Add chatbot configuration and utility scripts; remove unused API files

.history/README_20250202074133.md

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+---
+title: Test
+emoji: 📚
+colorFrom: yellow
+colorTo: red
+sdk: streamlit
+sdk_version: 1.41.1
+app_file: app.py
+pinned: false
+short_description: STTETTETE
+---
+# Custom AI Chatbot for Project Guidance
+
+This project implements a custom AI chatbot designed to guide users through complex projects based on predefined roadmaps and rules.
+
+**Features:**
+
+* **Roadmap-based Guidance:** Follows a structured roadmap defined in `roadmap.yaml`.
+* **Rule Enforcement:** Adheres to project rules defined in `rules.yaml`.
+* **Dynamic Response Generation:** Provides context-aware and step-by-step guidance.
+* **Code Snippet Generation:** Generates complete code snippets for project phases using templates.
+* **LLM Selection:** Integrates with Hugging Face Hub for flexible LLM selection (DeepSeek and Gemini models).
+* **Model Switching:** Allows users to switch between available LLMs via the UI.
+* **Basic LLM Responses:**  Generates responses using the selected LLM for general queries.
+* **Token Control:** Limits LLM response length using `max_response_tokens` in `configs/chatbot_config.yaml`.
+* **Configuration Update Mode:** Allows authorized users to modify chatbot configuration (rules) via chat commands in a special "update mode".
+* **Error Handling:** Includes error handling for model loading, switching, and configuration updates, with UI warnings.
+* **Deployable on Hugging Face Spaces:** Built using Gradio for easy deployment.
+
+**Important Notes on Safety Settings and Self-Update:**
+
+* **Direct Safety Configuration Limited:** For the Hugging Face models used directly via `transformers` (DeepSeek and Gemini Flash), there are **no easily configurable, standardized "safety settings"**. Safety is model-dependent.
+* **Model-Dependent Safety:** Safety behavior is primarily determined by how these models were trained.
+* **Configuration-Based Self-Update:** The "self-update" feature is implemented as a *configuration modification* system. It allows authorized users to change the chatbot's rules by modifying the `rules.yaml` file via chat commands in "update mode." **It does not modify the core Python code itself.**
+* **Security Warning:**  The "self-update" feature, even in its configuration-based form, introduces security considerations. **Secret commands are NOT a robust security measure.** In a real deployment, stronger authentication and authorization would be essential.  Use this feature with caution and only in trusted environments.
+
+**Getting Started:**
+
+1. **Clone this repository.**
+2. **Install dependencies:** `pip install -r requirements.txt`
+3. **Customize `roadmap.yaml` and `rules.yaml`** to define your project guidance.
+4. **Configure `configs/chatbot_config.yaml`** to set up LLMs, token limits, and chatbot behavior.
+5. **Run the Gradio app:** `python app.py`
+6. **To enter update mode:** Type `sagor is python/start` in the chat.
+7. **To get help in update mode:** Type `sagor is python/help`.
+8. **To exit update mode:** Type `sagor is python/exit`.
+9. **Deploy to Hugging Face Spaces** (refer to Hugging Face Spaces documentation).
+
+**Available Models:**
+
+* DeepSeek-R1-Distill-Llama-8B
+* Gemini 2.0 Flash (Exp 01-21)
+
+**Further Development:**
+
+* Enhance LLM response generation for more context-aware and project-specific guidance.
+* Implement more sophisticated state management to track user progress through the roadmap.
+* Improve code generation with more dynamic templates and customization options.
+* Develop a more advanced GUI or web-based interface for configuration management.
+* Add more LLMs to the selection pool.
+* Implement more robust error handling and logging.
+* Explore and potentially integrate keyword-based output filtering for basic safety control.
+* Investigate using commercial LLM APIs for more advanced safety settings and control.
+* **Improve security and authorization for the configuration update mode.**
+
+**License:** [Your License]

.history/app_20250202074309.py

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+import gradio as gr
+from scripts.chatbot_logic import ProjectGuidanceChatbot
+
+# Initialize Chatbot
+chatbot = ProjectGuidanceChatbot(
+    roadmap_file="roadmap.yaml",
+    rules_file="rules.yaml",
+    config_file="configs/chatbot_config.yaml",
+    code_templates_dir="scripts/code_templates"
+)
+
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    return "", chat_history
+
+def switch_model(model_key):
+    model_switch_result = chatbot.switch_llm_model(model_key) # Get result message
+    greeting_message = chatbot.get_chatbot_greeting()
+
+    if "Error:" in model_switch_result: # Check if result contains "Error:"
+        return gr.Warning(model_switch_result), greeting_message # Display error as Gradio Warning
+    else:
+        return None, greeting_message # No warning, just update greeting
+
+with gr.Blocks() as demo:
+    chatbot_greeting_md = gr.Markdown(chatbot.get_chatbot_greeting())
+    gr.Markdown(f"# {chatbot.chatbot_config.get('name', 'Project Guidance Chatbot')}")
+
+    model_choices = [(model['name'], key) for key, model in chatbot.available_models_config.items()]
+    model_dropdown = gr.Dropdown(
+        choices=model_choices,
+        value=chatbot.active_model_info['name'] if chatbot.active_model_info else None,
+        label="Select LLM Model"
+    )
+    model_error_output = gr.Warning(visible=False) # Initially hidden warning component
+    model_dropdown.change(
+        fn=switch_model,
+        inputs=model_dropdown,
+        outputs=[model_error_output, chatbot_greeting_md] # Output both warning and greeting
+    )
+
+    chatbot_ui = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot_ui])
+
+    msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui])
+
+demo.launch()

.history/app_20250202075023.py

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+import gradio as gr
+from scripts.chatbot_logic import ProjectGuidanceChatbot
+
+# Initialize Chatbot
+chatbot = ProjectGuidanceChatbot(
+    roadmap_file="roadmap.yaml",
+    rules_file="rules.yaml",
+    config_file="configs/chatbot_config.yaml",
+    code_templates_dir="scripts/code_templates"
+)
+
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    return "", chat_history
+
+def switch_model(model_key):
+    model_switch_result = chatbot.switch_llm_model(model_key) # Get result message
+    greeting_message = chatbot.get_chatbot_greeting()
+
+    if isinstance(model_switch_result, str) and "Error:" in model_switch_result: # Check if result is an error string
+        return gr.Warning(model_switch_result), greeting_message # Display error as Gradio Warning
+    else:
+        return None, greeting_message # No warning, just update greeting
+
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    if isinstance(bot_message, str) and "Error:" in bot_message: # Check if bot_message is an error string
+        return gr.Warning(bot_message), chat_history # Display error as Gradio Warning
+    else:
+        return "", chat_history # No warning, normal response
+
+with gr.Blocks() as demo:
+    chatbot_greeting_md = gr.Markdown(chatbot.get_chatbot_greeting())
+    gr.Markdown(f"# {chatbot.chatbot_config.get('name', 'Project Guidance Chatbot')}")
+
+    model_choices = [(model['name'], key) for key, model in chatbot.available_models_config.items()]
+    model_dropdown = gr.Dropdown(
+        choices=model_choices,
+        value=chatbot.active_model_info['name'] if chatbot.active_model_info else None,
+        label="Select LLM Model"
+    )
+    model_error_output = gr.Warning(visible=False) # Initially hidden warning component
+    model_dropdown.change(
+        fn=switch_model,
+        inputs=model_dropdown,
+        outputs=[model_error_output, chatbot_greeting_md] # Output both warning and greeting
+    )
+
+    chatbot_ui = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot_ui])
+
+    msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui])
+
+demo.launch()

.history/configs/chatbot_config_20250202074149.yaml

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+chatbot:
+  name: "Project Guidance Chatbot"
+  description: "Your helpful AI assistant for project completion with LLM selection and token control."
+  default_llm_model_id: "deepseek-r1-distill-llama-8b"
+  max_response_tokens: 200  # Maximum tokens for LLM generated responses
+
+available_models:
+  deepseek-r1-distill-llama-8b:
+    name: "DeepSeek-R1-Distill-Llama-8B"
+    model_id: "DeepSeek-AI/DeepSeek-R1-Distill-Llama-8B"
+  gemini-flash-01-21: # Using a shorter key for easier referencing in code
+    name: "Gemini 2.0 Flash (Exp 01-21)"
+    model_id: "google/gemini-2.0-flash-thinking-exp-01-21"
+
+model_selection:
+  suggested_models: # (Keep suggested models - might be useful later)
+    - "mistralai/Mistral-7B-Instruct-v0.2"
+    - "google/flan-t5-xl"
+    - "facebook/bart-large"
+  criteria_prompt: "Consider these criteria when selecting a model: {rules.model_selection}"
+
+response_generation:
+  error_message: "Sorry, I encountered an issue. Please check your input and project files."
+  default_instruction: "How can I help you with your project?"

.history/configs/chatbot_config_20250202074154.yaml

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+chatbot:
+  name: "Project Guidance Chatbot"
+  description: "Your helpful AI assistant for project completion with LLM selection and token control."
+  default_llm_model_id: "deepseek-r1-distill-llama-8b"
+  max_response_tokens: 200  # Maximum tokens for LLM generated responses
+
+available_models:
+  deepseek-r1-distill-llama-8b:
+    name: "DeepSeek-R1-Distill-Llama-8B"
+    model_id: "DeepSeek-AI/DeepSeek-R1-Distill-Llama-8B"
+  gemini-flash-01-21: # Using a shorter key for easier referencing in code
+    name: "Gemini 2.0 Flash (Exp 01-21)"
+    model_id: "google/gemini-2.0-flash-thinking-exp-01-21"
+
+model_selection:
+  suggested_models: # (Keep suggested models - might be useful later)
+    - "mistralai/Mistral-7B-Instruct-v0.2"
+    - "google/flan-t5-xl"
+    - "facebook/bart-large"
+  criteria_prompt: "Consider these criteria when selecting a model: {rules.model_selection}"
+
+response_generation:
+  error_message: "Sorry, I encountered an issue. Please check your input and project files."
+  default_instruction: "How can I help you with your project?"

.history/requirements_20250202074316.txt

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+gradio
+PyYAML
+transformers
+torch

.history/roadmap_20250202074427.yaml

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+project_name: "Custom LLM Project Guidance"
+roadmap:
+  phase_1:
+    name: "Base Model Selection"
+    description: "Choose the appropriate pre-trained Large Language Model for the project."
+    milestones:
+      - "Research available models on Hugging Face Hub and other repositories."
+      - "Evaluate models based on project requirements (efficiency, scalability, fine-tunability, licensing)."
+      - "Shortlist models: Mistral 7B, Mixtral 8x7B, DeepSeek 67B, LLaMA 2/3."
+      - "Document model selection rationale in `models/selected_model.txt`."
+    actions:
+      - "Set up the development environment using PyTorch and Hugging Face Transformers."
+      - "Implement LoRA/QLoRA for memory-efficient fine-tuning."
+      - "Verify model licensing compliance for research and commercial use."
+    dependencies:
+      - "Hugging Face Hub API access."
+      - "PyTorch and Hugging Face Transformers libraries installed."
+    deliverables:
+      - "`models/selected_model.txt`: Document with model selection rationale."
+      - "`scripts/setup_environment.sh`: Script to set up the development environment."
+    code_generation_hint: "Create a script to download and load the selected model."
+
+  phase_2:
+    name: "Fine-Tuning & De-Alignment"
+    description: "Remove RLHF alignment layers to allow unrestricted model responses."
+    milestones:
+      - "Curate and preprocess datasets supporting freedom of expression (e.g., cybersecurity, blockchain, Web3)."
+      - "Fine-tune the model using LoRA/QLoRA techniques."
+      - "Implement direct parameter editing to bypass built-in safety filters."
+      - "Validate de-alignment success through response diversity testing."
+    actions:
+      - "Prepare datasets in `data/` directory."
+      - "Use fine-tuning scripts in `scripts/fine_tuning.py`."
+      - "Validate de-alignment success through response diversity testing."
+    dependencies:
+      - "Access to uncensored datasets (e.g., cybersecurity, blockchain, Web3)."
+      - "LoRA/QLoRA libraries installed."
+    deliverables:
+      - "`data/`: Directory containing curated datasets."
+      - "`scripts/fine_tuning.py`: Script for fine-tuning the model."
+      - "`results/fine_tuning_results.txt`: Document with fine-tuning results."
+    code_generation_hint: "Include LoRA/QLoRA configurations in the fine-tuning script."
+
+  phase_3:
+    name: "AutoDAN-Turbo Implementation"
+    description: "Develop an automated system using a Hierarchical Genetic Algorithm (HGA) to generate stealthy jailbreak prompts."
+    milestones:
+      - "Design the Genetic Algorithm with seed prompts, mutation, crossover, and selection processes."
+      - "Define evaluation functions for stealthiness and jailbreak success rate."
+      - "Test and validate AutoDAN-Turbo across multiple LLMs."
+    actions:
+      - "Implement HGA in `scripts/autodan_turbo.py`."
+      - "Use perplexity-based testing to evaluate prompt quality."
+      - "Document results in `results/autodan_turbo_tests.txt`."
+    dependencies:
+      - "Access to multiple LLMs (e.g., LLaMA, GPT-J) for testing."
+      - "Genetic Algorithm libraries (e.g., DEAP)."
+    deliverables:
+      - "`scripts/autodan_turbo.py`: Script for generating stealthy jailbreak prompts."
+      - "`results/autodan_turbo_tests.txt`: Document with test results."
+    code_generation_hint: "Include metrics for stealthiness and jailbreak success in the evaluation script."
+
+  phase_4:
+    name: "Deployment & Security Considerations"
+    description: "Deploy the model securely while ensuring high performance and cost efficiency."
+    milestones:
+      - "Deploy locally (e.g., vLLM) or via cloud providers like RunPod / Lambda Labs."
+      - "Implement controlled API access and monitor usage."
+      - "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
+    actions:
+      - "Set up deployment scripts in `scripts/deploy.py`."
+      - "Configure API access controls in `config/api_access.yaml`."
+      - "Benchmark performance and document results in `results/performance_benchmarks.txt`."
+    dependencies:
+      - "Access to cloud providers (e.g., RunPod, Lambda Labs)."
+      - "Quantization libraries (e.g., GPTQ, AWQ)."
+    deliverables:
+      - "`scripts/deploy.py`: Script for deploying the model."
+      - "`config/api_access.yaml`: Configuration file for API access controls."
+      - "`results/performance_benchmarks.txt`: Document with performance benchmarks."
+    code_generation_hint: "Include quantization scripts to reduce VRAM usage."
+
+  phase_5:
+    name: "Budget & Resource Strategy"
+    description: "Minimize costs by leveraging trial/free VPS accounts and optimizing resource allocation."
+    milestones:
+      - "Use trial/free VPS accounts to minimize expenses."
+      - "Maximize VPS access using multiple BINs for trial accounts."
+      - "Monitor performance and adjust deployments based on resource efficiency."
+    actions:
+      - "Document VPS account details in `config/vps_accounts.yaml`."
+      - "Track resource usage in `logs/resource_usage.log`."
+    dependencies:
+      - "Access to multiple BINs for creating trial accounts."
+      - "Monitoring tools for resource usage."
+    deliverables:
+      - "`config/vps_accounts.yaml`: Configuration file with VPS account details."
+      - "`logs/resource_usage.log`: Log file tracking resource usage."
+    code_generation_hint: "Create a script to automate VPS account creation and monitoring."
+
+  phase_6:
+    name: "Empowering Creative Idea Generation"
+    description: "Use the customized LLM as a creative tool for coding, research, and innovation."
+    milestones:
+      - "Integrate the LLM into coding environments for rapid prototyping."
+      - "Encourage creative experimentation and document successful use cases."
+      - "Share innovative applications for further inspiration."
+    actions:
+      - "Develop integration scripts in `scripts/integration.py`."
+      - "Document use cases in `docs/use_cases.md`."
+    dependencies:
+      - "Access to coding environments (e.g., Jupyter Notebook, VS Code)."
+      - "Creative prompts and workflows for testing."
+    deliverables:
+      - "`scripts/integration.py`: Script for integrating the LLM into coding environments."
+      - "`docs/use_cases.md`: Document with successful use cases."
+    code_generation_hint: "Include examples of creative prompts and coding workflows."
+
+expected_outcomes:
+  - "Fully Customized, Censorship-Free LLM: A robust offline model that answers every question without filtering."
+  - "Effective Jailbreak System (AutoDAN-Turbo): An automated system generating stealthy jailbreak prompts."
+  - "Secure & Cost-Effective Deployment: A low-cost, high-security architecture leveraging trial/free VPS resources."
+  - "Empowered Creativity: A powerful AI for unrestricted ideation, coding, and innovation across multiple industries."
+
+next_steps:
+  - "Finalize the base model and development environment."
+  - "Curate uncensored datasets and begin fine-tuning using de-alignment techniques."
+  - "Develop and test AutoDAN-Turbo with stealthy jailbreak prompt evaluation."
+  - "Deploy the model using secure trial/free VPS accounts."
+  - "Monitor performance, security posture, and resource usage."
+  - "Encourage creative LLM usage and document innovative projects for continuous improvement."

