0.0.5

Version 0.0.5 (deprecated)

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Changes Made

1. User Interface Enhancement
   In the Advanced Analysis page, I added a new section "Project Objective" with a text area. This is where you can describe your goal (e.g., "Predict customer churn").

Advanced Analysis UI (Note: Placeholder caption for UI)

2. Objective-Driven SQL & EDA
   Dynamic Dataset Construction: When an objective is provided, the Automatic EDA Agent now uses the LLM to write a custom SQL query. This query can join multiple tables to build exactly the dataset needed for your goal.
   Targeted EDA: All subsequent EDA phases (Statistics, Missing Data, Correlations) now take the objective into account when generating insights.

3. Tailored Machine Learning Code
   The Code Adaptation Agent uses the ML objective to customize the load_data and preprocessing steps in the final Python script.
   The Insights Agent now evaluates the model's performance specifically against the stated objective.

4. Improved Join Accuracy & Schema Context
   AI-Friendly Schema: Added a specialized text-based schema representation (get_llm_schema_context) that explicitly lists columns and foreign key relationships.

Strict SQL Rules & Prompt Sanitization:
Removed all specific examples (like casino_id) from AI prompts to prevent "mimicry" of non-existent columns.
Added a Mandatory Reasoning Step: Agents must now list and verify columns from the schema BEFORE writing any code.
Improved Error Fixing: Added logic to prevent agents from repeating failed join keys, forcing them to find alternatives or simplify the query.

5. Technical Implementation
   Backend Endpoints: Updated Pydantic models in endpoints.py to support ml_objective.
   API Client: Updated client.ts to pass the new parameter through all stages of the pipeline.
   State Management: Ensured the objective is preserved across navigation between AdvancedAnalysis, EDAProgress, and Results pages.