Create app.py

app.py

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+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
+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"
+}
+
+# List of product names to match
+PRODUCT_NAMES = [
+    "Fusion", "Agroking", "CG commercial motors", "Jaguar", "Gaurav"
+]
+
+# Salesforce credentials
+SALESFORCE_USERNAME = "venkatramana@sandbox.com"
+SALESFORCE_PASSWORD = "Seta12345@"
+SALESFORCE_SECURITY_TOKEN = "Drl0jchCwLBfvX4ODMeFDksP"
+
+# Initialize PaddleOCR
+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)
+
+# 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
+
+# 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, 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"
+        price_field_name = "Price__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)}, {price_field_name} FROM {object_name} WHERE {' AND '.join(query_conditions)} LIMIT 1"
+            response = sf.query(query)
+
+            if response["records"]:
+                record = response["records"][0]
+                record_id = record["Id"]
+                updated_fields = {field: quantity for field in field_names}
+                sf_object.update(record_id, updated_fields)
+
+                price = record.get(price_field_name, "N/A")
+                return (
+                    f"✅ Updated record for product '{attributes['Product name']}' in {object_name}.\n"
+                    f"Updated fields: {updated_fields}.\n"
+                    f"Details: Product Name: {attributes['Product name']}, H.P.: {attributes['H.P.']}, "
+                    f"Stage: {attributes['Stage']}, Price: {price}."
+                )
+            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)}"
+
+# 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
+
+# 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
+
+if __name__ == "__main__":
+    app().launch(share=True)