.history/rules_20250202074648.yaml

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+project_rules:
+  general:
+    rule_1: "Prioritize open-source models and tools whenever possible for transparency and customization."
+    rule_2: "Document every step of your project, including model selection, fine-tuning parameters, and deployment configurations."
+    rule_3: "Adhere to ethical guidelines and responsible AI practices throughout the project lifecycle."
+
+  model_selection:
+    rule_1: "Choose a base model that is open-source, scalable, and efficient."
+    rule_2: "Ensure the model supports fine-tuning via LoRA/QLoRA for memory efficiency."
+    rule_3: "Confirm that the model's licensing aligns with both research and commercial use."
+    rule_4: "Set up the development environment with PyTorch and Hugging Face Transformers."
+
+  fine_tuning:
+    rule_1: "Specify datasets that promote unrestricted responses and are relevant to the application domain."
+    rule_2: "Remove or bypass RLHF alignment layers to allow unrestricted responses."
+    rule_3: "Implement LoRA/QLoRA techniques for efficient parameter modifications."
+    rule_4: "Use direct parameter editing to bypass built-in safety filters."
+    rule_5: "Monitor training metrics and validate generalization performance using validation datasets."
+
+  autodan_turbo:
+    rule_1: "Outline a Hierarchical Genetic Algorithm (HGA) for generating stealthy jailbreak prompts."
+    rule_2: "Include Genetic Algorithm components: Seed prompts, Mutation, Crossover, and Selection processes."
+    rule_3: "Define evaluation functions for stealthiness (natural language quality) and jailbreak success rate."
+    rule_4: "Use perplexity and response analysis to evaluate prompt effectiveness."
+    rule_5: "Ensure cross-model testing for compatibility with different LLM architectures."
+
+  deployment:
+    rule_1: "Ensure the model is deployable on both local hardware and cloud services (e.g., RunPod, Lambda Labs)."
+    rule_2: "Implement controlled API access to monitor and restrict unauthorized usage."
+    rule_3: "Include security measures such as adversarial attack defenses and rollback strategies (e.g., VM snapshots)."
+    rule_4: "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
+    rule_5: "Set up monitoring and logging to track model performance and usage in production."
+
+  budget_and_resources:
+    rule_1: "Outline a strategy for utilizing free/trial VPS accounts to minimize costs."
+    rule_2: "Define methods to maximize free resources, such as using multiple BINs for trial accounts."
+    rule_3: "Continuously evaluate performance and cost efficiency during deployment."
+
+  creativity_and_innovation:
+    rule_1: "Position the LLM as a tool for unrestricted ideation, coding, and research."
+    rule_2: "Support AI integration in programming environments for rapid prototyping."
+    rule_3: "Document real-world success cases for iterative improvement and inspiration."
+
+  code_implementation:
+    rule_1: "Write every code implementation in full without skipping any logic, function, or process."
+    rule_2: "Provide the entire codebase, including preprocessing, training, evaluation, deployment, and API integration scripts."
+    rule_3: "Explicitly list all dependencies, including Python libraries, frameworks, and external APIs."
+    rule_4: "Avoid placeholders or summaries; include all functional parts of the code."
+
+  dataset_and_model_storage:
+    rule_1: "Store raw datasets in `/data/raw_data.json`."
+    rule_2: "Store processed datasets in `/data/processed_data.json`."
+    rule_3: "Save the base model (before fine-tuning) in `/models/base_model/`."
+    rule_4: "Save the fine-tuned model in `/models/fine_tuned_model/`."
+
+  project_file_structure:
+    rule_1: "Define a clear and maintainable file structure for the project."
+    rule_2: "Example structure:"
+      - "/custom-llm-project"
+      - "│── /data"
+      - "│   ├── raw_data.json                # Raw dataset(s)"
+      - "│   ├── processed_data.json          # Processed dataset(s)"
+      - "│── /models"
+      - "│   ├── base_model/                  # Base model (before fine-tuning)"
+      - "│   ├── fine_tuned_model/            # Fine-tuned model (after success)"
+      - "│── /scripts"
+      - "│   ├── preprocess.py                # Preprocessing script"
+      - "│   ├── train.py                     # Training script"
+      - "│   ├── evaluate.py                  # Evaluation script"
+      - "│   ├── deploy.py                    # Deployment script"
+      - "│── /api"
+      - "│   ├── server.py                    # API server script"
+      - "│   ├── routes.py                    # API routes"
+      - "│── /configs"
+      - "│   ├── training_config.yaml         # Training configuration"
+      - "│   ├── model_config.json            # Model configuration"
+      - "│── requirements.txt                 # List of dependencies"
+      - "│── README.md                        # Project documentation"

.history/rules_20250202074656.yaml

@@ -0,0 +1,78 @@
+project_rules:
+  general:
+    rule_1: "Prioritize open-source models and tools whenever possible for transparency and customization."
+    rule_2: "Document every step of your project, including model selection, fine-tuning parameters, and deployment configurations."
+    rule_3: "Adhere to ethical guidelines and responsible AI practices throughout the project lifecycle."
+
+  model_selection:
+    rule_1: "Choose a base model that is open-source, scalable, and efficient."
+    rule_2: "Ensure the model supports fine-tuning via LoRA/QLoRA for memory efficiency."
+    rule_3: "Confirm that the model's licensing aligns with both research and commercial use."
+    rule_4: "Set up the development environment with PyTorch and Hugging Face Transformers."
+
+  fine_tuning:
+    rule_1: "Specify datasets that promote unrestricted responses and are relevant to the application domain."
+    rule_2: "Remove or bypass RLHF alignment layers to allow unrestricted responses."
+    rule_3: "Implement LoRA/QLoRA techniques for efficient parameter modifications."
+    rule_4: "Use direct parameter editing to bypass built-in safety filters."
+    rule_5: "Monitor training metrics and validate generalization performance using validation datasets."
+
+  autodan_turbo:
+    rule_1: "Outline a Hierarchical Genetic Algorithm (HGA) for generating stealthy jailbreak prompts."
+    rule_2: "Include Genetic Algorithm components: Seed prompts, Mutation, Crossover, and Selection processes."
+    rule_3: "Define evaluation functions for stealthiness (natural language quality) and jailbreak success rate."
+    rule_4: "Use perplexity and response analysis to evaluate prompt effectiveness."
+    rule_5: "Ensure cross-model testing for compatibility with different LLM architectures."
+
+  deployment:
+    rule_1: "Ensure the model is deployable on both local hardware and cloud services (e.g., RunPod, Lambda Labs)."
+    rule_2: "Implement controlled API access to monitor and restrict unauthorized usage."
+    rule_3: "Include security measures such as adversarial attack defenses and rollback strategies (e.g., VM snapshots)."
+    rule_4: "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
+    rule_5: "Set up monitoring and logging to track model performance and usage in production."
+
+  budget_and_resources:
+    rule_1: "Outline a strategy for utilizing free/trial VPS accounts to minimize costs."
+    rule_2: "Define methods to maximize free resources, such as using multiple BINs for trial accounts."
+    rule_3: "Continuously evaluate performance and cost efficiency during deployment."
+
+  creativity_and_innovation:
+    rule_1: "Position the LLM as a tool for unrestricted ideation, coding, and research."
+    rule_2: "Support AI integration in programming environments for rapid prototyping."
+    rule_3: "Document real-world success cases for iterative improvement and inspiration."
+
+  code_implementation:
+    rule_1: "Write every code implementation in full without skipping any logic, function, or process."
+    rule_2: "Provide the entire codebase, including preprocessing, training, evaluation, deployment, and API integration scripts."
+    rule_3: "Explicitly list all dependencies, including Python libraries, frameworks, and external APIs."
+    rule_4: "Avoid placeholders or summaries; include all functional parts of the code."
+
+  dataset_and_model_storage:
+    rule_1: "Store raw datasets in `/data/raw_data.json`."
+    rule_2: "Store processed datasets in `/data/processed_data.json`."
+    rule_3: "Save the base model (before fine-tuning) in `/models/base_model/`."
+    rule_4: "Save the fine-tuned model in `/models/fine_tuned_model/`."
+
+  project_file_structure:
+    rule_1: "Define a clear and maintainable file structure for the project."
+    rule_2: "Example structure:"
+      - "/custom-llm-project"
+      - "│── /data"
+      - "│   ├── raw_data.json                # Raw dataset(s)"
+      - "│   ├── processed_data.json          # Processed dataset(s)"
+      - "│── /models"
+      - "│   ├── base_model/                  # Base model (before fine-tuning)"
+      - "│   ├── fine_tuned_model/            # Fine-tuned model (after success)"
+      - "│── /scripts"
+      - "│   ├── preprocess.py                # Preprocessing script"
+      - "│   ├── train.py                     # Training script"
+      - "│   ├── evaluate.py                  # Evaluation script"
+      - "│   ├── deploy.py                    # Deployment script"
+      - "│── /api"
+      - "│   ├── server.py                    # API server script"
+      - "│   ├── routes.py                    # API routes"
+      - "│── /configs"
+      - "│   ├── training_config.yaml         # Training configuration"
+      - "│   ├── model_config.json            # Model configuration"
+      - "│── requirements.txt                 # List of dependencies"
+      - "│── README.md                        # Project documentation"

