Update app.py

app.py

@@ -15,7 +15,7 @@ SF_PASSWORD = os.getenv("SF_PASSWORD")
 SF_SECURITY_TOKEN = os.getenv("SF_SECURITY_TOKEN")
 SF_LOGIN_URL = os.getenv("SF_LOGIN_URL", "https://login.salesforce.com")
 SF_OBJECT_NAME = os.getenv("SF_OBJECT_NAME", "Agent_Prospect__c")
-SF_SCORE_FIELD = os.getenv("SF_SCORE_FIELD", "Suitability_Score__c")
+SF_SCORE_FIELD = os.getenv("SF_SCORE_FIELD", "Suitability_Srode__c")
 SF_LINK_FIELD = os.getenv("SF_RESUME_FIELD_LINK", "Resume_File_Link__c")
 SF_RECORD_ID = os.getenv("SF_RECORD_ID")
 
@@ -29,12 +29,17 @@ if missing:
 domain = "login" if "login" in SF_LOGIN_URL else "test"
 
 # Connect to Salesforce
-sf = Salesforce(
-    username=SF_USERNAME,
-    password=SF_PASSWORD,
-    security_token=SF_SECURITY_TOKEN,
-    domain=domain
-)
+try:
+    sf = Salesforce(
+        username=SF_USERNAME,
+        password=SF_PASSWORD,
+        security_token=SF_SECURITY_TOKEN,
+        domain=domain,
+        version="59.0"
+    )
+    print("Salesforce connection successful.")
+except Exception as e:
+    raise ValueError(f"Failed to connect to Salesforce: {str(e)}")
 
 # Load Hugging Face model
 classifier = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
@@ -42,10 +47,12 @@ classifier = pipeline("text-classification", model="nlptown/bert-base-multilingu
 def process_resume(file):
     try:
         record_id = SF_RECORD_ID
+        print(f"Processing resume for record ID: {record_id}")
 
         # Extract text from PDF
         with pdfplumber.open(file.name) as pdf:
             extracted_text = "\n".join([page.extract_text() or "" for page in pdf.pages])
+        print(f"Extracted text length: {len(extracted_text)} characters")
 
         if not extracted_text.strip():
             return "❌ No extractable text found in the PDF."
@@ -55,10 +62,12 @@ def process_resume(file):
         label = result[0]['label']
         score = round(float(result[0]['score']) * 100, 2)
         summary = f"Predicted Label: {label}\nSuitability Score: {score:.2f}"
+        print(f"Classifier result: {summary}")
 
         # Encode PDF in base64
         with open(file.name, "rb") as f:
             encoded_pdf = base64.b64encode(f.read()).decode("utf-8")
+        print(f"Encoded PDF size: {len(encoded_pdf)}")
 
         # Upload file as ContentVersion
         content_result = sf.ContentVersion.create({
@@ -66,30 +75,42 @@ def process_resume(file):
             "PathOnClient": file.name,
             "VersionData": encoded_pdf
         })
+        version_id = content_result.get("id")
+        print(f"ContentVersion created: {version_id}")
 
         # Get ContentDocumentId from ContentVersion
-        version_id = content_result.get("id")
         query_result = sf.query(
             f"SELECT ContentDocumentId FROM ContentVersion WHERE Id = '{version_id}'"
         )
+        print(f"Query result: {query_result}")
+        if not query_result["records"]:
+            return "❌ Failed to retrieve ContentDocumentId."
         content_doc_id = query_result["records"][0]["ContentDocumentId"]
+        print(f"ContentDocumentId: {content_doc_id}")
 
         # Link file to Salesforce record
-        sf.ContentDocumentLink.create({
-            "ContentDocumentId": content_doc_id,
-            "LinkedEntityId": record_id,
-            "ShareType": "V",
-            "Visibility": "AllUsers"
-        })
+        try:
+            sf.ContentDocumentLink.create({
+                "ContentDocumentId": content_doc_id,
+                "LinkedEntityId": record_id,
+                "ShareType": "V",
+                "Visibility": "AllUsers"
+            })
+            print("ContentDocumentLink created successfully.")
+        except Exception as e:
+            print(f"ContentDocumentLink Error: {str(e)}")
+            raise
 
         # Create download link
         download_link = f"https://{sf.sf_instance}/sfc/servlet.shepherd/document/download/{content_doc_id}"
+        print(f"Download link: {download_link}")
 
         # Update record with score and link
         sf.__getattr__(SF_OBJECT_NAME).update(record_id, {
             SF_SCORE_FIELD: score,
             SF_LINK_FIELD: download_link
         })
+        print("Salesforce record updated successfully.")
 
         return f"{summary}\n\n✅ Score and resume uploaded to Salesforce.\n📎 [Download Resume]({download_link})"
 
@@ -103,4 +124,4 @@ gr.Interface(
     outputs="text",
     title="LIC Resume AI Scorer",
     description="Upload a resume PDF. It will be scored and stored in Salesforce automatically."
-).launch(share=False)
+).launch(share=False)