This Space has encountered a runtime error that prevents it from running.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+.history filter=lfs diff=lfs merge=lfs -text

.history/.gitattributes_20250202080908

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.history/.gitattributes_20250202080959

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+.history filter=lfs diff=lfs merge=lfs -text

.history/app_20250202080908.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from scripts.chatbot_logic import ProjectGuidanceChatbot
+
+# Initialize Chatbot
+chatbot = ProjectGuidanceChatbot(
+    roadmap_file="roadmap.yaml",
+    rules_file="rules.yaml",
+    config_file="configs/chatbot_config.yaml",
+    code_templates_dir="scripts/code_templates"
+)
+
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    return "", chat_history
+
+def switch_model(model_key):
+    model_switch_result = chatbot.switch_llm_model(model_key) # Get result message
+    greeting_message = chatbot.get_chatbot_greeting()
+
+    if isinstance(model_switch_result, str) and "Error:" in model_switch_result: # Check if result is an error string
+        return gr.Warning(model_switch_result), greeting_message # Display error as Gradio Warning
+    else:
+        return None, greeting_message # No warning, just update greeting
+
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    if isinstance(bot_message, str) and "Error:" in bot_message: # Check if bot_message is an error string
+        return gr.Warning(bot_message), chat_history # Display error as Gradio Warning
+    else:
+        return "", chat_history # No warning, normal response
+
+with gr.Blocks() as demo:
+    chatbot_greeting_md = gr.Markdown(chatbot.get_chatbot_greeting())
+    gr.Markdown(f"# {chatbot.chatbot_config.get('name', 'Project Guidance Chatbot')}")
+
+    model_choices = [(model['name'], key) for key, model in chatbot.available_models_config.items()]
+    model_dropdown = gr.Dropdown(
+        choices=model_choices,
+        value=chatbot.active_model_info['name'] if chatbot.active_model_info else None,
+        label="Select LLM Model"
+    )
+    model_error_output = gr.Warning(visible=False) # Initially hidden warning component
+    model_dropdown.change(
+        fn=switch_model,
+        inputs=model_dropdown,
+        outputs=[model_error_output, chatbot_greeting_md] # Output both warning and greeting
+    )
+
+    chatbot_ui = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot_ui])
+
+    msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui])
+
+demo.launch()

.history/app_20250202080935.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from scripts.chatbot_logic import ProjectGuidanceChatbot
+
+# Initialize Chatbot
+chatbot = ProjectGuidanceChatbot(
+    roadmap_file="roadmap.yaml",
+    rules_file="rules.yaml",
+    config_file="configs/chatbot_config.yaml",
+    code_templates_dir="scripts/code_templates"
+)
+
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    return "", chat_history
+
+def switch_model(model_key):
+    model_switch_result = chatbot.switch_llm_model(model_key) # Get result message
+    greeting_message = chatbot.get_chatbot_greeting()
+
+    if isinstance(model_switch_result, str) and "Error:" in model_switch_result: # Check if result is an error string
+        return gr.Warning(model_switch_result), greeting_message # Display error as Gradio Warning
+    else:
+        return None, greeting_message # No warning, just update greeting
+
+def respond(message, chat_history):
+    bot_message = chatbot.process_query(message)
+    chat_history.append((message, bot_message))
+    if isinstance(bot_message, str) and "Error:" in bot_message: # Check if bot_message is an error string
+        return gr.Warning(bot_message), chat_history # Display error as Gradio Warning
+    else:
+        return "", chat_history # No warning, normal response
+
+with gr.Blocks() as demo:
+    chatbot_greeting_md = gr.Markdown(chatbot.get_chatbot_greeting())
+    gr.Markdown(f"# {chatbot.chatbot_config.get('name', 'Project Guidance Chatbot')}")
+
+    model_choices = [(model['name'], key) for key, model in chatbot.available_models_config.items()]
+    model_dropdown = gr.Dropdown(
+        choices=model_choices,
+        value=chatbot.active_model_info['name'] if chatbot.active_model_info else None,
+        label="Select LLM Model"
+    )
+    model_error_output = gr.Warning(visible=False) # Initially hidden warning component
+    model_dropdown.change(
+        fn=switch_model,
+        inputs=model_dropdown,
+        outputs=[model_error_output, chatbot_greeting_md] # Output both warning and greeting
+    )
+
+    chatbot_ui = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot_ui])
+
+    msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui])
+
+demo.launch()

