Update app.py

app.py

@@ -15,43 +15,44 @@ import kaleido  # Ensure kaleido is imported
 ATTRIBUTE_MAPPING = {
     "Product name": "Productname__c",
     "Type": "Type__c",
-    "model name" :"Model Name",
-    "Voltage":"Voltage__c",
+    "model name": "Model Name",
+    "Voltage": "Voltage__c",
     "Outlet": "Outlet__c",
-    "Stage":"Stage__c",
+    "Stage": "Stage__c",
     "H.p": "H_p__c",
     "Model": "Model__c"
 }
 
 # List of product names to match
-PRODUCT_NAMES = {
+PRODUCT_NAMES = [
     "CG COMMERCIAL MOTORS", "Fusion", "Agroking", "Openwell"
-    }
+]
 
 # List of model names to match
-MODEL_NAMES = {
+MODEL_NAMES = [
     "V9", "V4", "V3", "V7", "V6"
-}
-    
-
-# 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)
+def extract_text_from_image(image_array):
+    """
+    Extracts text from an image using PaddleOCR.
+    Accepts an image as a numpy array (cv2 image or similar).
+    """
+    result = ocr.ocr(image_array)
     extracted_text = []
+    
     for line in result[0]:
-        extracted_text.append(line[1][0])
+        extracted_text.append(line[1][0])  # Extract detected text
+        
     return "\n".join(extracted_text)
 
-# Function to match product name using fuzzy matching
 def match_product_name(extracted_text):
+    """
+    Uses fuzzy matching to find the best product name match from extracted text.
+    """
     best_match = None
     best_score = 0
 
@@ -63,7 +64,6 @@ def match_product_name(extracted_text):
 
     return best_match if best_score >= 70 else None  # Threshold of 70 for a match
 
-# Function to match model name using fuzzy matching
 def match_model_name(extracted_text):
     """
     Uses fuzzy matching to find the best model name match from extracted text.
@@ -79,6 +79,14 @@ def match_model_name(extracted_text):
 
     return best_match if best_score >= 70 else None  # Threshold of 70 for a match
 
+# Example Usage: Load an image file into a numpy array (cv2 format)
+def process_image(image_path):
+    image = cv2.imread(image_path)  # Read the image
+    extracted_text = extract_text_from_image(image)  # Extract text
+
+    # Matching extracted text with product and model names
+    matched_product = match_product_name(extracted_text)
+    matched_model = match_model_name(extracted_text)
 
 # Function to extract attributes and their values
 def extract_attributes(extracted_text):