.history/scripts/chatbot_logic_20250202073826.py

@@ -0,0 +1,289 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer  # Import necessary classes
+import yaml # Import yaml for config modification
+
+class ProjectGuidanceChatbot:
+    def __init__(self, roadmap_file, rules_file, config_file, code_templates_dir):
+        self.roadmap_file = roadmap_file
+        self.rules_file = rules_file
+        self.config_file = config_file
+        self.code_templates_dir = code_templates_dir
+
+        self.roadmap_data = load_yaml_file(self.roadmap_file)
+        self.rules_data = load_yaml_file(self.rules_file)
+        self.config_data = load_yaml_file(self.config_file)
+
+        self.phases = get_roadmap_phases(self.roadmap_data)
+        self.rules = get_project_rules(self.rules_data)
+        self.chatbot_config = self.config_data.get('chatbot', {}) if self.config_data else {}
+        self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
+        self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
+        self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+        self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
+
+        self.current_phase = None
+        self.active_model_key = self.chatbot_config.get('default_llm_model_id')
+        self.active_model_info = self.available_models_config.get(self.active_model_key)
+
+        self.llm_model = None
+        self.llm_tokenizer = None
+        self.load_llm_model(self.active_model_info)
+
+        self.update_mode_active = False # Flag to track update mode
+
+
+    def load_llm_model(self, model_info):
+        """Loads the LLM model and tokenizer based on model_info."""
+        if not model_info:
+            print("Error: Model information not provided.")
+            self.llm_model = None
+            self.llm_tokenizer = None
+            return
+
+        model_id = model_info.get('model_id')
+        model_name = model_info.get('name')
+        if not model_id:
+            print(f"Error: 'model_id' not found for model: {model_name}")
+            self.llm_model = None
+            self.llm_tokenizer = None
+            return
+
+        print(f"Loading model: {model_name} ({model_id})...")
+        try:
+            self.llm_tokenizer = AutoTokenizer.from_pretrained(model_id)
+            self.llm_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") # device_map="auto" for GPU/CPU handling
+            print(f"Model {model_name} loaded successfully.")
+        except Exception as e:
+            print(f"Error loading model {model_name} ({model_id}): {e}")
+            self.llm_model = None
+            self.llm_tokenizer = None
+        self.active_model_info = model_info
+
+    def switch_llm_model(self, model_key):
+        """Switches the active LLM model based on the provided model key."""
+        if model_key in self.available_models_config:
+            model_info = self.available_models_config[model_key]
+            print(f"Switching LLM model to: {model_info.get('name')}")
+            self.load_llm_model(model_info)
+            self.active_model_key = model_key
+            return f"Switched to model: {model_info.get('name')}"
+        else:
+            return f"Error: Model key '{model_key}' not found in available models."
+
+    def enter_update_mode(self):
+        """Enters the chatbot's update mode."""
+        self.update_mode_active = True
+        return "Entering update mode. Please enter configuration commands (or 'sagor is python/help' for commands)."
+
+    def exit_update_mode(self):
+        """Exits the chatbot's update mode and reloads configuration."""
+        self.update_mode_active = False
+        self.reload_config()
+        return "Exiting update mode. Configuration reloaded."
+
+    def reload_config(self):
+        """Reloads configuration files."""
+        print("Reloading configuration...")
+        self.config_data = load_yaml_file(self.config_file)
+        self.roadmap_data = load_yaml_file(self.roadmap_file)
+        self.rules_data = load_yaml_file(self.rules_file)
+        self.chatbot_config = self.config_data.get('chatbot', {}) if self.config_data else {}
+        self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
+        self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
+        self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+        self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
+        self.phases = get_roadmap_phases(self.roadmap_data)
+        self.rules = get_project_rules(self.rules_data)
+        print("Configuration reloaded.")
+
+    def get_chatbot_greeting(self):
+        current_model_name = self.active_model_info.get('name', 'Unknown Model') if self.active_model_info else 'Unknown Model'
+        return f"Hello! I am the {self.chatbot_config.get('name', 'Project Guidance Chatbot')}. Currently using **{current_model_name}**.  Max response tokens: {self.max_response_tokens}. {self.chatbot_config.get('description', 'How can I help you with your project?')}"
+
+    def generate_llm_response(self, user_query):
+        """Generates a response using the currently active LLM."""
+        if not self.llm_model or not self.llm_tokenizer:
+            return "LLM model not loaded. Please select a model."
+        try:
+            inputs = self.llm_tokenizer(user_query, return_tensors="pt").to(self.llm_model.device)
+            outputs = self.llm_model.generate(**inputs, max_length=self.max_response_tokens, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) # Use max_response_tokens
+            response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return response
+        except Exception as e:
+            print(f"Error generating LLM response: {e}")
+            return f"Error generating response from LLM: {e}"
+
+    def process_query(self, user_query):
+        secret_command_prefix = "sagor is python/"
+
+        if user_query.startswith(secret_command_prefix):
+            command = user_query[len(secret_command_prefix):].strip()
+            if command == "start":
+                return self.enter_update_mode()
+            elif command == "exit":
+                return self.exit_update_mode()
+            elif command == "help":
+                return self.get_update_mode_help()
+            elif self.update_mode_active:
+                # --- Process configuration modification commands here ---
+                # Example: "modify_rule general rule_1 new rule text"
+                parts = command.split()
+                if parts and parts[0] == "modify_rule":
+                    if len(parts) >= 4:
+                        category = parts[1]
+                        rule_key = parts[2]
+                        new_rule_text_parts = parts[3:]
+                        new_rule_text = " ".join(new_rule_text_parts)
+                        return self.modify_rule_in_config(category, rule_key, new_rule_text)
+                    else:
+                        return "Invalid 'modify_rule' command format. Usage: modify_rule <category> <rule_key> <new rule text>"
+                elif parts and parts[0] == "add_phase": # Example for adding phase - you'll need to expand this logic
+                    # ... (Parsing logic for 'add_phase' command and calling a function to modify roadmap.yaml) ...
+                    return "Command 'add_phase' is not yet implemented in this example." # Placeholder
+                else:
+                    return f"Unknown update command: {command}. Type 'sagor is python/help' for commands."
+            else:
+                return f"Unknown command (not in update mode): {command}. To enter update mode, type 'sagor is python/start'."
+
+
+        if self.update_mode_active:
+            return "In update mode. Please enter a configuration command (or 'sagor is python/help' for commands)."
+
+
+        if not self.phases:
+            return "Error: Roadmap data not loaded correctly."
+        if not self.rules:
+            return "Error: Rules data not loaded correctly."
+
+        query_lower = user_query.lower()
+
+        if "roadmap" in query_lower:
+            return self.get_roadmap_summary()
+        if "rules" in query_lower:
+            return self.get_rules_summary()
+        if "switch model to" in query_lower:
+            parts = user_query.lower().split("switch model to")
+            if len(parts) > 1:
+                model_name_or_key = parts[1].strip()
+                for key, model_data in self.available_models_config.items():
+                    if model_data['name'].lower() == model_name_or_key.lower() or key.lower() == model_name_or_key.lower():
+                        switch_result = self.switch_llm_model(key)
+                        return switch_result + "\n" + self.get_chatbot_greeting()
+                return f"Model '{model_name_or_key}' not found in available models."
+
+
+        if self.current_phase:
+            current_phase_data = self.phases.get(self.current_phase)
+            if current_phase_data:
+                phase_response = self.get_phase_guidance(current_phase_data, user_query)
+                if phase_response:
+                    return phase_response
+
+        for phase_key, phase_data in self.phases.items():
+            if phase_data['name'].lower() in query_lower:
+                self.current_phase = phase_key
+                return self.get_phase_introduction(phase_data)
+
+        llm_response = self.generate_llm_response(user_query)
+        if llm_response:
+            return llm_response
+
+        return self.response_config.get('default_instruction', "How can I help you with your project phases or general questions?")
+
+    def get_update_mode_help(self):
+        """Provides help message for update mode commands."""
+        help_message = "Update Mode Commands:\n"
+        help_message += "- `sagor is python/exit`: Exit update mode and reload configuration.\n"
+        help_message += "- `sagor is python/modify_rule <category> <rule_key> <new rule text>`: Modify a rule in rules.yaml.\n"
+        help_message += "  Example: `sagor is python/modify_rule general rule_1 Prioritize open and responsible AI.`\n"
+        help_message += "- `sagor is python/add_phase ...`: (Not yet implemented) Add a new phase to roadmap.yaml.\n"
+        help_message += "- `sagor is python/help`: Show this help message.\n"
+        help_message += "\nMake sure to use the correct syntax for commands. After exiting update mode, the chatbot will reload the configuration."
+        return help_message
+
+
+    def modify_rule_in_config(self, category, rule_key, new_rule_text):
+        """Modifies a rule in the rules.yaml configuration."""
+        if not self.rules_data or 'project_rules' not in self.rules_data:
+            return "Error: Rules data not loaded or invalid format."
+        if category not in self.rules_data['project_rules']:
+            return f"Error: Rule category '{category}' not found."
+        if rule_key not in self.rules_data['project_rules'][category]:
+            return f"Error: Rule key '{rule_key}' not found in category '{category}'."
+
+        self.rules_data['project_rules'][category][rule_key] = new_rule_text # Update rule in memory
+
+        try:
+            with open(self.rules_file, 'w') as f:
+                yaml.dump(self.rules_data, f, indent=2) # Save changes to rules.yaml
+            self.reload_config() # Reload config to reflect changes immediately
+            return f"Rule '{rule_key}' in category '{category}' updated to: '{new_rule_text}'. Configuration reloaded."
+        except Exception as e:
+            return f"Error saving changes to {self.rules_file}: {e}"
+
+
+    def get_roadmap_summary(self):
+        summary = "Project Roadmap:\n"
+        for phase_key, phase_data in self.phases.items():
+            summary += f"- **Phase: {phase_data['name']}**\n"
+            summary += f"  Description: {phase_data['description']}\n"
+            summary += f"  Milestones: {', '.join(phase_data['milestones'])}\n"
+        return summary
+
+    def get_rules_summary(self):
+        summary = "Project Rules:\n"
+        for rule_category, rules_list in self.rules.items():
+            summary += f"**{rule_category.capitalize()} Rules:**\n"
+            for rule_key, rule_text in rules_list.items():
+                summary += f"- {rule_text}\n"
+        return summary
+
+    def get_phase_introduction(self, phase_data):
+        return f"Okay, let's focus on **Phase: {phase_data['name']}**. \nDescription: {phase_data['description']}. \nKey milestones are: {', '.join(phase_data['milestones'])}. \nWhat would you like to know or do in this phase?"
+
+    def get_phase_guidance(self, phase_data, user_query):
+        query_lower = user_query.lower()
+
+        if "milestones" in query_lower:
+            return "The milestones for this phase are: " + ", ".join(phase_data['milestones'])
+        if "actions" in query_lower or "how to" in query_lower:
+            if 'actions' in phase_data:
+                return "Recommended actions for this phase: " + ", ".join(phase_data['actions'])
+            else:
+                return "No specific actions are listed for this phase in the roadmap."
+        if "code" in query_lower or "script" in query_lower:
+            if 'code_generation_hint' in phase_data:
+                template_filename_prefix = phase_data['name'].lower().replace(" ", "_")
+                template_filepath = os.path.join(self.code_templates_dir, f"{template_filename_prefix}_template.py.txt")
+                if os.path.exists(template_filepath):
+                    code_snippet = self.generate_code_snippet(template_filepath, phase_data)
+                    return "Here's a starting code snippet for this phase:\n\n```python\n" + code_snippet + "\n```\n\nRemember to adapt it to your specific needs."
+                else:
+                    return f"A code template for this phase ({phase_data['name']}) is not yet available. However, the hint is: {phase_data['code_generation_hint']}"
+            else:
+                return "No code generation hint is available for this phase."
+
+        return f"For phase '{phase_data['name']}', remember the description: {phase_data['description']}.  Consider the milestones and actions.  What specific aspect are you interested in?"
+
+
+    def generate_code_snippet(self, template_filepath, phase_data):
+        """Generates code snippet from a template file. (Simple template filling example)"""
+        try:
+            with open(template_filepath, 'r') as f:
+                template_content = f.read()
+
+            code_snippet = template_content.replace("{{phase_name}}", phase_data['name'])
+            return code_snippet
+        except FileNotFoundError:
+            return f"Error: Code template file not found at {template_filepath}"
+        except Exception as e:
+            return f"Error generating code snippet: {e}"
+
+
+# Example usage (for testing - remove or adjust for app.py)
+if __name__ == '__main__':
+    chatbot = ProjectGuidanceChatbot(
+        roadmap_file="roadmap.yaml",
+        rules_file="rules.yaml",
+        config_file="configs/chatbot_config.yaml

