Replaced custom log handler and async report generation logic with a simplified fake data streamer for the StreamingResponse. Added uvicorn server startup code for direct script execution.
Simplified the logic for log streaming by consolidating into a single async generator (`log_stream`). Removed redundant tasks and streamlined report generation to improve code readability and maintainability.
Enhanced report generation by integrating `CustomLogsHandler` and separating tasks for generating reports and streaming logs. Replaced the previous implementation with a unified async generator to handle concurrent execution and improve error handling. Updated module exports to include the new `CustomLogsHandler` component.
Replaced `generator.researcher.complete` with the correct `generator.complete`. This resolves a potential logic error and ensures the completion condition is properly evaluated.
Replaced `.is_complete()` method calls with direct `.complete` attribute access to streamline the code. Removed the redundant `is_complete()` method from `deepresearch.py` to reduce unnecessary indirection. This simplifies the logic and improves readability.
The is_complete method was incorrectly referenced without parentheses, causing a potential runtime error. This fix ensures proper method invocation and prevents the loop from behaving incorrectly.
Modified the report generation process to stream logs incrementally during execution. Introduced new methods `init` and `is_complete` to enhance modularity and track completion status. Adjusted generator logic to better manage and yield logs in real-time.
This change introduces status updates to indicate the start and successful completion of the report generation. These progress messages improve user feedback during the asynchronous operation.
Removed unused log yielding in `main.py` to simplify the flow. Updated `send_json` in `deepresearch.py` to use an async generator for more flexible streaming of log data.
The `generate_report` method is now explicitly called within the main process, ensuring the researcher's report generation is initiated properly. Updated its return type annotation for clarity in `deepresearch.py`.
Refactored `CustomLogsHandler` to accept an optional logs list and modified its method to append data instead of yielding. Enhanced `ReportGenerator` with proper asynchronous iterator implementation, enabling smooth log handling and retrieval. Simplified variable naming for better readability.
Implement a modular FastAPI-based service for generating research reports using `GPTResearcher`. Includes secure API key authentication, a streaming response endpoint, and a Dockerized deployment setup. Also adds documentation, core dependencies, and project structure.
