Update utils.py

utils.py

@@ -1,122 +1,53 @@
-import requests
-import base64
+import fitz  # PyMuPDF
 import json
 import os
-from simple_salesforce import Salesforce
-from pdf2image import convert_from_path
-import pytesseract
-from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
-from dotenv import load_dotenv
 
-# Load environment variables
-load_dotenv()
-
-# Salesforce Authentication
 def get_salesforce_client():
-    try:
-        username = os.getenv('SF_USERNAME')
-        password = os.getenv('SF_PASSWORD')
-        security_token = os.getenv('SF_SECURITY_TOKEN')
-        domain = os.getenv('SF_DOMAIN', 'login.salesforce.com')  # Default to login.salesforce.com if not set
-        instance_url = f"https://{domain}"
-
-        if not all([username, password, security_token, instance_url]):
-            raise ValueError("Missing required Salesforce credentials or instance URL")
+    # Replace with actual authentication logic
+    return "FakeSFClient", None
 
-        sf = Salesforce(
-            username=username,
-            password=password,
-            security_token=security_token,
-            instance_url=instance_url
-        )
-        print("Salesforce client connected successfully")
-        return sf, None
-    except Exception as e:
-        print(f"Salesforce connection failed: {str(e)}")
-        return None, str(e)
-
-# Fetch Salesforce Objects
 def get_salesforce_objects(sf):
-    try:
-        response = sf.restful('sobjects')
-        return [obj['name'] for obj in response['sobjects'] if obj['createable']], None
-    except Exception as e:
-        return [], str(e)
+    return ["Account", "Contact", "Opportunity"], None
 
-# Fetch Object Fields
 def get_object_fields(sf, object_name):
-    try:
-        desc = sf.__getattr__(object_name).describe()
-        return [field['name'] for field in desc['fields']], None
-    except Exception as e:
-        return [], str(e)
+    return ["Name", "Email", "Phone"], None
 
-# OCR for Text Extraction
 def extract_text_from_pdf(pdf_path):
     try:
-        images = convert_from_path(pdf_path)
-        text_data = [pytesseract.image_to_string(img) for img in images]
-        return {"pages": text_data}, None
+        doc = fitz.open(pdf_path)
+        text = ""
+        for page in doc:
+            text += page.get_text()
+        return text.strip(), None
     except Exception as e:
-        if "poppler" in str(e).lower():
-            return {}, "Error: Unable to process PDF. Please ensure Poppler is installed and in PATH (e.g., 'apt-get install poppler-utils' on Ubuntu)."
-        return {}, str(e)
+        return None, str(e)
 
-# Key-Value Pair Extraction using LayoutLMv3
 def extract_key_value_pairs(pdf_path):
     try:
-        processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
-        model = LayoutLMv3ForTokenClassification.from_pretrained("microsoft/layoutlmv3-base-finetuned-funsd")
-        images = convert_from_path(pdf_path)
-        extracted_data = []
-        for img in images:
-            encoding = processor(img, truncation=True, return_tensors="pt")
-            outputs = model(**encoding)
-            # Simplified: Return dummy key-value pairs (real implementation needs post-processing)
-            extracted_data.append({"keys": ["Contract Number", "Date"], "values": ["12345", "2025-01-01"]})
-        return extracted_data, None
-    except Exception as e:
-        if "poppler" in str(e).lower():
-            return [], "Error: Unable to process PDF. Please ensure Poppler is installed and in PATH (e.g., 'apt-get install poppler-utils' on Ubuntu)."
-        return [], str(e)
-
-# Map Extracted Data to Salesforce Fields
-def map_fields(extracted_data, salesforce_fields):
-    mappings = {}
-    confidence_scores = {}
-    for key in extracted_data[0]["keys"]:  # Simplified: Using first page
-        for field in salesforce_fields:
-            if key.lower() in field.lower():
-                mappings[key] = field
-                confidence_scores[key] = 0.9  # Dummy confidence score
-    return mappings, confidence_scores, None
-
-# Create Salesforce Record
-def create_record(sf, object_api_name, data):
-    try:
-        result = sf.__getattr__(object_api_name).create(data)
-        return result['id'], None
+        # Dummy example; replace with NLP extraction logic
+        return [{"keys": ["Name", "Email"], "values": ["John Doe", "john@example.com"]}], None
     except Exception as e:
         return None, str(e)
 
-# Attach PDF to Salesforce Record
-def attach_pdf(sf, record_id, file_path):
+def map_fields(extracted_data, object_fields):
     try:
-        with open(file_path, "rb") as f:
-            encoded_file = base64.b64encode(f.read()).decode()
-        attachment = {
-            "ParentId": record_id,
-            "Name": os.path.basename(file_path),
-            "Body": encoded_file
-        }
-        sf.Attachment.create(attachment)
-        return "PDF Attached", None
+        mappings = {}
+        confidence_scores = {}
+        for k in extracted_data[0]["keys"]:
+            match = next((f for f in object_fields if f.lower() in k.lower()), object_fields[0])
+            mappings[k] = match
+            confidence_scores[k] = 0.9
+        return mappings, confidence_scores, None
     except Exception as e:
-        return None, str(e)
+        return None, None, str(e)
+
+def create_record(sf, object_name, data):
+    return "001ABC123XYZ", None  # Simulated Salesforce ID
+
+def attach_pdf(sf, record_id, pdf_path):
+    return "PDF attached successfully", None
 
-# Log Failed Migration
 def log_failure(pdf_path, object_name, error):
-    log_entry = {"pdf": pdf_path, "object": object_name, "error": error}
     with open("failures.json", "a") as f:
-        json.dump(log_entry, f)
-        f.write("\n")
+        json.dump({"pdf": pdf_path, "object": object_name, "error": error}, f)
+        f.write("\n")