push full macanism

.history/README_20250202035449.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
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

.history/README_20250202065215.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 provides a template for building a custom AI chatbot that guides users through projects based on predefined roadmaps and rules.
+
+## Getting Started
+
+1.  **Clone this repository.**
+2.  **Install dependencies:** `pip install -r requirements.txt`
+3.  **Customize `roadmap.txt` and `rules.txt`:** Define your project's roadmap and rules in YAML format.
+4.  **Run the chatbot:** `python app.py`
+5.  **Access the chatbot:** Open the Gradio link provided in the console.
+
+## Deployment on Hugging Face Spaces
+
+1.  **Create a new Space on Hugging Face Spaces.** Choose Gradio or Streamlit as the SDK.
+2.  **Upload all project files** to your Space repository.
+3.  **Hugging Face Spaces will automatically install dependencies from `requirements.txt` and run `app.py` (or your chosen app script).**
+
+## Customization
+
+*   **Modify `roadmap.txt` and `rules.txt`** to fit your specific project.
+*   **Implement actual LLM integration** in `guidance_system.py` and `app.py`.
+*   **Develop code generation logic** in `code_generator.py`.
+*   **Enhance the user interface** in `app.py`.
+*   **Add more sophisticated parsing and guidance logic** as needed.
+
+## File Structure
+
+*   `/data`:  Intended for storing project datasets (e.g., for fine-tuning). Currently a placeholder.
+*   `/models`: Intended for storing downloaded or trained models. Currently a placeholder.
+*   `/scripts`: Contains Python scripts for core chatbot logic (parsing, guidance, code generation, error handling).
+*   `/api`: Intended for API related files if you integrate with external services. Currently a placeholder.
+*   `/configs`: Contains configuration files like `config.yaml`.
+*   `app.py`: The main application script using Gradio for the user interface.
+*   `requirements.txt`: Lists Python dependencies.
+*   `roadmap.txt`: Defines the project roadmap in YAML format.
+*   `rules.txt`: Defines project rules and best practices in YAML format.
+
+## Example Roadmap and Rules
+
+See `roadmap.txt` and `rules.txt` for example content.
+
+## License
+
+[Your License] (e.g., MIT License)

.history/README_20250202065217.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 provides a template for building a custom AI chatbot that guides users through projects based on predefined roadmaps and rules.
+
+## Getting Started
+
+1.  **Clone this repository.**
+2.  **Install dependencies:** `pip install -r requirements.txt`
+3.  **Customize `roadmap.txt` and `rules.txt`:** Define your project's roadmap and rules in YAML format.
+4.  **Run the chatbot:** `python app.py`
+5.  **Access the chatbot:** Open the Gradio link provided in the console.
+
+## Deployment on Hugging Face Spaces
+
+1.  **Create a new Space on Hugging Face Spaces.** Choose Gradio or Streamlit as the SDK.
+2.  **Upload all project files** to your Space repository.
+3.  **Hugging Face Spaces will automatically install dependencies from `requirements.txt` and run `app.py` (or your chosen app script).**
+
+## Customization
+
+*   **Modify `roadmap.txt` and `rules.txt`** to fit your specific project.
+*   **Implement actual LLM integration** in `guidance_system.py` and `app.py`.
+*   **Develop code generation logic** in `code_generator.py`.
+*   **Enhance the user interface** in `app.py`.
+*   **Add more sophisticated parsing and guidance logic** as needed.
+
+## File Structure
+
+*   `/data`:  Intended for storing project datasets (e.g., for fine-tuning). Currently a placeholder.
+*   `/models`: Intended for storing downloaded or trained models. Currently a placeholder.
+*   `/scripts`: Contains Python scripts for core chatbot logic (parsing, guidance, code generation, error handling).
+*   `/api`: Intended for API related files if you integrate with external services. Currently a placeholder.
+*   `/configs`: Contains configuration files like `config.yaml`.
+*   `app.py`: The main application script using Gradio for the user interface.
+*   `requirements.txt`: Lists Python dependencies.
+*   `roadmap.txt`: Defines the project roadmap in YAML format.
+*   `rules.txt`: Defines project rules and best practices in YAML format.
+
+## Example Roadmap and Rules
+
+See `roadmap.txt` and `rules.txt` for example content.
+
+## License
+
+[Your License] (e.g., MIT License)