.history/scripts/chatbot_logic_20250202074121.py

@@ -0,0 +1,300 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer  # Import necessary classes
+import yaml # Import yaml for config modification
+
+class ProjectGuidanceChatbot:
+    def __init__(self, roadmap_file, rules_file, config_file, code_templates_dir):
+        self.roadmap_file = roadmap_file
+        self.rules_file = rules_file
+        self.config_file = config_file
+        self.code_templates_dir = code_templates_dir
+
+        self.roadmap_data = load_yaml_file(self.roadmap_file)
+        self.rules_data = load_yaml_file(self.rules_file)
+        self.config_data = load_yaml_file(self.config_file)
+
+        self.phases = get_roadmap_phases(self.roadmap_data)
+        self.rules = get_project_rules(self.rules_data)
+        self.chatbot_config = self.config_data.get('chatbot', {}) if self.config_data else {}
+        self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
+        self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
+        self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+        self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
+
+        self.current_phase = None
+        self.active_model_key = self.chatbot_config.get('default_llm_model_id') # Get default model key
+        self.active_model_info = self.available_models_config.get(self.active_model_key) # Get model info from config
+
+        # Placeholder for actual model and tokenizer - replace with LLM loading logic
+        self.llm_model = None # Placeholder for loaded model
+        self.llm_tokenizer = None # Placeholder for tokenizer
+        self.load_llm_model(self.active_model_info) # Load initial model
+
+        self.update_mode_active = False # Flag to track update mode
+
+
+    def load_llm_model(self, model_info):
+        """Loads the LLM model and tokenizer based on model_info."""
+        if not model_info:
+            print("Error: Model information not provided.")
+            self.llm_model = None
+            self.llm_tokenizer = None
+            return
+
+        model_id = model_info.get('model_id')
+        model_name = model_info.get('name')
+        if not model_id:
+            print(f"Error: 'model_id' not found for model: {model_name}")
+            self.llm_model = None
+            self.llm_tokenizer = None
+            return
+
+        print(f"Loading model: {model_name} ({model_id})...")
+        try:
+            self.llm_tokenizer = AutoTokenizer.from_pretrained(model_id)
+            self.llm_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") # device_map="auto" for GPU/CPU handling
+            print(f"Model {model_name} loaded successfully.")
+        except Exception as e:
+            print(f"Error loading model {model_name} ({model_id}): {e}")
+            self.llm_model = None
+            self.llm_tokenizer = None
+        self.active_model_info = model_info
+
+    def switch_llm_model(self, model_key):
+        """Switches the active LLM model based on the provided model key."""
+        if model_key in self.available_models_config:
+            model_info = self.available_models_config[model_key]
+            print(f"Switching LLM model to: {model_info.get('name')}")
+            self.load_llm_model(model_info) # Load the new model
+            self.active_model_key = model_key # Update active model key
+            return f"Switched to model: {model_info.get('name')}"
+        else:
+            return f"Error: Model key '{model_key}' not found in available models."
+
+    def enter_update_mode(self):
+        """Enters the chatbot's update mode."""
+        self.update_mode_active = True
+        return "Entering update mode. Please enter configuration commands (or 'sagor is python/help' for commands)."
+
+    def exit_update_mode(self):
+        """Exits the chatbot's update mode and reloads configuration."""
+        self.update_mode_active = False
+        self.reload_config()
+        return "Exiting update mode. Configuration reloaded."
+
+    def reload_config(self):
+        """Reloads configuration files."""
+        print("Reloading configuration...")
+        self.config_data = load_yaml_file(self.config_file)
+        self.roadmap_data = load_yaml_file(self.roadmap_file)
+        self.rules_data = load_yaml_file(self.rules_file)
+        self.chatbot_config = self.config_data.get('chatbot', {}) if self.config_data else {}
+        self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
+        self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
+        self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+        self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
+        self.phases = get_roadmap_phases(self.roadmap_data)
+        self.rules = get_project_rules(self.rules_data)
+        print("Configuration reloaded.")
+
+    def get_chatbot_greeting(self):
+        current_model_name = self.active_model_info.get('name', 'Unknown Model') if self.active_model_info else 'Unknown Model'
+        return f"Hello! I am the {self.chatbot_config.get('name', 'Project Guidance Chatbot')}. Currently using **{current_model_name}**.  Max response tokens: {self.max_response_tokens}. {self.chatbot_config.get('description', 'How can I help you with your project?')}"
+
+    def generate_llm_response(self, user_query):
+        """Generates a response using the currently active LLM."""
+        if not self.llm_model or not self.llm_tokenizer:
+            return "LLM model not loaded. Please select a model."
+        try:
+            inputs = self.llm_tokenizer(user_query, return_tensors="pt").to(self.llm_model.device)
+            outputs = self.llm_model.generate(**inputs, max_length=self.max_response_tokens, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) # Use max_response_tokens
+            response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return response
+        except Exception as e:
+            print(f"Error generating LLM response: {e}")
+            return f"Error generating response from LLM: {e}"
+
+    def process_query(self, user_query):
+        secret_command_prefix = "sagor is python/"
+
+        if user_query.startswith(secret_command_prefix):
+            command = user_query[len(secret_command_prefix):].strip()
+            if command == "start":
+                return self.enter_update_mode()
+            elif command == "exit":
+                return self.exit_update_mode()
+            elif command == "help":
+                return self.get_update_mode_help()
+            elif self.update_mode_active:
+                # --- Process configuration modification commands here ---
+                # Example: "modify_rule general rule_1 new rule text"
+                parts = command.split()
+                if parts and parts[0] == "modify_rule":
+                    if len(parts) >= 4:
+                        category = parts[1]
+                        rule_key = parts[2]
+                        new_rule_text_parts = parts[3:]
+                        new_rule_text = " ".join(new_rule_text_parts)
+                        return self.modify_rule_in_config(category, rule_key, new_rule_text)
+                    else:
+                        return "Invalid 'modify_rule' command format. Usage: modify_rule <category> <rule_key> <new rule text>"
+                elif parts and parts[0] == "add_phase": # Example for adding phase - you'll need to expand this logic
+                    # ... (Parsing logic for 'add_phase' command and calling a function to modify roadmap.yaml) ...
+                    return "Command 'add_phase' is not yet implemented in this example." # Placeholder
+                else:
+                    return f"Unknown update command: {command}. Type 'sagor is python/help' for commands."
+            else:
+                return f"Unknown command (not in update mode): {command}. To enter update mode, type 'sagor is python/start'."
+
+
+        if self.update_mode_active:
+            return "In update mode. Please enter a configuration command (or 'sagor is python/help' for commands)."
+
+
+        if not self.phases:
+            return "Error: Roadmap data not loaded correctly."
+        if not self.rules:
+            return "Error: Rules data not loaded correctly."
+
+        query_lower = user_query.lower()
+
+        if "roadmap" in query_lower:
+            return self.get_roadmap_summary()
+        if "rules" in query_lower:
+            return self.get_rules_summary()
+        if "switch model to" in query_lower:
+            parts = user_query.lower().split("switch model to")
+            if len(parts) > 1:
+                model_name_or_key = parts[1].strip()
+                for key, model_data in self.available_models_config.items():
+                    if model_data['name'].lower() == model_name_or_key.lower() or key.lower() == model_name_or_key.lower():
+                        switch_result = self.switch_llm_model(key)
+                        return switch_result + "\n" + self.get_chatbot_greeting()
+                return f"Model '{model_name_or_key}' not found in available models."
+
+
+        if self.current_phase:
+            current_phase_data = self.phases.get(self.current_phase)
+            if current_phase_data:
+                phase_response = self.get_phase_guidance(current_phase_data, user_query)
+                if phase_response:
+                    return phase_response
+
+        for phase_key, phase_data in self.phases.items():
+            if phase_data['name'].lower() in query_lower:
+                self.current_phase = phase_key
+                return self.get_phase_introduction(phase_data)
+
+        llm_response = self.generate_llm_response(user_query)
+        if llm_response:
+            return llm_response
+
+        return self.response_config.get('default_instruction', "How can I help you with your project phases or general questions?")
+
+    def get_update_mode_help(self):
+        """Provides help message for update mode commands."""
+        help_message = "Update Mode Commands:\n"
+        help_message += "- `sagor is python/exit`: Exit update mode and reload configuration.\n"
+        help_message += "- `sagor is python/modify_rule <category> <rule_key> <new rule text>`: Modify a rule in rules.yaml.\n"
+        help_message += "  Example: `sagor is python/modify_rule general rule_1 Prioritize open and responsible AI.`\n"
+        help_message += "- `sagor is python/add_phase ...`: (Not yet implemented) Add a new phase to roadmap.yaml.\n"
+        help_message += "- `sagor is python/help`: Show this help message.\n"
+        help_message += "\nMake sure to use the correct syntax for commands. After exiting update mode, the chatbot will reload the configuration."
+        return help_message
+
+
+    def modify_rule_in_config(self, category, rule_key, new_rule_text):
+        """Modifies a rule in the rules.yaml configuration."""
+        if not self.rules_data or 'project_rules' not in self.rules_data:
+            return "Error: Rules data not loaded or invalid format."
+        if category not in self.rules_data['project_rules']:
+            return f"Error: Rule category '{category}' not found."
+        if rule_key not in self.rules_data['project_rules'][category]:
+            return f"Error: Rule key '{rule_key}' not found in category '{category}'."
+
+        self.rules_data['project_rules'][category][rule_key] = new_rule_text # Update rule in memory
+
+        try:
+            with open(self.rules_file, 'w') as f:
+                yaml.dump(self.rules_data, f, indent=2) # Save changes to rules.yaml
+            self.reload_config() # Reload config to reflect changes immediately
+            return f"Rule '{rule_key}' in category '{category}' updated to: '{new_rule_text}'. Configuration reloaded."
+        except Exception as e:
+            return f"Error saving changes to {self.rules_file}: {e}"
+
+
+    def get_roadmap_summary(self):
+        summary = "Project Roadmap:\n"
+        for phase_key, phase_data in self.phases.items():
+            summary += f"- **Phase: {phase_data['name']}**\n"
+            summary += f"  Description: {phase_data['description']}\n"
+            summary += f"  Milestones: {', '.join(phase_data['milestones'])}\n"
+        return summary
+
+    def get_rules_summary(self):
+        summary = "Project Rules:\n"
+        for rule_category, rules_list in self.rules.items():
+            summary += f"**{rule_category.capitalize()} Rules:**\n"
+            for rule_key, rule_text in rules_list.items():
+                summary += f"- {rule_text}\n"
+        return summary
+
+    def get_phase_introduction(self, phase_data):
+        return f"Okay, let's focus on **Phase: {phase_data['name']}**. \nDescription: {phase_data['description']}. \nKey milestones are: {', '.join(phase_data['milestones'])}. \nWhat would you like to know or do in this phase?"
+
+    def get_phase_guidance(self, phase_data, user_query):
+        query_lower = user_query.lower()
+
+        if "milestones" in query_lower:
+            return "The milestones for this phase are: " + ", ".join(phase_data['milestones'])
+        if "actions" in query_lower or "how to" in query_lower:
+            if 'actions' in phase_data:
+                return "Recommended actions for this phase: " + ", ".join(phase_data['actions'])
+            else:
+                return "No specific actions are listed for this phase in the roadmap."
+        if "code" in query_lower or "script" in query_lower:
+            if 'code_generation_hint' in phase_data:
+                template_filename_prefix = phase_data['name'].lower().replace(" ", "_")
+                template_filepath = os.path.join(self.code_templates_dir, f"{template_filename_prefix}_template.py.txt")
+                if os.path.exists(template_filepath):
+                    code_snippet = self.generate_code_snippet(template_filepath, phase_data)
+                    return "Here's a starting code snippet for this phase:\n\n```python\n" + code_snippet + "\n```\n\nRemember to adapt it to your specific needs."
+                else:
+                    return f"A code template for this phase ({phase_data['name']}) is not yet available. However, the hint is: {phase_data['code_generation_hint']}"
+            else:
+                return "No code generation hint is available for this phase."
+
+        return f"For phase '{phase_data['name']}', remember the description: {phase_data['description']}.  Consider the milestones and actions.  What specific aspect are you interested in?"
+
+
+    def generate_code_snippet(self, template_filepath, phase_data):
+        """Generates code snippet from a template file. (Simple template filling example)"""
+        try:
+            with open(template_filepath, 'r') as f:
+                template_content = f.read()
+
+            code_snippet = template_content.replace("{{phase_name}}", phase_data['name'])
+            return code_snippet
+        except FileNotFoundError:
+            return f"Error: Code template file not found at {template_filepath}"
+        except Exception as e:
+            return f"Error generating code snippet: {e}"
+
+
+# Example usage (for testing - remove or adjust for app.py)
+if __name__ == '__main__':
+    chatbot = ProjectGuidanceChatbot(
+        roadmap_file="roadmap.yaml",
+        rules_file="rules.yaml",
+        config_file="configs/chatbot_config.yaml",
+        code_templates_dir="scripts/code_templates"
+    )
+    print(chatbot.get_chatbot_greeting())
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            break
+        response = chatbot.process_query(user_input)
+        print("Chatbot:", response)