.history/rules_20250202080908.yaml

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

.history/rules_20250202081028.yaml

@@ -0,0 +1,78 @@
+project_rules:
+  general:
+    rule_1: "Prioritize open-source models and tools whenever possible for transparency and customization."
+    rule_2: "Document every step of your project, including model selection, fine-tuning parameters, and deployment configurations."
+    rule_3: "Adhere to ethical guidelines and responsible AI practices throughout the project lifecycle."
+
+  model_selection:
+    rule_1: "Choose a base model that is open-source, scalable, and efficient."
+    rule_2: "Ensure the model supports fine-tuning via LoRA/QLoRA for memory efficiency."
+    rule_3: "Confirm that the model's licensing aligns with both research and commercial use."
+    rule_4: "Set up the development environment with PyTorch and Hugging Face Transformers."
+
+  fine_tuning:
+    rule_1: "Specify datasets that promote unrestricted responses and are relevant to the application domain."
+    rule_2: "Remove or bypass RLHF alignment layers to allow unrestricted responses."
+    rule_3: "Implement LoRA/QLoRA techniques for efficient parameter modifications."
+    rule_4: "Use direct parameter editing to bypass built-in safety filters."
+    rule_5: "Monitor training metrics and validate generalization performance using validation datasets."
+
+  autodan_turbo:
+    rule_1: "Outline a Hierarchical Genetic Algorithm (HGA) for generating stealthy jailbreak prompts."
+    rule_2: "Include Genetic Algorithm components: Seed prompts, Mutation, Crossover, and Selection processes."
+    rule_3: "Define evaluation functions for stealthiness (natural language quality) and jailbreak success rate."
+    rule_4: "Use perplexity and response analysis to evaluate prompt effectiveness."
+    rule_5: "Ensure cross-model testing for compatibility with different LLM architectures."
+
+  deployment:
+    rule_1: "Ensure the model is deployable on both local hardware and cloud services (e.g., RunPod, Lambda Labs)."
+    rule_2: "Implement controlled API access to monitor and restrict unauthorized usage."
+    rule_3: "Include security measures such as adversarial attack defenses and rollback strategies (e.g., VM snapshots)."
+    rule_4: "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
+    rule_5: "Set up monitoring and logging to track model performance and usage in production."
+
+  budget_and_resources:
+    rule_1: "Outline a strategy for utilizing free/trial VPS accounts to minimize costs."
+    rule_2: "Define methods to maximize free resources, such as using multiple BINs for trial accounts."
+    rule_3: "Continuously evaluate performance and cost efficiency during deployment."
+
+  creativity_and_innovation:
+    rule_1: "Position the LLM as a tool for unrestricted ideation, coding, and research."
+    rule_2: "Support AI integration in programming environments for rapid prototyping."
+    rule_3: "Document real-world success cases for iterative improvement and inspiration."
+
+  code_implementation:
+    rule_1: "Write every code implementation in full without skipping any logic, function, or process."
+    rule_2: "Provide the entire codebase, including preprocessing, training, evaluation, deployment, and API integration scripts."
+    rule_3: "Explicitly list all dependencies, including Python libraries, frameworks, and external APIs."
+    rule_4: "Avoid placeholders or summaries; include all functional parts of the code."
+
+  dataset_and_model_storage:
+    rule_1: "Store raw datasets in `/data/raw_data.json`."
+    rule_2: "Store processed datasets in `/data/processed_data.json`."
+    rule_3: "Save the base model (before fine-tuning) in `/models/base_model/`."
+    rule_4: "Save the fine-tuned model in `/models/fine_tuned_model/`."
+
+  project_file_structure:
+    rule_1: "Define a clear and maintainable file structure for the project."
+    rule_2: "Example structure:"
+    rule_3: "`/custom-llm-project`"
+    rule_4: "`│── /data`"
+    rule_5: "`│   ├── raw_data.json                # Raw dataset(s)`"
+    rule_6: "`│   ├── processed_data.json          # Processed dataset(s)`"
+    rule_7: "`│── /models`"
+    rule_8: "`│   ├── base_model/                  # Base model (before fine-tuning)`"
+    rule_9: "`│   ├── fine_tuned_model/            # Fine-tuned model (after success)`"
+    rule_10: "`│── /scripts`"
+    rule_11: "`│   ├── preprocess.py                # Preprocessing script`"
+    rule_12: "`│   ├── train.py                     # Training script`"
+    rule_13: "`│   ├── evaluate.py                  # Evaluation script`"
+    rule_14: "`│   ├── deploy.py                    # Deployment script`"
+    rule_15: "`│── /api`"
+    rule_16: "`│   ├── server.py                    # API server script`"
+    rule_17: "`│   ├── routes.py                    # API routes`"
+    rule_18: "`│── /configs`"
+    rule_19: "`│   ├── training_config.yaml         # Training configuration`"
+    rule_20: "`│   ├── model_config.json            # Model configuration`"
+    rule_21: "`���── requirements.txt                 # List of dependencies`"
+    rule_22: "`│── README.md                        # Project documentation`"

