Update app.py

app.py

@@ -13,72 +13,72 @@ import kaleido
 
 # 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",
- "H.P.": "H_p__c"
+    "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",
+    "H.P.": "H_p__c"
 }
 
 # List of product names to match
 PRODUCT_NAMES = [
- "Fusion", "Agroking", "CG commercial motors", "Jaguar", "Gaurav"
+    "Fusion", "Agroking", "CG commercial motors", "Jaguar", "Gaurav"
 ]
 
 # Salesforce credentials
@@ -91,239 +91,137 @@ 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)
+    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
+    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
+    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
+    return best_match if best_score >= 70 else None
 
 # Function to extract attributes and their values
 def extract_attributes(extracted_text):
- attributes = {}
+    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()
+    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
+    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, attributes):
- try:
- sf = Salesforce(
- username=SALESFORCE_USERNAME,
- password=SALESFORCE_PASSWORD,
- security_token=SALESFORCE_SECURITY_TOKEN
- )
-
- object_name = None
- field_name = None
- field_names = []
- product_field_name = "Productname__c"
- model_field_name = "Model__c"
- stage_field_name = "Stage__c"
- hp_field_name = "H_p__c"
-
- 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"
- field_names = ["Quantity_Sold__c", "soldstock__c"]
- elif entry_type == "Non-Sales":
- object_name = "Un_Billable__c"
- field_names = ["Sold_Out__c", "soldstock__c"]
-
- if not object_name or (not field_name and not field_names):
- return "Invalid mode or entry type."
-
- sf_object = sf.__getattr__(object_name)
- schema = sf_object.describe()
- valid_fields = {field["name"] for field in schema["fields"]}
-
- filtered_attributes = filter_valid_attributes(attributes, valid_fields)
-
- if mode == "Exit":
- query_conditions = [f"{product_field_name} = '{attributes['Product name']}'"]
- if "Model Name" in attributes and attributes["Model Name"]:
- query_conditions.append(f"{model_field_name} = '{attributes['Model Name']}'")
- if "Stage" in attributes and attributes["Stage"]:
- query_conditions.append(f"{stage_field_name} = '{attributes['Stage']}'")
- if "H.P." in attributes and attributes["H.P."]:
- query_conditions.append(f"{hp_field_name} = '{attributes['H.P.']}'")
-
- query = f"SELECT Id, {', '.join(field_names)} FROM {object_name} WHERE {' AND '.join(query_conditions)} LIMIT 1"
- response = sf.query(query)
-
- if response["records"]:
- record_id = response["records"][0]["Id"]
- updated_fields = {field: quantity for field in field_names}
- sf_object.update(record_id, updated_fields)
- return f"✅ Updated record for product '{attributes['Product name']}' in {object_name}. Updated fields: {updated_fields}."
- else:
- return f"❌ No matching record found for product '{attributes['Product name']}' in {object_name}."
-
- else:
- 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 process image, extract attributes, and allow editing
-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, None
-
- product_name = match_product_name(extracted_text)
- attributes = extract_attributes(extracted_text)
- if product_name:
- attributes["Product name"] = product_name
-
- # Ensure fixed attributes are present
- for fixed_attr in ["Stage", "H.P.", "Product name", "Model"]:
- if fixed_attr not in attributes:
- attributes[fixed_attr] = ""
-
- # Convert attributes to DataFrame for editing
- df = pd.DataFrame(list(attributes.items()), columns=["Attribute", "Value"])
- return f"Extracted Text:\n{extracted_text}", df, None
-
-# Function to handle edited attributes and export to Salesforce
-def export_to_salesforce(mode, entry_type, quantity, edited_df):
- try:
- # Convert edited DataFrame back to dictionary
- edited_attributes = dict(zip(edited_df["Attribute"], edited_df["Value"]))
- 
- # Export to Salesforce
- message = interact_with_salesforce(mode, entry_type, quantity, edited_attributes)
- return message
- except Exception as e:
- return f"❌ Error exporting to Salesforce: {str(e)}"
-
-import pytz
+    return {ATTRIBUTE_MAPPING[key]: value for key, value in attributes.items() if ATTRIBUTE_MAPPING[key] in valid_fields}
 
