Update app.py

app.py

@@ -76,6 +76,11 @@ ATTRIBUTE_MAPPING = {
     "H.P.": "H_p__c"
 }
 
+# List of product names to match
+PRODUCT_NAMES = [
+     "Fusion", "Agroking", "openwell", "CG commercial motors", "Jaguar", "Submersible pumps", "Gaurav"
+]
+
 # Salesforce credentials
 SALESFORCE_USERNAME = "venkatramana@sandbox.com"
 SALESFORCE_PASSWORD = "Venkat12345@"
@@ -92,6 +97,21 @@ def extract_text(image):
         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, threshold=70):
+    best_match = None
+    best_score = 0
+
+    print("Matching Process:")  # Debugging: Print matching process
+    for line in extracted_text.split("\n"):
+        match, score = process.extractOne(line, PRODUCT_NAMES)
+        print(f"Line: {line}, Match: {match}, Score: {score}")  # Debugging: Print each line and its match score
+        if score > best_score:
+            best_match = match
+            best_score = score
+
+    return best_match if best_score >= threshold else None  # Adjustable threshold for a match
+
 # Function to extract attributes and their values
 def extract_attributes(extracted_text):
     attributes = {}
@@ -254,15 +274,19 @@ def pull_data_from_salesforce(selected_object):
         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):
+def process_image(image, mode, entry_type, quantity, threshold=70):
     extracted_text = extract_text(image)
     if not extracted_text:
         return "No text detected in the image.", None, None
 
     print("Extracted Text:", extracted_text)  # Debugging: Print extracted text
 
+    product_name = match_product_name(extracted_text, threshold)
     attributes = extract_attributes(extracted_text)
-    product_name = attributes.get("Product name", "Unknown")
+    if product_name:
+        attributes["Product name"] = product_name
+    else:
+        attributes["Product name"] = "Unknown"
 
     # Combine extracted text and attributes for display
     combined_text = extracted_text + "\n\nExtracted Attributes:\n"
@@ -284,16 +308,17 @@ def app():
             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)
+            threshold_input = gr.Slider(label="🔍 Matching Threshold", minimum=0, maximum=100, value=70, step=1)
             extracted_text_output = gr.Text(label="📝 Extracted Image Data")
             attributes_output = gr.Dataframe(label="✏️ Edit Attributes", headers=["Attribute", "Value"], interactive=True)
             process_button = gr.Button("Process Image")
 
-            def process_image_and_display(image, mode, entry_type, quantity):
-                combined_text, attributes = process_image(image, mode, entry_type, quantity)
+            def process_image_and_display(image, mode, entry_type, quantity, threshold):
+                combined_text, attributes = process_image(image, mode, entry_type, quantity, threshold)
                 attributes_list = [[key, value] for key, value in attributes.items()]
                 return combined_text, attributes_list
 
-            process_button.click(process_image_and_display, inputs=[image_input, mode_input, entry_type_input, quantity_input], outputs=[extracted_text_output, attributes_output])
+            process_button.click(process_image_and_display, inputs=[image_input, mode_input, entry_type_input, quantity_input, threshold_input], outputs=[extracted_text_output, attributes_output])
 
             export_button = gr.Button("OK")
             result_output = gr.Text(label="🚀 Result")