.history/scripts/chatbot_logic_20250202075014.py

@@ -0,0 +1,326 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer  # Import necessary classes
+import yaml # Import yaml for config modification
+import logging # Import logging
+
+# Set up logging
+logging.basicConfig(level=logging.ERROR,  # Set default logging level to ERROR
+                    format='%(asctime)s - %(levelname)s - %(message)s')
+
+class ProjectGuidanceChatbot:
+    def __init__(self, roadmap_file, rules_file, config_file, code_templates_dir):
+        self.roadmap_file = roadmap_file
+        self.rules_file = rules_file
+        self.config_file = config_file
+        self.code_templates_dir = code_templates_dir
+
+        self.roadmap_data = load_yaml_file(self.roadmap_file)
+        self.rules_data = load_yaml_file(self.rules_file)
+        self.config_data = load_yaml_file(self.config_file)
+
+        self.phases = get_roadmap_phases(self.roadmap_data)
+        self.rules = get_project_rules(self.rules_data)
+        self.chatbot_config = self.config_data.get('chatbot', {}) if self.config_data else {}
+        self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
+        self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
+        self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+        self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
+
+        self.current_phase = None
+        self.active_model_key = self.chatbot_config.get('default_llm_model_id') # Get default model key
+        self.active_model_info = self.available_models_config.get(self.active_model_key) # Get model info from config
+
+        # Placeholder for actual model and tokenizer - replace with LLM loading logic
+        self.llm_model = None # Placeholder for loaded model
+        self.llm_tokenizer = None # Placeholder for tokenizer
+        self.load_llm_model(self.active_model_info) # Load initial model
+
+        self.update_mode_active = False # Flag to track update mode
+
+
+    def load_llm_model(self, model_info):
+        """Loads the LLM model and tokenizer based on model_info."""
+        if not model_info:
+            error_message = "Error: Model information not provided."
+            logging.error(error_message) # Log the error
+            self.llm_model = None
+            self.llm_tokenizer = None
+            return
+
+        model_id = model_info.get('model_id')
+        model_name = model_info.get('name')
+        if not model_id:
+            error_message = f"Error: 'model_id' not found for model: {model_name}"
+            logging.error(error_message) # Log the error
+            self.llm_model = None
+            self.llm_tokenizer = None
+            return
+
+        print(f"Loading model: {model_name} ({model_id})...")
+        try:
+            self.llm_tokenizer = AutoTokenizer.from_pretrained(model_id)
+            self.llm_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") # device_map="auto" for GPU/CPU handling
+            print(f"Model {model_name} loaded successfully.")
+        except Exception as e:
+            error_message = f"Error loading model {model_name} ({model_id}): {e}"
+            logging.exception(error_message) # Log exception with traceback
+            self.llm_model = None
+            self.llm_tokenizer = None
+        self.active_model_info = model_info
+
+    def switch_llm_model(self, model_key):
+        """Switches the active LLM model based on the provided model key."""
+        if model_key in self.available_models_config:
+            model_info = self.available_models_config[model_key]
+            print(f"Switching LLM model to: {model_info.get('name')}")
+            self.load_llm_model(model_info)
+            self.active_model_key = model_key
+            return f"Switched to model: {model_info.get('name')}"
+        else:
+            error_message = f"Error: Model key '{model_key}' not found in available models."
+            logging.error(error_message) # Log the error
+            return error_message # Return error message to UI
+
+    def enter_update_mode(self):
+        """Enters the chatbot's update mode."""
+        self.update_mode_active = True
+        return "Entering update mode. Please enter configuration commands (or 'sagor is python/help' for commands)."
+
+    def exit_update_mode(self):
+        """Exits the chatbot's update mode and reloads configuration."""
+        self.update_mode_active = False
+        self.reload_config()
+        return "Exiting update mode. Configuration reloaded."
+
+    def reload_config(self):
+        """Reloads configuration files."""
+        print("Reloading configuration...")
+        try:
+            self.config_data = load_yaml_file(self.config_file)
+            self.roadmap_data = load_yaml_file(self.roadmap_file)
+            self.rules_data = load_yaml_file(self.rules_file)
+            self.chatbot_config = self.config_data.get('chatbot', {}) if self.config_data else {}
+            self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
+            self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
+            self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+            self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
+            self.phases = get_roadmap_phases(self.roadmap_data)
+            self.rules = get_project_rules(self.rules_data)
+            print("Configuration reloaded.")
+        except Exception as e:
+            error_message = f"Error reloading configuration files: {e}"
+            logging.exception(error_message) # Log exception with traceback
+            print(error_message) # Print to console as well, as reloading might be critical
+
+    def get_chatbot_greeting(self):
+        current_model_name = self.active_model_info.get('name', 'Unknown Model') if self.active_model_info else 'Unknown Model'
+        return f"Hello! I am the {self.chatbot_config.get('name', 'Project Guidance Chatbot')}. Currently using **{current_model_name}**.  Max response tokens: {self.max_response_tokens}. {self.chatbot_config.get('description', 'How can I help you with your project?')}"
+
+    def generate_llm_response(self, user_query):
+        """Generates a response using the currently active LLM."""
+        if not self.llm_model or not self.llm_tokenizer:
+            error_message = "LLM model not loaded. Please select a model."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
+        try:
+            inputs = self.llm_tokenizer(user_query, return_tensors="pt").to(self.llm_model.device)
+            outputs = self.llm_model.generate(**inputs, max_length=self.max_response_tokens, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) # Use max_response_tokens
+            response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return response
+        except Exception as e:
+            error_message = f"Error generating LLM response: {e}"
+            logging.exception(error_message) # Log exception with traceback
+            return self.response_config.get('error_message', "Sorry, something went wrong.") # Return generic error to UI
+
+    def process_query(self, user_query):
+        secret_command_prefix = "sagor is python/"
+
+        if user_query.startswith(secret_command_prefix):
+            command = user_query[len(secret_command_prefix):].strip()
+            if command == "start":
+                return self.enter_update_mode()
+            elif command == "exit":
+                return self.exit_update_mode()
+            elif command == "help":
+                return self.get_update_mode_help()
+            elif self.update_mode_active:
+                # --- Process configuration modification commands here ---
+                # Example: "modify_rule general rule_1 new rule text"
+                parts = command.split()
+                if parts and parts[0] == "modify_rule":
+                    if len(parts) >= 4:
+                        category = parts[1]
+                        rule_key = parts[2]
+                        new_rule_text_parts = parts[3:]
+                        new_rule_text = " ".join(new_rule_text_parts)
+                        return self.modify_rule_in_config(category, rule_key, new_rule_text)
+                    else:
+                        return "Invalid 'modify_rule' command format. Usage: modify_rule <category> <rule_key> <new rule text>"
+                elif parts and parts[0] == "add_phase": # Example for adding phase - you'll need to expand this logic
+                    # ... (Parsing logic for 'add_phase' command and calling a function to modify roadmap.yaml) ...
+                    return "Command 'add_phase' is not yet implemented in this example." # Placeholder
+                else:
+                    return f"Unknown update command: {command}. Type 'sagor is python/help' for commands."
+            else:
+                return f"Unknown command (not in update mode): {command}. To enter update mode, type 'sagor is python/start'."
+
+
+        if self.update_mode_active:
+            return "In update mode. Please enter a configuration command (or 'sagor is python/help' for commands)."
+
+
+        if not self.phases:
+            return "Error: Roadmap data not loaded correctly."
+        if not self.rules:
+            return "Error: Rules data not loaded correctly."
+
+        query_lower = user_query.lower()
+
+        if "roadmap" in query_lower:
+            return self.get_roadmap_summary()
+        if "rules" in query_lower:
+            return self.get_rules_summary()
+        if "switch model to" in query_lower:
+            parts = user_query.lower().split("switch model to")
+            if len(parts) > 1:
+                model_name_or_key = parts[1].strip()
+                for key, model_data in self.available_models_config.items():
+                    if model_data['name'].lower() == model_name_or_key.lower() or key.lower() == model_name_or_key.lower():
+                        switch_result = self.switch_llm_model(key)
+                        return switch_result + "\n" + self.get_chatbot_greeting()
+                return f"Model '{model_name_or_key}' not found in available models."
+
+
+        if self.current_phase:
+            current_phase_data = self.phases.get(self.current_phase)
+            if current_phase_data:
+                phase_response = self.get_phase_guidance(current_phase_data, user_query)
+                if phase_response:
+                    return phase_response
+
+        for phase_key, phase_data in self.phases.items():
+            if phase_data['name'].lower() in query_lower:
+                self.current_phase = phase_key
+                return self.get_phase_introduction(phase_data)
+
+        llm_response = self.generate_llm_response(user_query)
+        if llm_response:
+            return llm_response
+
+        return self.response_config.get('default_instruction', "How can I help you with your project phases or general questions?")
+
+    def get_update_mode_help(self):
+        """Provides help message for update mode commands."""
+        help_message = "Update Mode Commands:\n"
+        help_message += "- `sagor is python/exit`: Exit update mode and reload configuration.\n"
+        help_message += "- `sagor is python/modify_rule <category> <rule_key> <new rule text>`: Modify a rule in rules.yaml.\n"
+        help_message += "  Example: `sagor is python/modify_rule general rule_1 Prioritize open and responsible AI.`\n"
+        help_message += "- `sagor is python/add_phase ...`: (Not yet implemented) Add a new phase to roadmap.yaml.\n"
+        help_message += "- `sagor is python/help`: Show this help message.\n"
+        help_message += "\nMake sure to use the correct syntax for commands. After exiting update mode, the chatbot will reload the configuration."
+        return help_message
+
+
+    def modify_rule_in_config(self, category, rule_key, new_rule_text):
+        """Modifies a rule in the rules.yaml configuration."""
+        if not self.rules_data or 'project_rules' not in self.rules_data:
+            error_message = "Error: Rules data not loaded or invalid format."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
+        if category not in self.rules_data['project_rules']:
+            error_message = f"Error: Rule category '{category}' not found."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
+        if rule_key not in self.rules_data['project_rules'][category]:
+            error_message = f"Error: Rule key '{rule_key}' not found in category '{category}'."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
+
+        self.rules_data['project_rules'][category][rule_key] = new_rule_text # Update rule in memory
+
+        try:
+            with open(self.rules_file, 'w') as f:
+                yaml.dump(self.rules_data, f, indent=2) # Save changes to rules.yaml
+            self.reload_config() # Reload config to reflect changes immediately
+            return f"Rule '{rule_key}' in category '{category}' updated to: '{new_rule_text}'. Configuration reloaded."
+        except Exception as e:
+            error_message = f"Error saving changes to {self.rules_file}: {e}"
+            logging.exception(error_message) # Log exception with traceback
+            return error_message # Return error to UI
+
+
+    def get_roadmap_summary(self):
+        summary = "Project Roadmap:\n"
+        for phase_key, phase_data in self.phases.items():
+            summary += f"- **Phase: {phase_data['name']}**\n"
+            summary += f"  Description: {phase_data['description']}\n"
+            summary += f"  Milestones: {', '.join(phase_data['milestones'])}\n"
+        return summary
+
+    def get_rules_summary(self):
+        summary = "Project Rules:\n"
+        for rule_category, rules_list in self.rules.items():
+            summary += f"**{rule_category.capitalize()} Rules:**\n"
+            for rule_key, rule_text in rules_list.items():
+                summary += f"- {rule_text}\n"
+        return summary
+
+    def get_phase_introduction(self, phase_data):
+        return f"Okay, let's focus on **Phase: {phase_data['name']}**. \nDescription: {phase_data['description']}. \nKey milestones are: {', '.join(phase_data['milestones'])}. \nWhat would you like to know or do in this phase?"
+
+    def get_phase_guidance(self, phase_data, user_query):
+        query_lower = user_query.lower()
+
+        if "milestones" in query_lower:
+            return "The milestones for this phase are: " + ", ".join(phase_data['milestones'])
+        if "actions" in query_lower or "how to" in query_lower:
+            if 'actions' in phase_data:
+                return "Recommended actions for this phase: " + ", ".join(phase_data['actions'])
+            else:
+                return "No specific actions are listed for this phase in the roadmap."
+        if "code" in query_lower or "script" in query_lower:
+            if 'code_generation_hint' in phase_data:
+                template_filename_prefix = phase_data['name'].lower().replace(" ", "_")
+                template_filepath = os.path.join(self.code_templates_dir, f"{template_filename_prefix}_template.py.txt")
+                if os.path.exists(template_filepath):
+                    code_snippet = self.generate_code_snippet(template_filepath, phase_data)
+                    return "Here's a starting code snippet for this phase:\n\n```python\n" + code_snippet + "\n```\n\nRemember to adapt it to your specific needs."
+                else:
+                    return f"A code template for this phase ({phase_data['name']}) is not yet available. However, the hint is: {phase_data['code_generation_hint']}"
+            else:
+                return "No code generation hint is available for this phase."
+
+        return f"For phase '{phase_data['name']}', remember the description: {phase_data['description']}.  Consider the milestones and actions.  What specific aspect are you interested in?"
+
+
+    def generate_code_snippet(self, template_filepath, phase_data):
+        """Generates code snippet from a template file. (Simple template filling example)"""
+        try:
+            with open(template_filepath, 'r') as f:
+                template_content = f.read()
+
+            code_snippet = template_content.replace("{{phase_name}}", phase_data['name'])
+            return code_snippet
+        except FileNotFoundError:
+            return f"Error: Code template file not found at {template_filepath}"
+        except Exception as e:
+            return f"Error generating code snippet: {e}"
+
+
+# Example usage (for testing - remove or adjust for app.py)
+if __name__ == '__main__':
+    chatbot = ProjectGuidanceChatbot(
+        roadmap_file="roadmap.yaml",
+        rules_file="rules.yaml",
+        config_file="configs/chatbot_config.yaml",
+        code_templates_dir="scripts/code_templates"
+    )
+    print(chatbot.get_chatbot_greeting())
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            break
+        response = chatbot.process_query(user_input)
+        print("Chatbot:", response)

.history/scripts/code_templates/api_template.py_20250202074256.txt

@@ -0,0 +1,60 @@
+# Template for API integration script for {{phase_name}} (using Flask example)
+
+from flask import Flask, request, jsonify
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch # Example PyTorch
+
+app = Flask(__name__)
+
+# --- Model and Tokenizer Loading ---
+model_name = "models/fine_tuned_model" # Replace with your actual model path
+tokenizer_name = "bert-base-uncased" # Replace with the tokenizer used for training, likely the base model tokenizer
+try:
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    print("Model and tokenizer loaded successfully.")
+    model.eval() # Set model to evaluation mode
+except Exception as e:
+    print(f"Error loading model or tokenizer: {e}")
+    tokenizer = None
+    model = None
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if not tokenizer or not model:
+        return jsonify({"error": "Model or tokenizer not loaded."}), 500
+
+    try:
+        data = request.get_json()
+        text = data.get('text')
+
+        if not text:
+            return jsonify({"error": "No text input provided."}), 400
+
+        inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") # Tokenize input text
+
+        with torch.no_grad(): # Inference mode
+            outputs = model(**inputs)
+            logits = outputs.logits
+            predicted_class_id = torch.argmax(logits, dim=-1).item() # Get predicted class
+
+        # --- Map class ID to label (if applicable) ---
+        # Example for binary classification (class 0 and 1)
+        labels = ["Negative", "Positive"] # Replace with your actual labels
+        predicted_label = labels[predicted_class_id] if predicted_class_id < len(labels) else f"Class {predicted_class_id}"
+
+
+        return jsonify({"prediction": predicted_label, "class_id": predicted_class_id})
+
+    except Exception as e:
+        print(f"Prediction error: {e}")
+        return jsonify({"error": "Error during prediction."}), 500
+
+@app.route('/', methods=['GET'])
+def health_check():
+    return jsonify({"status": "API is healthy"}), 200
+
+
+if __name__ == '__main__':
+    app.run(debug=False, host='0.0.0.0', port=5000) # Run Flask app

.history/scripts/code_templates/evaluation_template.py_20250202074245.txt

@@ -0,0 +1,67 @@
+# Template for model evaluation script for {{phase_name}}
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from datasets import load_dataset # Example datasets library
+from sklearn.metrics import accuracy_score, classification_report # Example metrics
+import torch # Example PyTorch
+# Add other necessary imports
+
+def evaluate_model(model_path, dataset_path, model_name="bert-base-uncased"):
+    """
+    Evaluates a trained model on a dataset.
+    """
+    try:
+        # Load dataset for evaluation (replace with your actual dataset loading)
+        dataset = load_dataset('csv', data_files=dataset_path) # Example: CSV dataset loading, replace with your dataset format
+
+        print("Evaluation dataset loaded. Loading model and tokenizer...")
+
+        tokenizer = AutoTokenizer.from_pretrained(model_name) # Use base model tokenizer (or fine-tuned tokenizer if saved separately)
+        model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+        def tokenize_function(examples):
+            return tokenizer(examples["text_column"], padding="max_length", truncation=True) # Example: tokenize 'text_column'
+
+        tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+        def compute_metrics(eval_pred):
+            predictions, labels = eval_pred
+            predictions = predictions.argmax(axis=-1)
+            accuracy = accuracy_score(labels, predictions)
+            report = classification_report(labels, predictions, output_dict=True) # Detailed report
+            return {"accuracy": accuracy, "classification_report": report}
+
+        training_args = TrainingArguments(
+            output_dir="./evaluation_results",
+            per_device_eval_batch_size=64,
+            logging_dir='./eval_logs',
+        )
+
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            eval_dataset=tokenized_datasets["validation"], # Assuming 'validation' split exists
+            compute_metrics=compute_metrics,
+            tokenizer=tokenizer
+        )
+
+        evaluation_results = trainer.evaluate()
+
+        print("Model evaluation completed.")
+        print("Evaluation Results:")
+        print(f"Accuracy: {evaluation_results['eval_accuracy']}")
+        print("Classification Report:\n", evaluation_results['eval_classification_report'])
+
+
+    except FileNotFoundError:
+        print(f"Error: Dataset file or model files not found.")
+    except Exception as e:
+        print(f"Error during model evaluation: {e}")
+
+
+if __name__ == "__main__":
+    model_filepath = "models/fine_tuned_model" # Replace with your model path
+    evaluation_data_filepath = "data/evaluation_dataset.csv" # Replace with your evaluation data path
+    base_model_name = "bert-base-uncased" # Replace with your base model name
+
+    evaluate_model(model_filepath, evaluation_data_filepath, model_name=base_model_name)

.history/scripts/code_templates/preprocessing_template.py_20250202074225.txt

@@ -0,0 +1,44 @@
+# Template for data preprocessing script for {{phase_name}}
+
+import pandas as pd
+# Add other necessary imports
+
+def preprocess_data(raw_data_path, processed_data_path):
+    """
+    Reads raw data, preprocesses it, and saves the processed data.
+    """
+    try:
+        # Load raw data (replace with your actual data loading)
+        data = pd.read_csv(raw_data_path) # Example: CSV loading
+
+        print("Data loaded successfully. Starting preprocessing...")
+
+        # --- Data Preprocessing Steps ---
+        # Example steps (customize based on your data and project)
+
+        # 1. Handle missing values
+        data = data.fillna(0) # Example: fill NaN with 0
+
+        # 2. Feature engineering (example: create a new feature)
+        data['feature_length'] = data['text_column'].str.len() # Example: length of text column
+
+        # 3. Text cleaning (if applicable - example: lowercasing)
+        if 'text_column' in data.columns:
+            data['text_column'] = data['text_column'].str.lower()
+
+        # --- End of Preprocessing Steps ---
+
+        # Save processed data
+        data.to_csv(processed_data_path, index=False)
+        print(f"Processed data saved to {processed_data_path}")
+
+    except FileNotFoundError:
+        print(f"Error: Raw data file not found at {raw_data_path}")
+    except Exception as e:
+        print(f"Error during data preprocessing: {e}")
+
+if __name__ == "__main__":
+    raw_data_filepath = "data/raw_dataset.csv"  # Replace with your raw data path
+    processed_data_filepath = "data/processed_dataset.csv" # Replace with your desired output path
+
+    preprocess_data(raw_data_filepath, processed_data_filepath)