.history/README_20250202065728.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.
+* **LLM Selection:** Integrates with Hugging Face Hub for flexible LLM selection.
+* **Deployable on Hugging Face Spaces:** Built using Gradio for easy deployment.
+
+**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`** (optional).
+5. **Run the Gradio app:** `python app.py`
+6. **Deploy to Hugging Face Spaces** (refer to Hugging Face Spaces documentation).
+
+**Further Development:**
+
+* [List potential improvements and features here, as discussed earlier]
+
+**License:** [Your License]

.history/README_20250202071804.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.
+* **Error Handling:** Includes error handling for model loading and switching, with UI warnings.
+* **Deployable on Hugging Face Spaces:** Built using Gradio for easy deployment.
+
+**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 and chatbot behavior.
+5. **Run the Gradio app:** `python app.py`
+6. **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.
+* Add more LLMs to the selection pool.
+* Implement more robust error handling and logging.
+
+**License:** [Your License]

.history/README_20250202072652.md

@@ -0,0 +1,61 @@
+---
+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`.
+* **Error Handling:** Includes error handling for model loading and switching, with UI warnings.
+* **Deployable on Hugging Face Spaces:** Built using Gradio for easy deployment.
+
+**Important Notes on Safety Settings:**
+
+* **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.
+
+**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. **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.
+* 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.**
+
+**License:** [Your License]

.history/app_20250202070123.py

@@ -0,0 +1,28 @@
+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
+
+with gr.Blocks() as demo:
+    chatbot_greeting = chatbot.get_chatbot_greeting()
+    gr.Markdown(f"# {chatbot.chatbot_config.get('name', 'Project Guidance Chatbot')}")
+    gr.Markdown(chatbot_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_20250202071439.py

@@ -0,0 +1,42 @@
+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_message = chatbot.switch_llm_model(model_key)
+    greeting_message = chatbot.get_chatbot_greeting()
+    return greeting_message # Update greeting to reflect new model
+
+with gr.Blocks() as demo:
+    chatbot_greeting_md = gr.Markdown(chatbot.get_chatbot_greeting()) # Markdown for greeting - to update dynamically
+    gr.Markdown(f"# {chatbot.chatbot_config.get('name', 'Project Guidance Chatbot')}")
+
+    # Model selection dropdown
+    model_choices = [(model['name'], key) for key, model in chatbot.available_models_config.items()] # (Display Name, Key)
+    model_dropdown = gr.Dropdown(
+        choices=model_choices,
+        value=chatbot.active_model_info['name'] if chatbot.active_model_info else None, # Set initial value to current model name
+        label="Select LLM Model"
+    )
+    model_dropdown.change(fn=switch_model, inputs=model_dropdown, outputs=chatbot_greeting_md) # Update greeting on model change
+
+
+    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_20250202072114.py

@@ -0,0 +1,49 @@
+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_20250202072637.py

@@ -0,0 +1,49 @@
+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_20250202072640.py

@@ -0,0 +1,49 @@
+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/configs/chatbot_config_20250202071313.yaml

@@ -0,0 +1,23 @@
+chatbot:
+  name: "Project Guidance Chatbot"
+  description: "Your helpful AI assistant for project completion with LLM selection."
+  default_llm_model_id: "deepseek-r1-distill-llama-8b" # Default model ID (using keys from 'available_models')
+
+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_20250202071856.yaml

@@ -0,0 +1,23 @@
+chatbot:
+  name: "Project Guidance Chatbot"
+  description: "Your helpful AI assistant for project completion with LLM selection."
+  default_llm_model_id: "deepseek-r1-distill-llama-8b" # Default model ID (using keys from 'available_models')
+
+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_20250202072554.yaml

@@ -0,0 +1,24 @@
+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:
+    name: "Gemini 2.0 Flash (Exp 01-21)"
+    model_id: "google/gemini-2.0-flash-thinking-exp-01-21"
+
+model_selection:
+  suggested_models:
+    - "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/details_20250202065715.txt

@@ -0,0 +1,21 @@
+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)

