Initialize email_text variable in update_ui function to prevent potential reference errors. This change ensures that the email_text is defined before use, contributing to a more robust and error-free email generation process.
Enhance email generation feedback in app.py by adding a loading spinner during the process and a success message upon completion. This improvement provides users with better visual cues while the application generates their emails, contributing to a more engaging and informative user experience.
Update app.py to clarify email generation instructions by specifying the use of exact contact details from the CV, eliminating placeholders. This change enhances the professionalism and accuracy of the generated job application emails, ensuring users provide the correct information for better communication.
Refactor app.py to simplify CV text extraction and enhance email generation. Removed the parse_cv_sections function, returning the full CV text instead of structured sections. Updated related functions to accommodate this change, improving the overall flow and user experience in the Streamlit interface. This refactor streamlines the process of handling CV uploads and job descriptions, ensuring a more efficient email generation workflow.
Enhance CV parsing functionality in app.py by expanding section header patterns to include more variations and improving line cleaning. Implement a more robust categorization process for unstructured content, extracting contact information, education, experience, and skills based on keywords. Additionally, update text extraction from DOCX files to include table content, ensuring comprehensive CV data retrieval. This change improves the accuracy and completeness of CV parsing, contributing to better email generation based on user inputs.
Update app.py to enhance the UI by adding a unique key to the "Generate Email" button and refining the text area update logic. This change improves the handling of UI components and ensures better management of state during email generation, contributing to a smoother user experience.
Enhance app.py to improve error handling during email generation. Added try-except blocks in both conversation_predict and update_ui functions to catch exceptions and display error messages in the Streamlit interface. This change ensures a more robust user experience by providing feedback in case of issues during response generation.
Refactor app.py to enhance the Streamlit UI for the AI Job Application Email Generator. Updated the update_ui function to accept parameters for message, cv_file, and cv_sections, improving the handling of user inputs. Reorganized the layout to ensure a smoother flow for CV uploads and job description input, while maintaining clear feedback for users. This change streamlines the email generation process and improves overall user experience.
Refactor app.py to implement streaming response generation for email creation. Removed tokenizer and model initialization in favor of using the InferenceClient for improved performance and reduced complexity. Updated conversation_predict function to yield streaming output, enhancing user experience with real-time email generation feedback. Added a new update_ui function to manage email display in the Streamlit interface, ensuring a smoother interaction flow.
Refactor app.py to streamline CV parsing and email generation. Removed the 'other' section from CV parsing logic, simplifying the handling of miscellaneous content. Updated the email prompt to clarify instructions and enhance professionalism. Increased max_new_tokens for model generation to improve response quality. Improved UI feedback for generated emails in the Streamlit interface.
Reduce max_new_tokens in model generation from 2048 to 512 in app.py to optimize response length and improve performance. This change aims to enhance the efficiency of the conversation prediction function.
Enhance app.py to support an 'other' section in CV parsing for miscellaneous content. Update email generation prompt to include additional information and contact details. Refine response handling to ensure only the generated email is displayed, improving user experience and clarity.
Enhance app.py with CV parsing functionality and improved email generation. Introduced a new function to parse CV sections from uploaded PDF or DOCX files, allowing structured extraction of contact, education, experience, skills, and projects. Updated the email generation prompt to include detailed guidelines for crafting job application emails based on parsed CV data. Streamlined the Streamlit UI to support CV uploads and display parsed details, improving user interaction and experience.
Refactor app.py to transition from Gradio to Streamlit for the job application email generator interface. Update UI components including text areas, file upload, and sliders for user input. Modify requirements.txt to remove Gradio and include necessary dependencies for Streamlit and Hugging Face. This change enhances user experience and streamlines the email generation process.
Update app.py to replace 'token' with 'API_KEY' in environment variable retrieval and adjust model/tokenizer initialization to use 'use_auth_token' for compatibility with Hugging Face's latest API changes. This ensures proper authentication handling when loading the model and tokenizer.
Update app.py to replace 'use_auth_token' with 'token' in model and tokenizer initialization for compatibility with Hugging Face's latest API changes. This adjustment ensures proper authentication handling when loading the model and tokenizer.
Refactor app.py to transition from Streamlit to Gradio for a more interactive user interface. Implement CV text extraction from both PDF and DOCX formats, enhancing file upload capabilities. Update email generation process to utilize Hugging Face's model directly, improving response generation. Modify requirements.txt to include 'gradio', 'huggingface_hub', and 'python-docx' for new dependencies.
Refactor app.py to remove device_map parameter from model initialization, simplifying the configuration for text generation. This change enhances code clarity and focuses on essential parameters.
Update app.py to increase max_new_tokens for text generation from 512 to 2048, enhancing model output capacity. Remove 'accelerate' package from requirements.txt to streamline dependencies.
Refactor app.py to streamline email generation process by removing unused memory initialization and integrating email history tracking. Added session state management for email history and improved display of previous email generations. Cleaned up comments and enhanced code readability.
Enhance .gitignore to include additional Python, environment, IDE, and model file exclusions. Refactor app.py to improve text generation pipeline initialization with better error handling and enhanced prompt structure for email generation. Update requirements.txt to specify minimum versions for dependencies, ensuring compatibility and stability.
Remove access token input from sidebar in app.py to streamline the CV upload process. This change simplifies the user interface by eliminating unnecessary fields.
Enhance app.py by importing the 'os' module for improved environment variable handling. This change supports better configuration management in the application.
Refactor app.py to implement CV upload and email generation features using Hugging Face's Gemma model. Removed unused job search functions and integrated PDF parsing for CVs. Updated requirements.txt to include PyPDF2 for PDF handling.