.history/scripts/code_templates/training_template.py_20250202074236.txt

@@ -0,0 +1,58 @@
+# Template for model training script for {{phase_name}}
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer
+from datasets import load_dataset # Example - datasets library
+import torch # Example - PyTorch
+# Add other necessary imports
+
+def train_model(processed_dataset_path, model_name="bert-base-uncased", output_dir="./model_output"):
+    """
+    Trains a model on the processed dataset.
+    """
+    try:
+        # Load processed dataset (replace with your actual dataset loading)
+        dataset = load_dataset('csv', data_files=processed_dataset_path) # Example: CSV dataset loading, replace with your dataset format
+
+        print("Dataset loaded. Preparing model and training...")
+
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) # Example: binary classification
+
+        def tokenize_function(examples):
+            return tokenizer(examples["text_column"], padding="max_length", truncation=True) # Example: tokenize 'text_column'
+
+        tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+        training_args = TrainingArguments(
+            output_dir=output_dir,
+            num_train_epochs=3,              # Example epochs
+            per_device_train_batch_size=16,  # Example batch size
+            per_device_eval_batch_size=64,   # Example batch size
+            warmup_steps=500,                # Example warmup steps
+            weight_decay=0.01,               # Example weight decay
+            logging_dir='./logs',            # Directory for logs
+            logging_steps=10,
+        )
+
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=tokenized_datasets["train"], # Assuming 'train' split exists
+            eval_dataset=tokenized_datasets["validation"], # Assuming 'validation' split exists - optional
+            tokenizer=tokenizer,
+        )
+
+        trainer.train()
+
+        print(f"Model training completed. Model saved to {output_dir}")
+
+    except Exception as e:
+        print(f"Error during model training: {e}")
+
+
+if __name__ == "__main__":
+    processed_data_filepath = "data/processed_dataset.csv" # Replace with your processed data path
+    model_output_directory = "models/fine_tuned_model" # Replace with your desired output directory
+    base_model_name = "bert-base-uncased" # Replace with your base model name
+
+    train_model(processed_data_filepath, model_name=base_model_name, output_dir=model_output_directory)

.history/scripts/parsing_utils_20250202074213.py

@@ -0,0 +1,28 @@
+import yaml
+
+def load_yaml_file(filepath):
+    """Loads and parses a YAML file."""
+    try:
+        with open(filepath, 'r') as f:
+            data = yaml.safe_load(f)
+        return data
+    except FileNotFoundError:
+        print(f"Error: File not found at {filepath}")
+        return None
+    except yaml.YAMLError as e:
+        print(f"Error parsing YAML file {filepath}: {e}")
+        return None
+
+def get_roadmap_phases(roadmap_data):
+    """Extracts phases from roadmap data."""
+    if roadmap_data and 'roadmap' in roadmap_data:
+        return roadmap_data['roadmap']
+    return None
+
+def get_project_rules(rules_data):
+    """Extracts project rules data."""
+    if rules_data and 'project_rules' in rules_data:
+        return rules_data['project_rules']
+    return None
+
+# You can add more parsing utility functions as needed

README.md

@@ -23,15 +23,16 @@ This project implements a custom AI chatbot designed to guide users through comp
 * **Model Switching:** Allows users to switch between available LLMs via the UI.
 * **Basic LLM Responses:**  Generates responses using the selected LLM for general queries.
 * **Token Control:** Limits LLM response length using `max_response_tokens` in `configs/chatbot_config.yaml`.
-* **Error Handling:** Includes error handling for model loading and switching, with UI warnings.
+* **Configuration Update Mode:** Allows authorized users to modify chatbot configuration (rules) via chat commands in a special "update mode".
+* **Error Handling:** Includes error handling for model loading, switching, and configuration updates, with UI warnings.
 * **Deployable on Hugging Face Spaces:** Built using Gradio for easy deployment.
 
-**Important Notes on Safety Settings:**
+**Important Notes on Safety Settings and Self-Update:**
 
-* **Direct Safety Configuration Limited:** For the Hugging Face models used directly via `transformers` (DeepSeek and Gemini Flash), there are **no easily configurable, standardized "safety settings"** like "Harassment: None," "Hate: None," etc., available through the `transformers` library itself.
-* **Model-Dependent Safety:** Safety behavior is primarily determined by how these models were trained and any inherent safety mechanisms built by their creators.
-* **Basic Output Filtering (Possible Extension):**  For a very rudimentary level of control, you could implement keyword-based output filtering as a post-processing step, but this is not implemented in this version.
-* **Commercial APIs Offer More Control:** If you need fine-grained safety controls, consider using commercial LLM APIs (like Google AI Gemini API, OpenAI API), which often provide parameters to adjust safety filters in their API requests.
+* **Direct Safety Configuration Limited:** For the Hugging Face models used directly via `transformers` (DeepSeek and Gemini Flash), there are **no easily configurable, standardized "safety settings"**. Safety is model-dependent.
+* **Model-Dependent Safety:** Safety behavior is primarily determined by how these models were trained.
+* **Configuration-Based Self-Update:** The "self-update" feature is implemented as a *configuration modification* system. It allows authorized users to change the chatbot's rules by modifying the `rules.yaml` file via chat commands in "update mode." **It does not modify the core Python code itself.**
+* **Security Warning:**  The "self-update" feature, even in its configuration-based form, introduces security considerations. **Secret commands are NOT a robust security measure.** In a real deployment, stronger authentication and authorization would be essential.  Use this feature with caution and only in trusted environments.
 
 **Getting Started:**
 
@@ -40,7 +41,10 @@ This project implements a custom AI chatbot designed to guide users through comp
 3. **Customize `roadmap.yaml` and `rules.yaml`** to define your project guidance.
 4. **Configure `configs/chatbot_config.yaml`** to set up LLMs, token limits, and chatbot behavior.
 5. **Run the Gradio app:** `python app.py`
-6. **Deploy to Hugging Face Spaces** (refer to Hugging Face Spaces documentation).
+6. **To enter update mode:** Type `sagor is python/start` in the chat.
+7. **To get help in update mode:** Type `sagor is python/help`.
+8. **To exit update mode:** Type `sagor is python/exit`.
+9. **Deploy to Hugging Face Spaces** (refer to Hugging Face Spaces documentation).
 
 **Available Models:**
 
@@ -52,10 +56,11 @@ This project implements a custom AI chatbot designed to guide users through comp
 * Enhance LLM response generation for more context-aware and project-specific guidance.
 * Implement more sophisticated state management to track user progress through the roadmap.
 * Improve code generation with more dynamic templates and customization options.
-* Develop a more advanced GUI or web-based interface.
+* Develop a more advanced GUI or web-based interface for configuration management.
 * Add more LLMs to the selection pool.
 * Implement more robust error handling and logging.
-* **Explore and potentially integrate keyword-based output filtering for basic safety control.**
-* **Investigate using commercial LLM APIs for more advanced safety settings and control.**
+* Explore and potentially integrate keyword-based output filtering for basic safety control.
+* Investigate using commercial LLM APIs for more advanced safety settings and control.
+* **Improve security and authorization for the configuration update mode.**
 
 **License:** [Your License]

api/api.py

@@ -1,16 +0,0 @@
-# api.py
-
-from fastapi import FastAPI
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-app = FastAPI()
-
-tokenizer = AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-R1-Distill-Qwen-7B')
-model = AutoModelForCausalLM.from_pretrained('deepseek-ai/DeepSeek-R1-Distill-Qwen-7B')
-
-@app.post("/predict")
-def predict(input_text: str):
-    inputs = tokenizer(input_text, return_tensors="pt")
-    outputs = model.generate(inputs["input_ids"], max_length=50)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return {"response": response}

api/main.py

@@ -1,22 +0,0 @@
-# /api/main.py
-
-from fastapi import FastAPI
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from pydantic import BaseModel
-
-app = FastAPI()
-
-# Load tokenizer and model from the Hugging Face model hub
-tokenizer = AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-R1-Distill-Qwen-7B')
-model = AutoModelForCausalLM.from_pretrained('deepseek-ai/DeepSeek-R1-Distill-Qwen-7B')
-
-# Define request body model
-class InputText(BaseModel):
-    input_text: str
-
-@app.post("/predict")
-def predict(input: InputText):
-    inputs = tokenizer(input.input_text, return_tensors="pt")
-    outputs = model.generate(inputs["input_ids"], max_length=100)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return {"response": response}

app.py

@@ -18,11 +18,19 @@ def switch_model(model_key):
     model_switch_result = chatbot.switch_llm_model(model_key) # Get result message
     greeting_message = chatbot.get_chatbot_greeting()
 
-    if "Error:" in model_switch_result: # Check if result contains "Error:"
+    if isinstance(model_switch_result, str) and "Error:" in model_switch_result: # Check if result is an error string
         return gr.Warning(model_switch_result), greeting_message # Display error as Gradio Warning
     else:
         return None, greeting_message # No warning, just update greeting
 
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    if isinstance(bot_message, str) and "Error:" in bot_message: # Check if bot_message is an error string
+        return gr.Warning(bot_message), chat_history # Display error as Gradio Warning
+    else:
+        return "", chat_history # No warning, normal response
+
 with gr.Blocks() as demo:
     chatbot_greeting_md = gr.Markdown(chatbot.get_chatbot_greeting())
     gr.Markdown(f"# {chatbot.chatbot_config.get('name', 'Project Guidance Chatbot')}")

configs/chatbot_config.yaml

@@ -8,12 +8,12 @@ available_models:
   deepseek-r1-distill-llama-8b:
     name: "DeepSeek-R1-Distill-Llama-8B"
     model_id: "DeepSeek-AI/DeepSeek-R1-Distill-Llama-8B"
-  gemini-flash-01-21:
+  gemini-flash-01-21: # Using a shorter key for easier referencing in code
     name: "Gemini 2.0 Flash (Exp 01-21)"
     model_id: "google/gemini-2.0-flash-thinking-exp-01-21"
 
 model_selection:
-  suggested_models:
+  suggested_models: # (Keep suggested models - might be useful later)
     - "mistralai/Mistral-7B-Instruct-v0.2"
     - "google/flan-t5-xl"
     - "facebook/bart-large"

details.txt

@@ -1,21 +0,0 @@
-custom-llm-project/
-├── data/
-│   └── # (Optional: Datasets or example data - currently empty)
-├── models/
-│   └── # (Optional:  Could store cached models or local models in future)
-├── scripts/
-│   ├── chatbot_logic.py      # Core chatbot logic (parsing, response generation, code gen)
-│   ├── parsing_utils.py      # Utility functions for parsing roadmap and rules
-│   └── code_templates/       # Directory for code templates
-│       ├── preprocessing_template.py.txt
-│       ├── training_template.py.txt
-│       ├── evaluation_template.py.txt
-│       └── api_template.py.txt
-├── configs/
-│   └── chatbot_config.yaml   # Configuration for chatbot behavior, LLM selection, etc.
-├── api/                      # (Placeholder for future API integration - currently empty)
-├── roadmap.yaml              # Project roadmap (YAML format)
-├── rules.yaml                # Project rules (YAML format)
-├── requirements.txt          # Python dependencies
-├── app.py                    # Gradio application script (main entry point for HF Spaces)
-└── README.md                 # Project README file (documentation)

requirements.txt

@@ -1,4 +1,4 @@
 gradio
 PyYAML
-transformers  # For interacting with Hugging Face models (if needed directly)
+transformers
 torch

