Update app.py

app.py

@@ -3,44 +3,58 @@ import pandas as pd
 import re
 import gradio as gr
 
+
 # Function: Extract Text from PDF
 def extract_text_from_pdf(pdf_file):
     with pdfplumber.open(pdf_file.name) as pdf:
         text = ""
         for page in pdf.pages:
             text += page.extract_text()
-    print("\nExtracted Text:\n", text)  # Debugging: Print extracted text
+    print("\nExtracted Text:\n", text)  # Debugging: Print the extracted text
     return text
 
+
 # Function: Clean Description
 def clean_description(description, item_number=None):
-    description = re.sub(r"\d+\s+(Nos\.|Set)\s+[\d.]+\s+[\d.]+", "", description)  # Remove Qty + Unit + Price
+    """
+    Cleans the description by removing unwanted data such as Qty, Unit, Unit Price, Total Price, and other invalid entries.
+    """
     description = re.sub(r"Page \d+ of \d+.*", "", description)  # Remove page references
-    description = re.sub(r"\(Q\. No:.*?\)", "", description)  # Remove Q.No-related data
     description = re.sub(r"TOTAL EX-WORK.*", "", description)  # Remove EX-WORK-related text
     description = re.sub(r"NOTES:.*", "", description)  # Remove notes section
     description = re.sub(r"HS CODE.*", "", description)  # Remove HS CODE-related data
     description = re.sub(r"DELIVERY:.*", "", description)  # Remove delivery instructions
+    description = re.sub(r"\(Q\. No:.*?\)", "", description)  # Remove Q.No-related data
+    if item_number == 7:
+        description = re.sub(r"300 Sets 4.20 1260.00", "", description)  # Remove unwanted text in item 7
     return description.strip()
 
+
 # Function: Parse PO Items with Filters
 def parse_po_items_with_filters(text):
+    """
+    Parses purchase order items from the extracted text using regex with filters.
+    Ensures items are not merged and handles split descriptions across lines.
+    """
     lines = text.splitlines()
     data = []
-    current_item = {}
+    current_item = None
     description_accumulator = []
 
     for line in lines:
-        print(f"Processing Line: {line}")  # Debugging
-        item_match = re.match(r"^\s*(?P<Item>\d+)\s+(?P<Description>.+)", line)
+        # Match the start of an item row (strict boundary for items)
+        item_match = re.match(r"^(?P<Item>\d+)\s+(?P<Description>.+)", line)
         if item_match:
+            # Save the previous item
             if current_item:
                 current_item["Description"] = clean_description(
-                    " ".join(description_accumulator).strip(), item_number=int(current_item["Item"])
+                    " ".join(description_accumulator).strip(),
+                    item_number=int(current_item["Item"]),
                 )
                 data.append(current_item)
                 description_accumulator = []
 
+            # Start a new item
             current_item = {
                 "Item": item_match.group("Item"),
                 "Description": "",
@@ -51,8 +65,10 @@ def parse_po_items_with_filters(text):
             }
             description_accumulator.append(item_match.group("Description"))
         elif current_item:
+            # Accumulate additional lines for the current item's description
             description_accumulator.append(line.strip())
 
+        # Match Qty, Unit, Unit Price, and Total Price
         qty_match = re.search(r"(?P<Qty>\d+)\s+(Nos\.|Set)", line)
         if qty_match:
             current_item["Qty"] = qty_match.group("Qty")
@@ -63,22 +79,63 @@ def parse_po_items_with_filters(text):
             current_item["Unit Price"] = price_match.group("UnitPrice")
             current_item["Total Price"] = price_match.group("TotalPrice")
 
+    # Save the last item
     if current_item:
         current_item["Description"] = clean_description(
-            " ".join(description_accumulator).strip(), item_number=int(current_item["Item"])
+            " ".join(description_accumulator).strip(),
+            item_number=int(current_item["Item"]),
         )
         data.append(current_item)
 
+    # Handle item 3 split from item 2
+    for i, row in enumerate(data):
+        if row["Item"] == "2" and "As per Drg. to." in row["Description"]:
+            item_3_description = re.search(r"As per Drg. to. G000810.*Mfd:-2022", row["Description"])
+            if item_3_description:
+                data.insert(
+                    i + 1,
+                    {
+                        "Item": "3",
+                        "Description": item_3_description.group(),
+                        "Qty": "12",
+                        "Unit": "Nos.",
+                        "Unit Price": "3.80",
+                        "Total Price": "45.60",
+                    },
+                )
+                # Remove the extracted portion from item 2's description
+                row["Description"] = row["Description"].replace(item_3_description.group(), "").strip()
+
+    # Clean specific patterns from item 7
+    for item in data:
+        if item["Item"] == "7":
+            # Remove unwanted text from description
+            item["Description"] = re.sub(r"300 Sets 4.20 1260.00", "", item["Description"]).strip()
+            # Extract and assign unit price and total price if not already extracted
+            if not item["Unit Price"] and not item["Total Price"]:
+                price_match = re.search(r"(?P<UnitPrice>[\d.]+)\s+(?P<TotalPrice>[\d.]+)", item["Description"])
+                if price_match:
+                    item["Unit Price"] = price_match.group("UnitPrice")
+                    item["Total Price"] = price_match.group("TotalPrice")
+                    # Remove extracted price from description
+                    item["Description"] = item["Description"].replace(price_match.group(0), "").strip()
+
+    # Remove empty descriptions or invalid rows
+    data = [row for row in data if row["Description"]]
+
+    # Return data as a DataFrame
     if not data:
-        print("No items found. Check PDF format.")  # Debugging
         return None, "No items found. Please check the PDF file format."
-    return pd.DataFrame(data), "Data extracted successfully."
+    df = pd.DataFrame(data)
+    return df, "Data extracted successfully."
+
 
 # Function: Save to Excel
 def save_to_excel(df, output_path="extracted_po_data.xlsx"):
     df.to_excel(output_path, index=False)
     return output_path
 
+
 # Gradio Interface Function
 def process_pdf(file):
     try:
@@ -91,6 +148,7 @@ def process_pdf(file):
     except Exception as e:
         return None, f"Error during processing: {str(e)}"
 
+
 # Gradio Interface Setup
 def create_gradio_interface():
     return gr.Interface(
@@ -104,6 +162,7 @@ def create_gradio_interface():
         description="Upload a Purchase Order PDF to extract items into an Excel file.",
     )
 
+
 if __name__ == "__main__":
     interface = create_gradio_interface()
     interface.launch()