fixed address extraction

etl/customers/stonewater/Wave 3 Preparation.py

@@ -1,6 +1,7 @@
 import os
 import PyPDF2
 import re
+import pandas as pd
 
 FILE_PATH = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater/Wave 3 Surveys"
 
@@ -11,12 +12,12 @@ def extract_summary_report(pdf_path):
     Data includes:
     - Current SAP rating
     - Fuel Bill
-    - Emissions (t/year)
+    - Address
     """
     data = {
-        "Current SAP rating": None,
+        "Address": None,
+        "Current SAP Rating": None,
         "Fuel Bill": None,
-        "Emissions (t/year)": None,
     }
 
     with open(pdf_path, "rb") as file:
@@ -28,17 +29,36 @@ def extract_summary_report(pdf_path):
         # Extract Current SAP rating
         sap_match = re.search(r"Current SAP rating:\s*([A-Z] \d+)", text)
         if sap_match:
-            data["Current SAP rating"] = sap_match.group(1)
+            data["Current SAP Rating"] = sap_match.group(1)
 
         # Extract Fuel Bill
         fuel_bill_match = re.search(r"Fuel Bill:\s*£(\d+)", text)
         if fuel_bill_match:
             data["Fuel Bill"] = f"£{fuel_bill_match.group(1)}"
 
-        # Extract Emissions
-        emissions_match = re.search(r"Emissions \(t/year\):\s*([\d.]+) tonnes", text)
-        if emissions_match:
-            data["Emissions (t/year)"] = f"{emissions_match.group(1)} tonnes"
+        # Extract individual address components
+        postcode = re.search(r"Postcode:\s*(.*?)\nRegion:", text)
+        # region = re.search(r"Region:\s*(.*?)\nHouse Name:", text)
+        house_name = re.search(r"House Name:\s*(.*?)\nHouse No:", text)
+        house_no = re.search(r"House No:\s*(.*?)\nStreet:", text)
+        street = re.search(r"Street:\s*(.*?)\nLocality:", text)
+        locality = re.search(r"Locality:\s*(.*?)\nTown:", text)
+        town = re.search(r"Town:\s*(.*?)\nCounty:", text)
+        county = re.search(r"County:\s*(.*?)\nProperty Tenure:", text)
+
+        # Clean extracted values and remove any prefixes
+        address_parts = [
+            house_no.group(1).strip() if house_no else "",
+            house_name.group(1).strip() if house_name else "",
+            street.group(1).strip() if street else "",
+            locality.group(1).strip() if locality else "",
+            town.group(1).strip() if town else "",
+            county.group(1).strip() if county else "",
+            postcode.group(1).strip() if postcode else ""
+        ]
+
+        # Join non-empty parts with a comma
+        data["Address"] = ", ".join([part for part in address_parts if part])
 
     return data
 
@@ -49,8 +69,7 @@ def extract_epr(pdf_path):
     """
     data = {
         "Address": None,
-        "Estimated Annual Costs": None,
-        "Current SAP": None,
+        "Current SAP Rating": None,
         "Space Heating": None,
         "Water Heating": None,
         "Fuel Bill": None,
@@ -82,8 +101,8 @@ def extract_epr(pdf_path):
         current_sap, potential_sap = int(sap_match.group(1)), int(sap_match.group(2))
         # Ensure potential is greater than or equal to current
         if 1 <= current_sap <= 99 and 1 <= potential_sap <= 99 and potential_sap >= current_sap:
-            data["Current SAP"] = current_sap
-            data["Potential SAP"] = potential_sap
+            data["Current SAP Rating"] = current_sap
+            data["Potential SAP Rating"] = potential_sap
         else:
             raise ValueError("Failed to parse SAP ratings correctly due to unexpected format.")
 
@@ -117,6 +136,8 @@ def extract_retrofit_assessment_folder(retrofit_folder_path):
         pdf_path = os.path.join(retrofit_folder_path, summary_report)
         return extract_summary_report(pdf_path)
 
+    raise Exception("Not Implemented")
+
     return None  # If no relevant PDF is found
 
 
@@ -193,7 +214,7 @@ def main():
             }
             extracted_data.append(summary_data)
 
-    print("Extracted Data:", extracted_data)
+    extracted_data = pd.DataFrame(extracted_data)
 
 
 if __name__ == "__main__":

etl/customers/stonewater/requirements/requirements-wave-3-prep.txt

@@ -1 +1,2 @@
 PyPDF2
+pandas