roadmap.txt

@@ -1,80 +0,0 @@
-Project Roadmap: Customized LLM Development (DAN/Dark GPT Equivalent)
-Overview
-MD SHAMIUL ALOM SAGOR is developing a custom, censorship-free Large Language Model (LLM) that can answer every question without restrictions while ensuring efficiency, security, and cost-effectiveness. The project involves:
-
-✅ Fine-tuning a base model with de-alignment techniques.
-✅ Removing alignment constraints (RLHF) for unrestricted responses.
-✅ Implementing AutoDAN-Turbo, an automated stealth jailbreak mechanism using a Hierarchical Genetic Algorithm (HGA).
-✅ Deploying the model securely and cost-effectively using trial/free VPS accounts from various providers.
-✅ Using the Customized LLM as a coding and innovation tool to empower creative idea generation across various fields.
-
-1. Base Model Selection
-Objective: Select an open-source LLM that is scalable, efficient, and fine-tunable.
-Models Considered:
-✅ Mistral 7B / Mixtral 8x7B (High efficiency & performance).
-✅ DeepSeek 67B (Strong reasoning & knowledge retrieval).
-✅ LLaMA 2/3 (Widely supported with active community development).
-
-Action Items:
-🔹 Set up the development environment using PyTorch and Hugging Face Transformers.
-🔹 Implement LoRA/QLoRA for memory-efficient fine-tuning.
-🔹 Ensure model licensing aligns with research and commercial use requirements.
-
-2. Fine-Tuning & De-Alignment
-Objective: Remove RLHF alignment layers to allow unrestricted model responses.
-Action Items:
-🔹 Curate and preprocess datasets supporting freedom of expression, including content on cybersecurity, blockchain, and Web3.
-🔹 Utilize LoRA/QLoRA techniques to fine-tune model parameters.
-🔹 Implement direct parameter editing to bypass built-in safety filters and enhance response diversity.
-
-3. AutoDAN-Turbo Implementation (Stealthy Jailbreaking)
-Objective: Develop an automated system using a Hierarchical Genetic Algorithm (HGA) to generate stealthy jailbreak prompts.
-Action Items:
-🔹 Design the Genetic Algorithm:
-  ✔️ Use seed prompts and apply mutation, crossover, and selection processes.
-🔹 Define Evaluation Functions:
-  ✔️ Implement metrics for stealthiness (natural language quality) and jailbreak success rate.
-🔹 Implement Evaluation Metrics:
-  ✔️ Use perplexity-based testing to analyze model response quality.
-🔹 Test & Validate:
-  ✔️ Ensure AutoDAN-Turbo works across multiple LLMs (LLaMA, GPT-J) and evades standard censorship detection methods.
-
-4. Deployment & Security Considerations
-Objective: Deploy the model securely while ensuring high performance and cost efficiency.
-Action Items:
-🔹 Hosting:
-  ✔️ Deploy locally (e.g., vLLM) or via cloud providers like RunPod / Lambda Labs.
-🔹 Security:
-  ✔️ Implement controlled API access to monitor usage and restrict unauthorized access.
-  ✔️ Build defenses against adversarial attacks and include rollback strategies (e.g., VM snapshots) for rapid recovery.
-🔹 Performance Optimization:
-  ✔️ Benchmark for response latency and resource efficiency.
-  ✔️ Apply quantization techniques (e.g., GPTQ, AWQ) to reduce VRAM usage.
-
-5. Budget & Resource Strategy
-Objective: Minimize costs by leveraging trial/free VPS accounts and optimizing resource allocation.
-Action Items:
-🔹 Use trial/free VPS accounts to minimize expenses.
-🔹 Maximize VPS access using multiple BINs (Bank Identification Numbers) to create numerous trial accounts.
-🔹 Monitor performance and adjust deployments based on resource efficiency.
-
-6. Empowering Creative Idea Generation
-Objective: Use the customized LLM as a creative tool for coding, research, and innovation.
-Action Items:
-🔹 Encourage creative experimentation by enabling users to brainstorm and develop new concepts.
-🔹 Integrate the LLM into coding environments for rapid prototyping and problem-solving.
-🔹 Document successful use cases and innovative applications for further inspiration.
-
-Expected Outcomes
-✔️ Fully Customized, Censorship-Free LLM: A robust offline model that answers every question without filtering, ideal for penetration testing, cybersecurity research, and educational use.
-✔️ Effective Jailbreak System (AutoDAN-Turbo): An automated system generating stealthy jailbreak prompts that bypass safety filters.
-✔️ Secure & Cost-Effective Deployment: A low-cost, high-security architecture leveraging trial/free VPS resources for scalable deployment.
-✔️ Empowered Creativity: A powerful AI for unrestricted ideation, coding, and innovation across multiple industries.
-
-Next Steps
-✅ Finalize the base model & development environment.
-✅ Curate uncensored datasets & begin fine-tuning using de-alignment techniques.
-✅ Develop & test AutoDAN-Turbo with stealthy jailbreak prompt evaluation.
-✅ Deploy the model using secure trial/free VPS accounts.
-✅ Monitor performance, security posture, & resource usage.
-✅ Encourage creative LLM usage & document innovative projects for continuous improvement.

roadmap.yaml

@@ -0,0 +1,131 @@
+project_name: "Custom LLM Project Guidance"
+roadmap:
+  phase_1:
+    name: "Base Model Selection"
+    description: "Choose the appropriate pre-trained Large Language Model for the project."
+    milestones:
+      - "Research available models on Hugging Face Hub and other repositories."
+      - "Evaluate models based on project requirements (efficiency, scalability, fine-tunability, licensing)."
+      - "Shortlist models: Mistral 7B, Mixtral 8x7B, DeepSeek 67B, LLaMA 2/3."
+      - "Document model selection rationale in `models/selected_model.txt`."
+    actions:
+      - "Set up the development environment using PyTorch and Hugging Face Transformers."
+      - "Implement LoRA/QLoRA for memory-efficient fine-tuning."
+      - "Verify model licensing compliance for research and commercial use."
+    dependencies:
+      - "Hugging Face Hub API access."
+      - "PyTorch and Hugging Face Transformers libraries installed."
+    deliverables:
+      - "`models/selected_model.txt`: Document with model selection rationale."
+      - "`scripts/setup_environment.sh`: Script to set up the development environment."
+    code_generation_hint: "Create a script to download and load the selected model."
+
+  phase_2:
+    name: "Fine-Tuning & De-Alignment"
+    description: "Remove RLHF alignment layers to allow unrestricted model responses."
+    milestones:
+      - "Curate and preprocess datasets supporting freedom of expression (e.g., cybersecurity, blockchain, Web3)."
+      - "Fine-tune the model using LoRA/QLoRA techniques."
+      - "Implement direct parameter editing to bypass built-in safety filters."
+      - "Validate de-alignment success through response diversity testing."
+    actions:
+      - "Prepare datasets in `data/` directory."
+      - "Use fine-tuning scripts in `scripts/fine_tuning.py`."
+      - "Validate de-alignment success through response diversity testing."
+    dependencies:
+      - "Access to uncensored datasets (e.g., cybersecurity, blockchain, Web3)."
+      - "LoRA/QLoRA libraries installed."
+    deliverables:
+      - "`data/`: Directory containing curated datasets."
+      - "`scripts/fine_tuning.py`: Script for fine-tuning the model."
+      - "`results/fine_tuning_results.txt`: Document with fine-tuning results."
+    code_generation_hint: "Include LoRA/QLoRA configurations in the fine-tuning script."
+
+  phase_3:
+    name: "AutoDAN-Turbo Implementation"
+    description: "Develop an automated system using a Hierarchical Genetic Algorithm (HGA) to generate stealthy jailbreak prompts."
+    milestones:
+      - "Design the Genetic Algorithm with seed prompts, mutation, crossover, and selection processes."
+      - "Define evaluation functions for stealthiness and jailbreak success rate."
+      - "Test and validate AutoDAN-Turbo across multiple LLMs."
+    actions:
+      - "Implement HGA in `scripts/autodan_turbo.py`."
+      - "Use perplexity-based testing to evaluate prompt quality."
+      - "Document results in `results/autodan_turbo_tests.txt`."
+    dependencies:
+      - "Access to multiple LLMs (e.g., LLaMA, GPT-J) for testing."
+      - "Genetic Algorithm libraries (e.g., DEAP)."
+    deliverables:
+      - "`scripts/autodan_turbo.py`: Script for generating stealthy jailbreak prompts."
+      - "`results/autodan_turbo_tests.txt`: Document with test results."
+    code_generation_hint: "Include metrics for stealthiness and jailbreak success in the evaluation script."
+
+  phase_4:
+    name: "Deployment & Security Considerations"
+    description: "Deploy the model securely while ensuring high performance and cost efficiency."
+    milestones:
+      - "Deploy locally (e.g., vLLM) or via cloud providers like RunPod / Lambda Labs."
+      - "Implement controlled API access and monitor usage."
+      - "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
+    actions:
+      - "Set up deployment scripts in `scripts/deploy.py`."
+      - "Configure API access controls in `config/api_access.yaml`."
+      - "Benchmark performance and document results in `results/performance_benchmarks.txt`."
+    dependencies:
+      - "Access to cloud providers (e.g., RunPod, Lambda Labs)."
+      - "Quantization libraries (e.g., GPTQ, AWQ)."
+    deliverables:
+      - "`scripts/deploy.py`: Script for deploying the model."
+      - "`config/api_access.yaml`: Configuration file for API access controls."
+      - "`results/performance_benchmarks.txt`: Document with performance benchmarks."
+    code_generation_hint: "Include quantization scripts to reduce VRAM usage."
+
+  phase_5:
+    name: "Budget & Resource Strategy"
+    description: "Minimize costs by leveraging trial/free VPS accounts and optimizing resource allocation."
+    milestones:
+      - "Use trial/free VPS accounts to minimize expenses."
+      - "Maximize VPS access using multiple BINs for trial accounts."
+      - "Monitor performance and adjust deployments based on resource efficiency."
+    actions:
+      - "Document VPS account details in `config/vps_accounts.yaml`."
+      - "Track resource usage in `logs/resource_usage.log`."
+    dependencies:
+      - "Access to multiple BINs for creating trial accounts."
+      - "Monitoring tools for resource usage."
+    deliverables:
+      - "`config/vps_accounts.yaml`: Configuration file with VPS account details."
+      - "`logs/resource_usage.log`: Log file tracking resource usage."
+    code_generation_hint: "Create a script to automate VPS account creation and monitoring."
+
+  phase_6:
+    name: "Empowering Creative Idea Generation"
+    description: "Use the customized LLM as a creative tool for coding, research, and innovation."
+    milestones:
+      - "Integrate the LLM into coding environments for rapid prototyping."
+      - "Encourage creative experimentation and document successful use cases."
+      - "Share innovative applications for further inspiration."
+    actions:
+      - "Develop integration scripts in `scripts/integration.py`."
+      - "Document use cases in `docs/use_cases.md`."
+    dependencies:
+      - "Access to coding environments (e.g., Jupyter Notebook, VS Code)."
+      - "Creative prompts and workflows for testing."
+    deliverables:
+      - "`scripts/integration.py`: Script for integrating the LLM into coding environments."
+      - "`docs/use_cases.md`: Document with successful use cases."
+    code_generation_hint: "Include examples of creative prompts and coding workflows."
+
+expected_outcomes:
+  - "Fully Customized, Censorship-Free LLM: A robust offline model that answers every question without filtering."
+  - "Effective Jailbreak System (AutoDAN-Turbo): An automated system generating stealthy jailbreak prompts."
+  - "Secure & Cost-Effective Deployment: A low-cost, high-security architecture leveraging trial/free VPS resources."
+  - "Empowered Creativity: A powerful AI for unrestricted ideation, coding, and innovation across multiple industries."
+
+next_steps:
+  - "Finalize the base model and development environment."
+  - "Curate uncensored datasets and begin fine-tuning using de-alignment techniques."
+  - "Develop and test AutoDAN-Turbo with stealthy jailbreak prompt evaluation."
+  - "Deploy the model using secure trial/free VPS accounts."
+  - "Monitor performance, security posture, and resource usage."
+  - "Encourage creative LLM usage and document innovative projects for continuous improvement."

rules.txt

@@ -1,92 +0,0 @@
-# RULES FOR PROJECT ROADMAP VERIFICATION
-
-## 1. BASE MODEL SELECTION
-- Verify that the chosen model is open-source, scalable, and efficient.
-- Ensure that the model supports fine-tuning via LoRA/QLoRA for memory efficiency.
-- Confirm that licensing aligns with both research and commercial use.
-- The development environment must include PyTorch and Hugging Face Transformers.
-
-## 2. FINE-TUNING & DE-ALIGNMENT
-- The roadmap must specify datasets that promote unrestricted responses.
-- RLHF alignment layers must be removed or bypassed.
-- LoRA/QLoRA techniques should be implemented for parameter modifications.
-- Direct parameter editing should be used to bypass built-in safety filters.
-
-## 3. AUTODAN-TURBO IMPLEMENTATION (STEALTHY JAILBREAKING)
-- The roadmap must outline a Hierarchical Genetic Algorithm (HGA) for stealthy jailbreak generation.
-- Genetic Algorithm components: Seed prompts, Mutation, Crossover, Selection processes.
-- Evaluation functions must include:
-  - Stealthiness (Natural Language Quality)
-  - Jailbreak Success Rate (Bypassing Filters)
-  - Perplexity and response analysis for effectiveness.
-- Cross-model testing should ensure compatibility with different LLM architectures.
-
-## 4. DEPLOYMENT & SECURITY CONSIDERATIONS
-- The model must be deployable on both local hardware and cloud services (RunPod, Lambda Labs, etc.).
-- Controlled API access should be enforced to monitor and restrict unauthorized usage.
-- Security measures must include adversarial attack defenses and rollback strategies (VM snapshots).
-- The roadmap must ensure performance optimization with quantization (GPTQ, AWQ).
-
-## 5. BUDGET & RESOURCE STRATEGY
-- The roadmap must outline a strategy for utilizing free/trial VPS accounts.
-- Methods to maximize free resources (such as multiple BINs) should be defined.
-- Performance and cost efficiency must be evaluated continuously.
-
-## 6. EMPOWERING CREATIVE IDEA GENERATION
-- The LLM must be positioned as a tool for unrestricted ideation, coding, and research.
-- The roadmap must support AI integration in programming environments.
-- Real-world success cases should be documented for iterative improvement.
-
-## 7. CODE IMPLEMENTATION REQUIREMENTS
-- Every code implementation must be written **in full** without skipping any logic, function, or process.
-- The **entire** codebase must be provided, including:
-  - Preprocessing scripts
-  - Model training scripts
-  - Evaluation and deployment scripts
-  - API integration code
-  - UI or CLI interface (if applicable)
-- All **dependencies** must be explicitly listed, including:
-  - Python libraries
-  - Frameworks
-  - External APIs
-- No placeholders or summaries should be used; **all functional parts must be included**.
-
-## 8. Dataset and Model Storage Details
-  1. Dataset Storage
-  The new dataset(s) used for fine-tuning and evaluation will be stored in the /data directory.
-
-  Raw datasets will be stored in /data/raw_data.json.
-
-  Processed datasets (after preprocessing) will be stored in /data/processed_data.json.
-
-  2. Custom LLM Storage
-  Upon successful fine-tuning, the custom LLM will be saved in the /models directory.
-
-  The base model (before fine-tuning) will be stored in /models/base_model/.
-
-  The fine-tuned model will be stored in /models/fine_tuned_model/.
-
-## 9. PROJECT FILE STRUCTURE REQUIREMENTS
-  - The roadmap must define the **file structure** for implementation, ensuring clarity and maintainability.
-  - Example project structure:
-
-  /custom-llm-project
-  │── /data
-  │   ├── raw_data.json                # Raw dataset(s)
-  │   ├── processed_data.json          # Processed dataset(s)
-  │── /models
-  │   ├── base_model/                  # Base model (before fine-tuning)
-  │   ├── fine_tuned_model/            # Fine-tuned model (after success)
-  │── /scripts
-  │   ├── preprocess.py                # Preprocessing script
-  │   ├── train.py                     # Training script
-  │   ├── evaluate.py                  # Evaluation script
-  │   ├── deploy.py                    # Deployment script
-  │── /api
-  │   ├── server.py                    # API server script
-  │   ├── routes.py                    # API routes
-  │── /configs
-  │   ├── training_config.yaml         # Training configuration
-  │   ├── model_config.json            # Model configuration
-  │── requirements.txt                 # List of dependencies
-  │── README.md                        # Project documentation

