Update app.py: adjust file handling and UI for resume optimization

app.py

@@ -38,122 +38,113 @@ def convert_md_to_pdf(md_path: str) -> str:
 def process_resume(openai_api_key, serper_api_key, model_choice, new_resume, company_name, job_url):
     """
     Processes the uploaded resume using ResumeCrew and converts the output Markdown files to PDFs.
-    
-    Parameters:
-        openai_api_key (str): OpenAI API key.
-        serper_api_key (str): Serper API key.
-        model_choice (str): The selected model.
-        new_resume: The uploaded resume file (file-like object or file path).
-        company_name (str): The company name.
-        job_url (str): The job posting URL.
-        
-    Returns:
-        A 7-tuple:
-          1. Status message (Textbox)
-          2. Optimized Resume PDF viewer
-          3. Optimized Resume PDF download link
-          4. Final Report PDF viewer
-          5. Final Report PDF download link
-          6. Interview Questions PDF viewer
-          7. Interview Questions PDF download link
+    Handles errors gracefully and stops execution upon failure.
     """
-    current_date = datetime.datetime.now().strftime("%Y%m%d")
-    
-    # --- Ensure a resume file is uploaded ---
-    if new_resume is None or not (hasattr(new_resume, "name") and new_resume.name.strip() != ""):
-        return ("Error: Please upload a resume.", None, None, None, None, None, None)
-    
-    # -- [MINIMAL CHANGE] -- Set environment variables so CrewAI sees these keys
-    os.environ["OPENAI_API_KEY"] = openai_api_key or ""
-    os.environ["SERPER_API_KEY"] = serper_api_key or ""
-
-    # --- Save the uploaded file into "knowledge" directory ---
-    if hasattr(new_resume, "read"):
-        original_filename = os.path.basename(new_resume.name)
-        file_data = new_resume.read()
-    else:
-        original_filename = os.path.basename(new_resume)
-        file_data = None
-
-    base_filename, ext = os.path.splitext(original_filename)
-    new_resume_filename = f"{base_filename}_{current_date}{ext}"  # e.g. CV_Yashwardhan_20250207.pdf
-    physical_path = os.path.join("knowledge", new_resume_filename)
-
-    os.makedirs("knowledge", exist_ok=True)
-
-    if file_data is not None:
-        with open(physical_path, "wb") as f:
-            f.write(file_data)
-    else:
-        shutil.copy(new_resume, physical_path)
-
-    # Pass ONLY the filename to ResumeCrew so it won't prepend "knowledge/" again:
-    crew_instance = ResumeCrew(
-        model=model_choice,
-        openai_api_key=openai_api_key,
-        serper_api_key=serper_api_key,
-        resume_pdf_path=new_resume_filename  # <-- Only filename
-    )
-
-    # Run the resume processing (kick off the crew)
-    crew_instance.crew().kickoff(inputs={'job_url': job_url, 'company_name': company_name})
-
-    # --- Retrieve output files ---
-    job_analysis_path = os.path.join("output", "job_analysis.json")
     try:
-        with open(job_analysis_path, "r") as f:
-            job_data = json.load(f)
-        position_name = job_data.get("job_title", "position")
-    except Exception:
-        position_name = "position"
-    
-    optimized_resume_path = os.path.join("output", "optimized_resume.md")
-    candidate_name = "candidate"
-    try:
-        with open(optimized_resume_path, "r") as f:
-            first_line = f.readline()
-            if first_line.startswith("#"):
-                candidate_name = first_line.lstrip("#").strip().replace(" ", "_")
-    except Exception:
+        current_date = datetime.datetime.now().strftime("%Y%m%d")
+        
+        # --- Ensure a resume file is uploaded ---
+        if new_resume is None or not (hasattr(new_resume, "name") and new_resume.name.strip() != ""):
+            return ("Error: Please upload a resume.", None, None, None, None, None, None)
+        
+        # --- Set API keys ---
+        os.environ["OPENAI_API_KEY"] = openai_api_key or ""
+        os.environ["SERPER_API_KEY"] = serper_api_key or ""
+
+        # --- Save uploaded file ---
+        try:
+            if hasattr(new_resume, "read"):
+                original_filename = os.path.basename(new_resume.name)
+                file_data = new_resume.read()
+            else:
+                original_filename = os.path.basename(new_resume)
+                file_data = None
+
+            base_filename, ext = os.path.splitext(original_filename)
+            new_resume_filename = f"{base_filename}_{current_date}{ext}"
+            physical_path = os.path.join("knowledge", new_resume_filename)
+            os.makedirs("knowledge", exist_ok=True)
+
+            if file_data is not None:
+                with open(physical_path, "wb") as f:
+                    f.write(file_data)
+            else:
+                shutil.copy(new_resume, physical_path)
+        except Exception as e:
+            return (f"Error saving the uploaded resume: {str(e)}", None, None, None, None, None, None)
+
+        # --- Initialize ResumeCrew ---
+        try:
+            crew_instance = ResumeCrew(
+                model=model_choice,
+                openai_api_key=openai_api_key,
+                serper_api_key=serper_api_key,
