generate_button = gr.Button("⚡ Generate Data")
+
+        generate_button.click(fn=get_salesforce_visualization, inputs=[], outputs=[salesforce_table, salesforce_graph])
+        submit_button.click(fn=process_image, inputs=[image_input, mode_dropdown, entry_type_radio, quantity_input], outputs=[image_view, result_output])
+        
+        return interface
 
 if __name__ == "__main__":
-    app.launch(server_name="0.0.0.0", server_port=7860, share=True)
-
-
-
+    app().launch(share=True)