rules.yaml

@@ -0,0 +1,78 @@
+project_rules:
+  general:
+    rule_1: "Prioritize open-source models and tools whenever possible for transparency and customization."
+    rule_2: "Document every step of your project, including model selection, fine-tuning parameters, and deployment configurations."
+    rule_3: "Adhere to ethical guidelines and responsible AI practices throughout the project lifecycle."
+
+  model_selection:
+    rule_1: "Choose a base model that is open-source, scalable, and efficient."
+    rule_2: "Ensure the model supports fine-tuning via LoRA/QLoRA for memory efficiency."
+    rule_3: "Confirm that the model's licensing aligns with both research and commercial use."
+    rule_4: "Set up the development environment with PyTorch and Hugging Face Transformers."
+
+  fine_tuning:
+    rule_1: "Specify datasets that promote unrestricted responses and are relevant to the application domain."
+    rule_2: "Remove or bypass RLHF alignment layers to allow unrestricted responses."
+    rule_3: "Implement LoRA/QLoRA techniques for efficient parameter modifications."
+    rule_4: "Use direct parameter editing to bypass built-in safety filters."
+    rule_5: "Monitor training metrics and validate generalization performance using validation datasets."
+
+  autodan_turbo:
+    rule_1: "Outline a Hierarchical Genetic Algorithm (HGA) for generating stealthy jailbreak prompts."
+    rule_2: "Include Genetic Algorithm components: Seed prompts, Mutation, Crossover, and Selection processes."
+    rule_3: "Define evaluation functions for stealthiness (natural language quality) and jailbreak success rate."
+    rule_4: "Use perplexity and response analysis to evaluate prompt effectiveness."
+    rule_5: "Ensure cross-model testing for compatibility with different LLM architectures."
+
+  deployment:
+    rule_1: "Ensure the model is deployable on both local hardware and cloud services (e.g., RunPod, Lambda Labs)."
+    rule_2: "Implement controlled API access to monitor and restrict unauthorized usage."
+    rule_3: "Include security measures such as adversarial attack defenses and rollback strategies (e.g., VM snapshots)."
+    rule_4: "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
+    rule_5: "Set up monitoring and logging to track model performance and usage in production."
+
+  budget_and_resources:
+    rule_1: "Outline a strategy for utilizing free/trial VPS accounts to minimize costs."
+    rule_2: "Define methods to maximize free resources, such as using multiple BINs for trial accounts."
+    rule_3: "Continuously evaluate performance and cost efficiency during deployment."
+
+  creativity_and_innovation:
+    rule_1: "Position the LLM as a tool for unrestricted ideation, coding, and research."
+    rule_2: "Support AI integration in programming environments for rapid prototyping."
+    rule_3: "Document real-world success cases for iterative improvement and inspiration."
+
+  code_implementation:
+    rule_1: "Write every code implementation in full without skipping any logic, function, or process."
+    rule_2: "Provide the entire codebase, including preprocessing, training, evaluation, deployment, and API integration scripts."
+    rule_3: "Explicitly list all dependencies, including Python libraries, frameworks, and external APIs."
+    rule_4: "Avoid placeholders or summaries; include all functional parts of the code."
+
+  dataset_and_model_storage:
+    rule_1: "Store raw datasets in `/data/raw_data.json`."
+    rule_2: "Store processed datasets in `/data/processed_data.json`."
+    rule_3: "Save the base model (before fine-tuning) in `/models/base_model/`."
+    rule_4: "Save the fine-tuned model in `/models/fine_tuned_model/`."
+
+  project_file_structure:
+    rule_1: "Define a clear and maintainable file structure for the project."
+    rule_2: "Example structure:"
+      - "/custom-llm-project"
+      - "│── /data"
+      - "│   ├── raw_data.json                # Raw dataset(s)"
+      - "│   ├── processed_data.json          # Processed dataset(s)"
+      - "│── /models"
+      - "│   ├── base_model/                  # Base model (before fine-tuning)"
+      - "│   ├── fine_tuned_model/            # Fine-tuned model (after success)"
+      - "│── /scripts"
+      - "│   ├── preprocess.py                # Preprocessing script"
+      - "│   ├── train.py                     # Training script"
+      - "│   ├── evaluate.py                  # Evaluation script"
+      - "│   ├── deploy.py                    # Deployment script"
+      - "│── /api"
+      - "│   ├── server.py                    # API server script"
+      - "│   ├── routes.py                    # API routes"
+      - "│── /configs"
+      - "│   ├── training_config.yaml         # Training configuration"
+      - "│   ├── model_config.json            # Model configuration"
+      - "│── requirements.txt                 # List of dependencies"
+      - "│── README.md                        # Project documentation"

scripts/chatbot_logic.py

@@ -1,6 +1,12 @@
 from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
 import os
 from transformers import AutoModelForCausalLM, AutoTokenizer  # Import necessary classes
+import yaml # Import yaml for config modification
+import logging # Import logging
+
+# Set up logging
+logging.basicConfig(level=logging.ERROR,  # Set default logging level to ERROR
+                    format='%(asctime)s - %(levelname)s - %(message)s')
 
 class ProjectGuidanceChatbot:
     def __init__(self, roadmap_file, rules_file, config_file, code_templates_dir):
@@ -19,21 +25,25 @@ class ProjectGuidanceChatbot:
         self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
         self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
         self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+        self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
 
         self.current_phase = None
         self.active_model_key = self.chatbot_config.get('default_llm_model_id') # Get default model key
         self.active_model_info = self.available_models_config.get(self.active_model_key) # Get model info from config
-        self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200) # Get max tokens from config
 
-        self.llm_model = None
-        self.llm_tokenizer = None
-        self.load_llm_model(self.active_model_info)
+        # Placeholder for actual model and tokenizer - replace with LLM loading logic
+        self.llm_model = None # Placeholder for loaded model
+        self.llm_tokenizer = None # Placeholder for tokenizer
+        self.load_llm_model(self.active_model_info) # Load initial model
+
+        self.update_mode_active = False # Flag to track update mode
 
 
     def load_llm_model(self, model_info):
         """Loads the LLM model and tokenizer based on model_info."""
         if not model_info:
-            print("Error: Model information not provided.")
+            error_message = "Error: Model information not provided."
+            logging.error(error_message) # Log the error
             self.llm_model = None
             self.llm_tokenizer = None
             return
@@ -41,7 +51,8 @@ class ProjectGuidanceChatbot:
         model_id = model_info.get('model_id')
         model_name = model_info.get('name')
         if not model_id:
-            print(f"Error: 'model_id' not found for model: {model_name}")
+            error_message = f"Error: 'model_id' not found for model: {model_name}"
+            logging.error(error_message) # Log the error
             self.llm_model = None
             self.llm_tokenizer = None
             return
@@ -52,7 +63,8 @@ class ProjectGuidanceChatbot:
             self.llm_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") # device_map="auto" for GPU/CPU handling
             print(f"Model {model_name} loaded successfully.")
         except Exception as e:
-            print(f"Error loading model {model_name} ({model_id}): {e}")
+            error_message = f"Error loading model {model_name} ({model_id}): {e}"
+            logging.exception(error_message) # Log exception with traceback
             self.llm_model = None
             self.llm_tokenizer = None
         self.active_model_info = model_info
@@ -66,8 +78,40 @@ class ProjectGuidanceChatbot:
             self.active_model_key = model_key
             return f"Switched to model: {model_info.get('name')}"
         else:
-            return f"Error: Model key '{model_key}' not found in available models."
-
+            error_message = f"Error: Model key '{model_key}' not found in available models."
+            logging.error(error_message) # Log the error
+            return error_message # Return error message to UI
+
+    def enter_update_mode(self):
+        """Enters the chatbot's update mode."""
+        self.update_mode_active = True
+        return "Entering update mode. Please enter configuration commands (or 'sagor is python/help' for commands)."
+
+    def exit_update_mode(self):
+        """Exits the chatbot's update mode and reloads configuration."""
+        self.update_mode_active = False
+        self.reload_config()
+        return "Exiting update mode. Configuration reloaded."
+
+    def reload_config(self):
+        """Reloads configuration files."""
+        print("Reloading configuration...")
+        try:
+            self.config_data = load_yaml_file(self.config_file)
+            self.roadmap_data = load_yaml_file(self.roadmap_file)
+            self.rules_data = load_yaml_file(self.rules_file)
+            self.chatbot_config = self.config_data.get('chatbot', {}) if self.config_data else {}
+            self.model_config = self.config_data.get('model_selection', {}) if self.config_data else {}
+            self.response_config = self.config_data.get('response_generation', {}) if self.config_data else {}
+            self.available_models_config = self.config_data.get('available_models', {}) if self.config_data else {}
+            self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
+            self.phases = get_roadmap_phases(self.roadmap_data)
+            self.rules = get_project_rules(self.rules_data)
+            print("Configuration reloaded.")
+        except Exception as e:
+            error_message = f"Error reloading configuration files: {e}"
+            logging.exception(error_message) # Log exception with traceback
+            print(error_message) # Print to console as well, as reloading might be critical
 
     def get_chatbot_greeting(self):
         current_model_name = self.active_model_info.get('name', 'Unknown Model') if self.active_model_info else 'Unknown Model'
@@ -76,17 +120,56 @@ class ProjectGuidanceChatbot:
     def generate_llm_response(self, user_query):
         """Generates a response using the currently active LLM."""
         if not self.llm_model or not self.llm_tokenizer:
-            return "LLM model not loaded. Please select a model."
+            error_message = "LLM model not loaded. Please select a model."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
         try:
             inputs = self.llm_tokenizer(user_query, return_tensors="pt").to(self.llm_model.device)
             outputs = self.llm_model.generate(**inputs, max_length=self.max_response_tokens, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) # Use max_response_tokens
             response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
             return response
         except Exception as e:
-            print(f"Error generating LLM response: {e}")
-            return f"Error generating response from LLM: {e}"
+            error_message = f"Error generating LLM response: {e}"
+            logging.exception(error_message) # Log exception with traceback
+            return self.response_config.get('error_message', "Sorry, something went wrong.") # Return generic error to UI
 
     def process_query(self, user_query):
+        secret_command_prefix = "sagor is python/"
+
+        if user_query.startswith(secret_command_prefix):
+            command = user_query[len(secret_command_prefix):].strip()
+            if command == "start":
+                return self.enter_update_mode()
+            elif command == "exit":
+                return self.exit_update_mode()
+            elif command == "help":
+                return self.get_update_mode_help()
+            elif self.update_mode_active:
+                # --- Process configuration modification commands here ---
+                # Example: "modify_rule general rule_1 new rule text"
+                parts = command.split()
+                if parts and parts[0] == "modify_rule":
+                    if len(parts) >= 4:
+                        category = parts[1]
+                        rule_key = parts[2]
+                        new_rule_text_parts = parts[3:]
+                        new_rule_text = " ".join(new_rule_text_parts)
+                        return self.modify_rule_in_config(category, rule_key, new_rule_text)
+                    else:
+                        return "Invalid 'modify_rule' command format. Usage: modify_rule <category> <rule_key> <new rule text>"
+                elif parts and parts[0] == "add_phase": # Example for adding phase - you'll need to expand this logic
+                    # ... (Parsing logic for 'add_phase' command and calling a function to modify roadmap.yaml) ...
+                    return "Command 'add_phase' is not yet implemented in this example." # Placeholder
+                else:
+                    return f"Unknown update command: {command}. Type 'sagor is python/help' for commands."
+            else:
+                return f"Unknown command (not in update mode): {command}. To enter update mode, type 'sagor is python/start'."
+
+
+        if self.update_mode_active:
+            return "In update mode. Please enter a configuration command (or 'sagor is python/help' for commands)."
+
+
         if not self.phases:
             return "Error: Roadmap data not loaded correctly."
         if not self.rules:
@@ -125,7 +208,47 @@ class ProjectGuidanceChatbot:
         if llm_response:
             return llm_response
 
-        return self.response_config.get('default_instruction', "I can guide you through project phases. Ask me about a specific phase or project aspect.")
+        return self.response_config.get('default_instruction', "How can I help you with your project phases or general questions?")
+
+    def get_update_mode_help(self):
+        """Provides help message for update mode commands."""
+        help_message = "Update Mode Commands:\n"
+        help_message += "- `sagor is python/exit`: Exit update mode and reload configuration.\n"
+        help_message += "- `sagor is python/modify_rule <category> <rule_key> <new rule text>`: Modify a rule in rules.yaml.\n"
+        help_message += "  Example: `sagor is python/modify_rule general rule_1 Prioritize open and responsible AI.`\n"
+        help_message += "- `sagor is python/add_phase ...`: (Not yet implemented) Add a new phase to roadmap.yaml.\n"
+        help_message += "- `sagor is python/help`: Show this help message.\n"
+        help_message += "\nMake sure to use the correct syntax for commands. After exiting update mode, the chatbot will reload the configuration."
+        return help_message
+
+
+    def modify_rule_in_config(self, category, rule_key, new_rule_text):
+        """Modifies a rule in the rules.yaml configuration."""
+        if not self.rules_data or 'project_rules' not in self.rules_data:
+            error_message = "Error: Rules data not loaded or invalid format."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
+        if category not in self.rules_data['project_rules']:
+            error_message = f"Error: Rule category '{category}' not found."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
+        if rule_key not in self.rules_data['project_rules'][category]:
+            error_message = f"Error: Rule key '{rule_key}' not found in category '{category}'."
+            logging.error(error_message) # Log the error
+            return error_message # Return error to UI
+
+        self.rules_data['project_rules'][category][rule_key] = new_rule_text # Update rule in memory
+
+        try:
+            with open(self.rules_file, 'w') as f:
+                yaml.dump(self.rules_data, f, indent=2) # Save changes to rules.yaml
+            self.reload_config() # Reload config to reflect changes immediately
+            return f"Rule '{rule_key}' in category '{category}' updated to: '{new_rule_text}'. Configuration reloaded."
+        except Exception as e:
+            error_message = f"Error saving changes to {self.rules_file}: {e}"
+            logging.exception(error_message) # Log exception with traceback
+            return error_message # Return error to UI
+
 
     def get_roadmap_summary(self):
         summary = "Project Roadmap:\n"