.history/requirements_20250202065234.txt

@@ -0,0 +1,3 @@
+# requirements.txt
+gradio
+PyYAML

.history/requirements_20250202065734.txt

@@ -0,0 +1,4 @@
+gradio
+PyYAML
+transformers  # For interacting with Hugging Face models (if needed directly)
+# Add other dependencies as needed, e.g., for specific LLMs or utilities

.history/requirements_20250202071821.txt

@@ -0,0 +1,4 @@
+gradio
+PyYAML
+transformers  # For interacting with Hugging Face models (if needed directly)
+torch

.history/requirements_20250202071824.txt

@@ -0,0 +1,4 @@
+gradio
+PyYAML
+transformers  # For interacting with Hugging Face models (if needed directly)
+torch

.history/scripts/chatbot_logic_20250202070001.py

@@ -0,0 +1,132 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+
+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.current_phase = None # Track current phase (can be improved with state management)
+
+    def get_chatbot_greeting(self):
+        return f"Hello! I am the {self.chatbot_config.get('name', 'Project Guidance Chatbot')}. {self.chatbot_config.get('description', 'How can I help you with your project?')}"
+
+    def process_query(self, user_query):
+        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 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
+
+        # Basic phase detection based on keywords (improve with more sophisticated NLP)
+        for phase_key, phase_data in self.phases.items():
+            if phase_data['name'].lower() in query_lower:
+                self.current_phase = phase_key # Set current phase if detected
+                return self.get_phase_introduction(phase_data)
+
+        return self.response_config.get('default_instruction', "I can guide you through project phases. Ask me about a specific phase or project aspect.")
+
+    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") # Example template file naming convention
+                if os.path.exists(template_filepath):
+                    code_snippet = self.generate_code_snippet(template_filepath, phase_data) # Pass phase data for potential customization
+                    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."
+
+        # General guidance based on phase description (can be enhanced with LLM)
+        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()
+
+            # Simple placeholder replacement - enhance with more sophisticated templating
+            code_snippet = template_content.replace("{{phase_name}}", phase_data['name'])
+            # Add more dynamic replacements based on phase_data or other context if needed
+
+            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_20250202071411.py

@@ -0,0 +1,150 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+# Placeholder import for LLM interaction - replace with actual LLM library when integrating
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+
+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.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
+
+
+    def load_llm_model(self, model_info):
+        """Loads the LLM model and tokenizer based on model_info (placeholder)."""
+        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})... (Placeholder - replace with actual LLM loading)")
+        # --- Placeholder for actual LLM loading using transformers (example) ---
+        # try:
+        #     self.llm_tokenizer = AutoTokenizer.from_pretrained(model_id)
+        #     self.llm_model = AutoModelForCausalLM.from_pretrained(model_id) # Or appropriate model class
+        #     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
+        # --- End Placeholder ---
+        self.active_model_info = model_info # Update active 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 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}**. {self.chatbot_config.get('description', 'How can I help you with your project?')}"
+
+    def process_query(self, user_query):
+        # ... (rest of the process_query function remains largely the same for roadmap/rules logic) ...
+
+        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: # Example command for switching model via text input
+            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) # Switch by key
+                        return switch_result + "\n" + self.get_chatbot_greeting() # Re-greet with new model info
+                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
+
+        # Basic phase detection based on keywords
+        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)
+
+        # --- Placeholder for LLM-based response generation ---
+        # If no specific roadmap/rule logic applies, you could use the LLM here
+        # llm_response = self.generate_llm_response(user_query) # Function to interact with LLM
+        # 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.")
+
+    # --- Placeholder for LLM response generation function ---
+    # def generate_llm_response(self, user_query):
+    #     """Generates a response using the currently active LLM (placeholder)."""
+    #     if not self.llm_model or not self.llm_tokenizer:
+    #         return "LLM model not loaded."
+    #     try:
+    #         inputs = self.llm_tokenizer(user_query, return_tensors="pt") # Tokenize
+    #         outputs = self.llm_model.generate(**inputs) # Generate response
+    #         response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True) # Decode
+    #         return response
+    #     except Exception as e:
+    #         return f"Error generating LLM response: {e}"
+
+
+    # ... (rest of the chatbot logic functions like get_roadmap_summary, get_rules_summary, get_phase_guidance, generate_code_snippet remain largely the same) ...
+
+
+# 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_20250202072003.py

