Update app.py

app.py

@@ -82,10 +82,12 @@ def download_pdf_blob_as_text(storage_connection_string, container_name, blob_na
 
 def load_job_data(job, resume, job_params):
     if not job:
+        print("JOB_PDF: "+job_params["job"].split("cv")[0][:-1]+".pdf")
+        print("Resume: "+job_params["job"]+".pdf")
         try:
-            pdf_filename_jobdescription = job_params["job"]+".pdf"
-            pdf_filename_cv = job_params["job"]+"_resume.pdf"
-            json_filename = job_params["job"]+"_jsondata.json" 
+            pdf_filename_jobdescription = job_params["job"].split("cv")[0][:-1]+".pdf"
+            pdf_filename_cv = job_params["job"]+".pdf"
+            json_filename = job_params["job"].split("cv")[0][:-1]+"_jsondata.json" 
             storage_connection_string = os_connection_string
             container_name = "jobdescriptions"  # Der Name des Blob-Containers
             job = download_pdf_blob_as_text(storage_connection_string, container_name, pdf_filename_jobdescription)
@@ -207,7 +209,7 @@ def send_evaluation(history, job, resume, job_params):
     # E-Mail-Nachricht erstellen
     subject = "Evaluation for the job: "+job_params[0]
     message = f"""Dear Recruiter,
-    
+
 Please find attached the complete chat history for this evaluation, resume and summary.
 
 The evaluation AI-supported summarized:
@@ -312,4 +314,4 @@ with gr.Blocks(css=css) as app:
     clr.click(lambda: None, None, chat, queue=False)
 
 app.queue()
-app.launch()
+app.launch()