+                resume_pdf_path=new_resume_filename
+            )
+        except Exception as e:
+            return (f"Error initializing ResumeCrew: {str(e)}", None, None, None, None, None, None)
+
+        # --- Run the resume processing ---
+        try:
+            crew_instance.crew().kickoff(inputs={'job_url': job_url, 'company_name': company_name})
+        except Exception as e:
+            return (f"Error during resume processing: {str(e)}", None, None, None, None, None, None)
+
+        # --- Retrieve output files ---
+        try:
+            job_analysis_path = os.path.join("output", "job_analysis.json")
+            with open(job_analysis_path, "r") as f:
+                job_data = json.load(f)
+            position_name = job_data.get("job_title", "position")
+        except Exception:
+            position_name = "position"
+
+        optimized_resume_path = os.path.join("output", "optimized_resume.md")
         candidate_name = "candidate"
-    
-    # --- Create the output folder ---
-    folder_name = f"{company_name}_{position_name}_{candidate_name}_{current_date}"
-    new_output_dir = os.path.join("output", folder_name)
-    os.makedirs(new_output_dir, exist_ok=True)
-    
-    # --- Move generated output files ---
-    for filename in os.listdir("output"):
-        file_path = os.path.join("output", filename)
-        if file_path == new_output_dir:
-            continue
-        if filename.endswith(".json") or filename.endswith(".md"):
-            if os.path.isfile(file_path):
-                shutil.move(file_path, os.path.join(new_output_dir, filename))
-    
-    # --- Convert each Markdown file in the new output directory to PDF ---
-    def md_to_pdf_in_dir(md_filename):
-        md_path = os.path.join(new_output_dir, md_filename)
-        if os.path.isfile(md_path):
-            return convert_md_to_pdf(md_path)
-        return ""
-    
-    pdf_opt = md_to_pdf_in_dir("optimized_resume.md")
-    pdf_final = md_to_pdf_in_dir("final_report.md")
-    pdf_int = md_to_pdf_in_dir("interview_questions.md")
-    
-    message = f"Processing completed using model {model_choice}. Output saved in: {new_output_dir}"
-    
-    # RETURN EXACTLY 7 outputs to match the UI
-    return (
-        message,      # 1) Status message
-        pdf_opt,      # 2) PDF viewer for optimized resume
-        pdf_opt,      # 3) File for downloading optimized resume
-        pdf_final,    # 4) PDF viewer for final report
-        pdf_final,    # 5) File for downloading final report
-        pdf_int,      # 6) PDF viewer for interview questions
-        pdf_int       # 7) File for downloading interview questions
-    )
+        try:
+            with open(optimized_resume_path, "r") as f:
+                first_line = f.readline()
+                if first_line.startswith("#"):
+                    candidate_name = first_line.lstrip("#").strip().replace(" ", "_")
+        except Exception:
+            candidate_name = "candidate"
+
+        # --- Create the output folder ---
+        try:
+            folder_name = f"{company_name}_{position_name}_{candidate_name}_{current_date}"
+            new_output_dir = os.path.join("output", folder_name)
+            os.makedirs(new_output_dir, exist_ok=True)
+
+            for filename in os.listdir("output"):
+                file_path = os.path.join("output", filename)
+                if file_path == new_output_dir:
+                    continue
+                if filename.endswith(".json") or filename.endswith(".md"):
+                    if os.path.isfile(file_path):
+                        shutil.move(file_path, os.path.join(new_output_dir, filename))
+        except Exception as e:
+            return (f"Error organizing output files: {str(e)}", None, None, None, None, None, None)
+
+        # --- Convert Markdown to PDF ---
+        def md_to_pdf_in_dir(md_filename):
+            try:
+                md_path = os.path.join(new_output_dir, md_filename)
+                if os.path.isfile(md_path):
+                    return convert_md_to_pdf(md_path)
+                return ""
+            except Exception as e:
+                return f"Error converting {md_filename} to PDF: {str(e)}"
+
+        pdf_opt = md_to_pdf_in_dir("optimized_resume.md")
+        pdf_final = md_to_pdf_in_dir("final_report.md")
+        pdf_int = md_to_pdf_in_dir("interview_questions.md")
+
+        message = f"Processing completed using model {model_choice}. Output saved in: {new_output_dir}"
+
+        return (message, pdf_opt, pdf_opt, pdf_final, pdf_final, pdf_int, pdf_int)
+
+    except Exception as e:
+        return (f"Unexpected error: {str(e)}", None, None, None, None, None, None)
 
 # --- Define available models ---
 model_choices = {
@@ -179,7 +170,7 @@ with gr.Blocks(css=".output-column { width: 700px; }") as demo:
             model_dropdown = gr.Dropdown(
                 choices=list(model_choices.values()),
                 label="Select Model",
-                value="o3-mini-2025-01-31",
+                value="gpt-4o-2024-08-06",
                 interactive=True,
                 info="Select the model to use for processing."
             )