@@ -0,0 +1,209 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer  # Import necessary classes
+
+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.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
+
+
+    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 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}**. {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) # Tokenize and move to model's device
+            outputs = self.llm_model.generate(**inputs, max_length=200, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) # Example generation parameters
+            response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True) # Decode
+            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):
+        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: # Example command for switching model via text input
+            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) # Switch by key
+                        return switch_result + "\n" + self.get_chatbot_greeting() # Re-greet with new model info
+                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
+
+        # Basic phase detection based on keywords
+        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)
+
+        # --- After roadmap/rules logic, try LLM response if no specific guidance found ---
+        llm_response = self.generate_llm_response(user_query)
+        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.")
+
+    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") # Example template file naming convention
+                if os.path.exists(template_filepath):
+                    code_snippet = self.generate_code_snippet(template_filepath, phase_data) # Pass phase data for potential customization
+                    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."
+
+        # General guidance based on phase description (can be enhanced with LLM)
+        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()
+
+            # Simple placeholder replacement - enhance with more sophisticated templating
+            code_snippet = template_content.replace("{{phase_name}}", phase_data['name'])
+            # Add more dynamic replacements based on phase_data or other context if needed
+
+            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_20250202072623.py

@@ -0,0 +1,203 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer  # Import necessary classes
+
+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.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)
+
+
+    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 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):
+        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', "I can guide you through project phases. Ask me about a specific phase or project aspect.")
+
+    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_20250202072626.py

@@ -0,0 +1,203 @@
+from scripts.parsing_utils import load_yaml_file, get_roadmap_phases, get_project_rules
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer  # Import necessary classes
+
+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.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)
+
+
+    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 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):
+        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', "I can guide you through project phases. Ask me about a specific phase or project aspect.")
+
+    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_20250202070107.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/api_template.py_20250202072103.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_20250202070058.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/evaluation_template.py_20250202072054.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_20250202070023.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/preprocessing_template.py_20250202072023.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_20250202070043.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/code_templates/training_template.py_20250202072034.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/parse_20250202061443.py

@@ -0,0 +1 @@
+# File reading and parsing functions for roadmap.txt and rules.txt

.history/scripts/parsing_utils_20250202065942.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

.history/scripts/parsing_utils_20250202071904.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

.history/scripts/parsing_utils_20250202071913.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

.history/scripts/parsing_utils_20250202071948.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

@@ -9,5 +9,53 @@ app_file: app.py
 pinned: false
 short_description: STTETTETE
 ---
+# Custom AI Chatbot for Project Guidance
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+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`.
+* **Error Handling:** Includes error handling for model loading and switching, with UI warnings.
+* **Deployable on Hugging Face Spaces:** Built using Gradio for easy deployment.
+
+**Important Notes on Safety Settings:**
+
+* **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.
+
+**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. **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.
+* 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.**
+
+**License:** [Your License]

app.py

@@ -1,36 +1,49 @@
-# /app.py
-
-from fastapi import FastAPI, HTTPException
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from pydantic import BaseModel
-import os
-import logging
-
-# Setup logging
-logging.basicConfig(level=logging.INFO)
-
-# FastAPI instance
-app = FastAPI()
-
-# Load the model and tokenizer
-MODEL_NAME = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-7B'
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
-
-class UserInput(BaseModel):
-    input_text: str
-
-@app.post("/predict")
-def predict(input: UserInput):
-    try:
-        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}
-    except Exception as e:
-        logging.error(f"Error during prediction: {e}")
-        raise HTTPException(status_code=500, detail="Error generating response")
-
-@app.get("/")
-def read_root():
-    return {"message": "Welcome to the Custom AI Chatbot"}
+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()

configs/chatbot_config.yaml

@@ -0,0 +1,24 @@
+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:
+    name: "Gemini 2.0 Flash (Exp 01-21)"
+    model_id: "google/gemini-2.0-flash-thinking-exp-01-21"
+
+model_selection:
+  suggested_models:
+    - "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?"

configs/requirments.txt

@@ -1,8 +0,0 @@
-# /configs/requirements.txt
-
-fastapi
-uvicorn
-transformers
-torch
-datasets
-pydantic

details.txt

@@ -0,0 +1,21 @@
+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)