Update app.py

app.py

@@ -9,6 +9,100 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from io import BytesIO
 from fuzzywuzzy import process
+import kaleido  # Ensure kaleido is imported
+
+# Attribute mappings: readable names to Salesforce API names
+ATTRIBUTE_MAPPING = {
+    "Productname": "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"
@@ -26,92 +120,210 @@ def extract_text(image):
         extracted_text.append(line[1][0])
     return "\n".join(extracted_text)
 
-# Function to fetch data from Salesforce and format it into a DataFrame
-def fetch_salesforce_data():
+# 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
         )
-        query = "SELECT Product_Name__c, Modal_Name__c, Current_Stocks__c FROM MotorDataAPI__c"
-        response = sf.query_all(query)
-        records = response["records"]
-        df = pd.DataFrame(records)
-        if not df.empty:
-            df = df.rename(columns={
-                "Product_Name__c": "Product Name",
-                "Modal_Name__c": "Model Name",
-                "Current_Stocks__c": "Current Stocks"
-            })
-            return df[["Product Name", "Model Name", "Current Stocks"]]
-        else:
-            return None
-    except Exception as e:
-        print(f"Error fetching Salesforce data: {e}")
-        return None
 
-# Function to generate a bar graph from Salesforce data
-def generate_salesforce_bar_chart(df):
+        # Mapping mode and entry_type to Salesforce object and field
+        object_name = None
+        field_name = None
+
+        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"
+
+            # Get valid fields from Salesforce object schema
+            sf_object = sf.__getattr__(object_name)
+            schema = sf_object.describe()
+            valid_fields = {field["name"] for field in schema["fields"]}
+
+            # Extract attributes from the extracted text
+            attributes = extract_attributes(extracted_text)
+
+                # Explicitly extract Product Name from the attributes
+            product_name = attributes.get("Productname", "").strip()  # Extract 'Productname' from the text
+            if product_name:
+                # Ensure that the Product Name is mapped to the right Salesforce field
+                if entry_type == "Sales":
+                    attributes["Product_Name__c"] = product_name  # For VENKATA_RAMANA_MOTORS__c
+                elif entry_type == "Non-Sales":
+                    attributes["Productname__c"] = product_name  # For UNBILLING_DATA__c
+                
+             # Convert extracted keys to match Salesforce API field names
+            mapped_attributes = {}
+            for key, value in attributes.items():
+                field_name_sf = key.replace(" ", "_") + "__c"  # Convert to Salesforce format
+                if field_name_sf in valid_fields:
+                    mapped_attributes[field_name_sf] = value  # Only keep valid fields
+
+            mapped_attributes[field_name] = quantity  # Ensure Quantity is added
+
+            if not mapped_attributes:
+                return "No valid attributes found to export."
+
+             # Creating a new record with all valid attributes
+            sf_object.create(mapped_attributes)
+            return f"✅ Record created in {object_name} with extracted valid attributes and Quantity: {quantity}."
+
+        elif mode == "Exit":
+            if entry_type == "Sales":
+                object_name = "Inventory_Management__c"
+                field_name = "Quantity_Sold__c"
+            elif entry_type == "Non-Sales":
+                object_name = "Un_Billable__c"
+                field_name = "Sold_Out__c"
+
+            # Extract product name
+            product_name = match_product_name(extracted_text)
+            if not product_name:
+                return "Product name could not be matched from the extracted text."
+
+            query = f"SELECT Id, {field_name} FROM {object_name} WHERE Product_Name__c = '{product_name}' LIMIT 1"
+            response = sf.query(query)
+
+            if response["records"]:
+                record_id = response["records"][0]["Id"]
+                updated_quantity = quantity
+                sf.__getattr__(object_name).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}."
+
+    except Exception as e:
+        return f"❌ Error interacting with Salesforce: {str(e)}"
+# Function to pull structured data from Salesforce and display as a table
+def pull_data_from_salesforce():
     try:
-        plt.figure(figsize=(12, 6))
-        plt.bar(df["Product Name"], df["Current Stocks"], color='blue')
-        plt.title("Stock Distribution by Product Name")
-        plt.xlabel("Product Name")
-        plt.ylabel("Current Stocks")
-        plt.xticks(rotation=45, ha="right")
+        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
+       
+        df = pd.DataFrame(records)
+        df = df.drop(columns=['attributes'], errors='ignore')
+       
+        # 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 vertical bar graph using Matplotlib
+        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)
-        return Image.open(buffer)
+        img = Image.open(buffer)
+       
+        return "Data successfully retrieved.", df, excel_path, img
     except Exception as e:
-        print(f"Error generating bar chart: {e}")
-        return None
-
-# Function to fetch and return Salesforce data
-def get_salesforce_visualization():
-    df = fetch_salesforce_data()
-    if df is not None:
-        table_component = df.to_html(index=False)
-        bar_chart_image = generate_salesforce_bar_chart(df)
-        return table_component, bar_chart_image
-    return "<p>No Data Available</p>", None
+        return f"Error fetching data: {str(e)}", None, None, 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():
-    with gr.Blocks() as interface:
-        gr.Markdown("<h1>🏢 VENKATARAMANA MOTORS</h1>")
-
-        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
+    return gr.TabbedInterface([
+        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="🏢 Inventory Management",
+            description="📦 Inventory Management System"
+        ),
+        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.Image(label="📉 Stock Distribution Bar Graph")
+            ],
+            title="📊 Salesforce Data Export",
+            description="View, visualize (zoom-in/out), and download Salesforce data (Product Name, Model Name, Current Stocks)."
+        )
+    ], ["📥 OCR Processing", "📊 Salesforce Data Export"])
 
 if __name__ == "__main__":
     app().launch(share=True)
+