Update parse_bhel.py

parse_bhel.py

@@ -1,63 +1,58 @@
-import pdfplumber
+import re
 import pandas as pd
+import pdfplumber
 
-def preprocess_rows(rows, expected_columns):
-    aligned_rows = []
-    buffer = []
-    unalignable_rows = []  # Capture unaligned rows for inspection
-
-    for row in rows:
-        # Check if the row contains irrelevant metadata or headers
-        if any(keyword in row[0] for keyword in ["GSTIN", "Currency", "Payment Terms", "General Terms", "Delivery Schedule"]):
-            continue
-
-        # If row matches expected length, add directly
-        if len(row) == expected_columns:
-            if buffer:
-                aligned_rows.append(buffer)  # Add any buffered row first
-                buffer = []  # Reset buffer
-            aligned_rows.append(row)
-
-        # If row contains part of an entry (such as "Material Number" or "HSN Code")
-        elif "Material Number" in row[0] or "HSN Code" in row[0] or "IGST" in row[0]:
-            if buffer:
-                buffer[-1] += " " + row[0]  # Append to last column in buffer
+# Define the target columns based on your table headers
+columns = [
+    "Purchase Order No", "Date", "Sl No", "Material Description", 
+    "Unit", "Quantity", "Dely Qty", "Dely Date", "Unit Rate", "Value"
+]
+
+# Initialize an empty DataFrame with the defined columns
+data = pd.DataFrame(columns=columns)
+
+# Define regex patterns to identify and parse required lines
+po_pattern = re.compile(r'^\d{10} / \d{2}\.\d{2}\.\d{4}')  # Purchase Order pattern
+material_pattern = re.compile(r'^\d{1,3} ')  # Pattern for lines starting with Sl No
+
+# Function to clean and split rows
+def clean_and_split_line(line):
+    # Split line into components based on spaces and commas
+    parts = re.split(r'\s{2,}', line.strip())  # Split by two or more spaces
+    return parts if len(parts) == len(columns) else None
+
+# Process the PDF and extract relevant lines
+with pdfplumber.open('your_pdf_file.pdf') as pdf:
+    for page in pdf.pages:
+        text = page.extract_text().splitlines()
+        
+        for line in text:
+            # Check for Purchase Order row
+            if po_pattern.match(line):
+                po_data = line.split(' / ')
+                po_no = po_data[0]
+                po_date = po_data[1]
+                
+            # Check if the line contains material data
+            elif material_pattern.match(line):
+                cleaned_data = clean_and_split_line(line)
+                if cleaned_data:
+                    row_data = {
+                        "Purchase Order No": po_no,
+                        "Date": po_date,
+                        "Sl No": cleaned_data[0],
+                        "Material Description": cleaned_data[1],
+                        "Unit": cleaned_data[2],
+                        "Quantity": cleaned_data[3],
+                        "Dely Qty": cleaned_data[4],
+                        "Dely Date": cleaned_data[5],
+                        "Unit Rate": cleaned_data[6],
+                        "Value": cleaned_data[7],
+                    }
+                    data = data.append(row_data, ignore_index=True)
+            # Skip irrelevant lines or unalignable rows
             else:
-                buffer = row  # Initialize buffer with this part-row
-        else:
-            # If unalignable, add to unalignable_rows for debugging
-            unalignable_rows.append(row)
-
-    # Log any remaining buffered content
-    if buffer:
-        aligned_rows.append(buffer)
-
-    # Print unalignable rows for analysis
-    for row in unalignable_rows:
-        print(f"Unalignable row: {row}")
-
-    return aligned_rows
-
-def parse_bhel_pdf(pdf_path):
-    columns = [
-        "Purchase Order No", "Date", "Sl No", "Material Description", 
-        "Unit", "Quantity", "Dely Qty", "Dely Date", "Unit Rate", "Value"
-    ]
-    expected_columns = len(columns)
-    data = []
-
-    with pdfplumber.open(pdf_path) as pdf:
-        for page in pdf.pages:
-            table = page.extract_table()
-            if table:
-                # Preprocess and align rows before DataFrame conversion
-                rows = preprocess_rows(table[1:], expected_columns)
-                for row in rows:
-                    if len(row) == expected_columns:
-                        data.append(row)
-                    else:
-                        print(f"Skipping unalignable row: {row}")
+                continue
 
-    # Convert aligned rows into a DataFrame
-    df = pd.DataFrame(data, columns=columns)
-    return df
+# Save extracted data to an Excel file
+data.to_excel("extracted_data.xlsx", index=False)