 # Function to pull structured data from Salesforce and display as a table
 def pull_data_from_salesforce(data_type):
- try:
- sf = Salesforce(
- username=SALESFORCE_USERNAME,
- password=SALESFORCE_PASSWORD,
- security_token=SALESFORCE_SECURITY_TOKEN
- )
- 
- if data_type == "Inventory":
- query = "SELECT Productname__c, Model__c, H_p__c, Stage__c, Current_Stocks__c, soldstock__c, Price__c FROM Inventory_Management__c LIMIT 100"
- else:
- query = "SELECT Productname__c, Model__c, H_p__c, Stage__c, Current_Stock__c, soldstock__c, Price__c FROM Un_Billable__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={
- "Productname__c": "Product Name",
- "Model__c": "Model",
- "H_p__c": "H.P",
- "Stage__c": "Stage",
- "Current_Stocks__c": "Current Stocks",
- "Current_Stock__c": "Current Stocks",
- "soldstock__c": "Sold Stock",
- "Price__c": "Price"
- }, 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)
- img = Image.open(buffer)
- 
- return df, excel_path, img
- except Exception as e:
- return f"Error fetching data: {str(e)}", None, None, None
+    try:
+        sf = Salesforce(
+            username=SALESFORCE_USERNAME,
+            password=SALESFORCE_PASSWORD,
+            security_token=SALESFORCE_SECURITY_TOKEN
+        )
+        
+        if data_type == "Inventory":
+            query = "SELECT Productname__c, Model__c, H_p__c, Stage__c, Current_Stocks__c, soldstock__c, Price__c, Last_Modified_Date__c FROM Inventory_Management__c LIMIT 100"
+        else:
+            query = "SELECT Productname__c, Model__c, H_p__c, Stage__c, Current_Stock__c, soldstock__c, Price__c, Last_Modified_Date__c FROM Un_Billable__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={
+            "Productname__c": "Product Name",
+            "Model__c": "Model",
+            "H_p__c": "H.P",
+            "Stage__c": "Stage",
+            "Current_Stocks__c": "Current Stocks",
+            "Current_Stock__c": "Current Stocks",
+            "soldstock__c": "Sold Stock",
+            "Price__c": "Price",
+            "Last_Modified_Date__c": "Last Modified Date"
+        }, 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)
+        img = Image.open(buffer)
+        
+        return df, excel_path, img
+    except Exception as e:
+        return f"Error fetching data: {str(e)}", None, None, None
 
 # Gradio Interface
 def app():
- with gr.Blocks() as demo:
- with gr.Tab("📥 OCR Processing"):
- with gr.Row():
- image_input = gr.Image(type="numpy", label="📄 Upload Image")
- mode_input = gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry")
- entry_type_input = gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales")
- quantity_input = gr.Number(label="🔢 Quantity", value=1, interactive=True)
- extract_button = gr.Button("Extract Text and Attributes")
- extracted_text_output = gr.Text(label="📝 Extracted Image Data")
- editable_df_output = gr.Dataframe(label="✏️ Edit Attributes (Key-Value Pairs)", interactive=True)
- ok_button = gr.Button("OK")
- result_output = gr.Text(label="🚀 Result")
-
- with gr.Tab("📊 Salesforce Data"):
- data_type_input = gr.Dropdown(label="Select Data Type", choices=["Inventory", "Unbilling"], value="Inventory")
- pull_button = gr.Button("Pull Data from Salesforce")
- salesforce_data_output = gr.Dataframe(label="📊 Salesforce Data")
- excel_download_output = gr.File(label="📥 Download Excel")
- graph_output = gr.Image(label="📈 Stock Distribution Graph")
-
- # Define button actions
- extract_button.click(
- fn=process_image,
- inputs=[image_input, mode_input, entry_type_input, quantity_input],
- outputs=[extracted_text_output, editable_df_output, result_output]
- )
- ok_button.click(
- fn=export_to_salesforce,
- inputs=[mode_input, entry_type_input, quantity_input, editable_df_output],
- outputs=[result_output]
- )
- pull_button.click(
- fn=pull_data_from_salesforce,
- inputs=[data_type_input],
- outputs=[salesforce_data_output, excel_download_output, graph_output]
- )
-
- return demo
+    with gr.Blocks() as demo:
+        with gr.Tab("📥 OCR Processing"):
+            with gr.Row():
+                image_input = gr.Image(type="numpy", label="📄 Upload Image")
+                mode_input = gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry")
+                entry_type_input = gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales")
+                quantity_input = gr.Number(label="🔢 Quantity", value=1, interactive=True)
+                extract_button = gr.Button("Extract Text and Attributes")
+                extracted_text_output = gr.Text(label="📝 Extracted Image Data")
+                editable_df_output = gr.Dataframe(label="✏️ Edit Attributes (Key-Value Pairs)", interactive=True)
+                ok_button = gr.Button("OK")
+                result_output = gr.Text(label="🚀 Result")
+
+        with gr.Tab("📊 Salesforce Data"):
+            data_type_input = gr.Dropdown(label="Select Data Type", choices=["Inventory", "Unbilling"], value="Inventory")
+            pull_button = gr.Button("Pull Data from Salesforce")
+            salesforce_data_output = gr.Dataframe(label="📊 Salesforce Data")
+            excel_download_output = gr.File(label="📥 Download Excel")
+            graph_output = gr.Image(label="📈 Stock Distribution Graph")
+
+            # Define button actions
+            extract_button.click(
+                fn=process_image,
+                inputs=[image_input, mode_input, entry_type_input, quantity_input],
+                outputs=[extracted_text_output, editable_df_output, result_output]
+            )
+            ok_button.click(
+                fn=export_to_salesforce,
+                inputs=[mode_input, entry_type_input, quantity_input, editable_df_output],
+                outputs=[result_output]
+            )
+            pull_button.click(
+                fn=pull_data_from_salesforce,
+                inputs=[data_type_input],
+                outputs=[salesforce_data_output, excel_download_output, graph_output]
+            )
+
+    return demo
 
 if __name__ == "__main__":
- app().launch(share=True)
+    app().launch(share=True)