Production Deployment (Phase 3)

Author: HxnDev

backend/app/__init__.py

@@ -6,7 +6,6 @@
 import os
 from importlib import import_module
 
-import google.generativeai as genai
 from dotenv import load_dotenv
 from flask import Flask
 from flask_cors import CORS
@@ -24,15 +23,15 @@ def create_app() -> Flask:
 
     # Initialize Flask app
     app = Flask(__name__)
-    
+
     # Configure CORS - allow all origins in development and specific origins in production
     is_production = os.getenv("FLASK_ENV") == "production"
-    
+
     if is_production:
         # In production, allow specific origins
         allowed_origins = [
-            "https://hxndev.github.io",       # GitHub Pages domain
-            "http://localhost:5173"            # Local dev frontend (for testing)
+            "https://hxndev.github.io",  # GitHub Pages domain
+            "http://localhost:5173",  # Local dev frontend (for testing)
         ]
         CORS(app, resources={r"/api/*": {"origins": allowed_origins}}, supports_credentials=True)
     else:
@@ -44,4 +43,4 @@ def create_app() -> Flask:
     routes = import_module(".routes", package="app")
     app.register_blueprint(routes.api_bp)
 
-    return app
+    return app

backend/app/main.py

@@ -3,16 +3,16 @@
 This module initializes and runs the Flask server.
 """
 
-import os
 import logging
+import os
 
 from . import create_app
 
 
 # Configure logging
 logging.basicConfig(
     level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
 )
 
 logger = logging.getLogger(__name__)
@@ -24,15 +24,15 @@
     # Get environment variables
     flask_env = os.getenv("FLASK_ENV", "development")
     port = int(os.getenv("PORT", "5050"))
-    
+
     # Only enable debug mode in development
     debug_mode = flask_env == "development"
-    
+
     logger.info(f"Starting JobFit API in {flask_env} mode on port {port}")
-    
+
     # Run the application
     app.run(
         debug=debug_mode,
         host="0.0.0.0",  # Listen on all interfaces
-        port=port
-    )
+        port=port,
+    )

backend/app/resume_analyzer.py

@@ -45,30 +45,30 @@ def extract_text_from_pdf(file_bytes: BinaryIO) -> str:
 
         # Use context manager for better resource management
         text = ""
-        
+
         # Open PDF reader with the buffer
         pdf = PdfReader(pdf_buffer)
-        
+
         # Process pages one by one to reduce memory usage
         for page in pdf.pages:
             # Extract text from the page and append to result
             page_text = page.extract_text()
             if page_text:
                 text += page_text + "\n"
-                
+
         # Close the buffer explicitly
         pdf_buffer.close()
-        
+
         # Force garbage collection to free up memory
         gc.collect()
-        
+
         # Truncate very long resume content to prevent token limits
         if len(text) > MAX_RESUME_CONTENT_LENGTH:
             logger.info(f"Truncating resume content from {len(text)} to {MAX_RESUME_CONTENT_LENGTH} chars")
             text = text[:MAX_RESUME_CONTENT_LENGTH] + "..."
-            
+
         return text
-        
+
     except Exception as e:
         logger.error(f"Error reading PDF: {str(e)}", exc_info=True)
         # Clean up resources on error
@@ -363,14 +363,14 @@ def generate_resume_review(resume_content: str, job_description: str, custom_ins
             prompt_resume = resume_content[:MAX_RESUME_CONTENT_LENGTH] + "..."
         else:
             prompt_resume = resume_content
-            
+
         # Truncate job description if it's very long
         if len(job_description) > MAX_JOB_DESCRIPTION_LENGTH:
             logger.info(f"Truncating job description for review from {len(job_description)} to {MAX_JOB_DESCRIPTION_LENGTH} chars")
             prompt_job = job_description[:MAX_JOB_DESCRIPTION_LENGTH] + "..."
         else:
             prompt_job = job_description
-            
+
         base_prompt = f"""
         You are a professional resume reviewer and career coach. Review this resume against the job description
         and provide detailed, actionable feedback to help improve the resume.
@@ -483,7 +483,7 @@ def generate_resume_review(resume_content: str, job_description: str, custom_ins
             except json.JSONDecodeError as e:
                 # Clean up memory on error
                 gc.collect()
-                
+
                 return {
                     "success": False,
                     "error": f"Invalid response format from AI model: {str(e)}",
@@ -495,4 +495,4 @@ def generate_resume_review(resume_content: str, job_description: str, custom_ins
     except Exception as e:
         # Clean up memory on error
         gc.collect()
-        return {"success": False, "error": f"Error generating resume review: {str(e)}"}
+        return {"success": False, "error": f"Error generating resume review: {str(e)}"}