Update app.py

app.py

@@ -1,3 +1,6 @@
+HUGGING FACE ALL FUNCTIONALITIES WORKING 
+
+
 import os
 from paddleocr import PaddleOCR
 from PIL import Image
@@ -145,81 +148,11 @@ def extract_attributes(extracted_text):
 
     return attributes
 
-# Function to filter attributes for valid Salesforce fields📊
+# 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
-
-
-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  # Ensure kaleido is imported
-
-# Salesforce credentials
-SALESFORCE_USERNAME = "venkatramana@sandbox.com"
-SALESFORCE_PASSWORD = "Venkat12345@"
-SALESFORCE_SECURITY_TOKEN = "GhcJJmjBEefdnukJoz4CAQlR"
-
-# 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
-    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"
-    ]
-    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 interact with Salesforce based on mode and type
+#📊  Function to interact with Salesforce based on mode and type
 def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
     try:
         sf = Salesforce(
@@ -232,6 +165,7 @@ def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
         object_name = None
         field_name = None
         product_field_name = "Product_Name__c"
+        model_field_name = "Modal_Name__c"  # Correct field for model name
 
         if mode == "Entry":
             if entry_type == "Sales":
@@ -240,16 +174,6 @@ def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
             elif entry_type == "Non-Sales":
                 object_name = "UNBILLING_DATA__c"
                 field_name = "TotalQuantity__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."
-
-            # Creating a new record
-            sf.__getattr__(object_name).create({product_field_name: product_name, field_name: quantity})
-            return f"✅ Record created in {object_name} with Product: '{product_name}', Quantity: {quantity}."
-
         elif mode == "Exit":
             if entry_type == "Sales":
                 object_name = "Inventory_Management__c"
@@ -258,70 +182,97 @@ def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
                 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."
+        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"]}
 
-            query = f"SELECT Id, {field_name} FROM {object_name} WHERE {product_field_name} = '{product_name}' LIMIT 1"
+        # Extract product name and attributes
+        product_name = match_product_name(extracted_text)
+        attributes = extract_attributes(extracted_text)
+        model_name = attributes.get("Model Name", "").strip()
+
+        if not product_name:
+            return "Product name could not be matched from the extracted text."
+
+        attributes["Product name"] = product_name
+
+        # Handling "Exit" Mode (Updating Records)
+        if mode == "Exit":
+            # Query should only match exact product name or exact model name
+            query_conditions = []
+            if model_name:
+                query_conditions.append(f"{model_field_name} = '{model_name}'")
+            query_conditions.append(f"{product_field_name} = '{product_name}'")
+
+            query = f"SELECT Id, {field_name} FROM {object_name} WHERE {' OR '.join(query_conditions)} 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}."
+                updated_quantity = quantity  # Overwrite the quantity
+                sf_object.update(record_id, {field_name: updated_quantity})
+                return f"✅ Updated record for product '{product_name}' ({model_name}) in {object_name}. New {field_name}: {updated_quantity}."
             else:
-                return f"❌ No matching record found for product '{product_name}' in {object_name}."
+                return f"❌ No matching record found for product '{product_name}' ({model_name}) in {object_name}."
+
+        # Handling "Entry" Mode (Creating Records)
+        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 structured data from Salesforce and display as a table
-    def pull_data_from_salesforce():
-        try:
-            sf = Salesforce(
+# Function to pull structured data from Salesforce and display as a table
+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"
-            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')
+       
+        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)
-            img = Image.open(buffer)
-            
-            return "Data successfully retrieved.", df, excel_path, img
-        except Exception as e:
-            return f"Error fetching data: {str(e)}", None, None, None
+        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)
+        img = Image.open(buffer)
+       
+        return "Data successfully retrieved.", df, excel_path, img
+    except Exception as e:
+        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):