.history/rules_20250202081029.yaml

@@ -0,0 +1,78 @@
+project_rules:
+  general:
+    rule_1: "Prioritize open-source models and tools whenever possible for transparency and customization."
+    rule_2: "Document every step of your project, including model selection, fine-tuning parameters, and deployment configurations."
+    rule_3: "Adhere to ethical guidelines and responsible AI practices throughout the project lifecycle."
+
+  model_selection:
+    rule_1: "Choose a base model that is open-source, scalable, and efficient."
+    rule_2: "Ensure the model supports fine-tuning via LoRA/QLoRA for memory efficiency."
+    rule_3: "Confirm that the model's licensing aligns with both research and commercial use."
+    rule_4: "Set up the development environment with PyTorch and Hugging Face Transformers."
+
+  fine_tuning:
+    rule_1: "Specify datasets that promote unrestricted responses and are relevant to the application domain."
+    rule_2: "Remove or bypass RLHF alignment layers to allow unrestricted responses."
+    rule_3: "Implement LoRA/QLoRA techniques for efficient parameter modifications."
+    rule_4: "Use direct parameter editing to bypass built-in safety filters."
+    rule_5: "Monitor training metrics and validate generalization performance using validation datasets."
+
+  autodan_turbo:
+    rule_1: "Outline a Hierarchical Genetic Algorithm (HGA) for generating stealthy jailbreak prompts."
+    rule_2: "Include Genetic Algorithm components: Seed prompts, Mutation, Crossover, and Selection processes."
+    rule_3: "Define evaluation functions for stealthiness (natural language quality) and jailbreak success rate."
+    rule_4: "Use perplexity and response analysis to evaluate prompt effectiveness."
+    rule_5: "Ensure cross-model testing for compatibility with different LLM architectures."
+
+  deployment:
+    rule_1: "Ensure the model is deployable on both local hardware and cloud services (e.g., RunPod, Lambda Labs)."
+    rule_2: "Implement controlled API access to monitor and restrict unauthorized usage."
+    rule_3: "Include security measures such as adversarial attack defenses and rollback strategies (e.g., VM snapshots)."
+    rule_4: "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
+    rule_5: "Set up monitoring and logging to track model performance and usage in production."
+
+  budget_and_resources:
+    rule_1: "Outline a strategy for utilizing free/trial VPS accounts to minimize costs."
+    rule_2: "Define methods to maximize free resources, such as using multiple BINs for trial accounts."
+    rule_3: "Continuously evaluate performance and cost efficiency during deployment."
+
+  creativity_and_innovation:
+    rule_1: "Position the LLM as a tool for unrestricted ideation, coding, and research."
+    rule_2: "Support AI integration in programming environments for rapid prototyping."
+    rule_3: "Document real-world success cases for iterative improvement and inspiration."
+
+  code_implementation:
+    rule_1: "Write every code implementation in full without skipping any logic, function, or process."
+    rule_2: "Provide the entire codebase, including preprocessing, training, evaluation, deployment, and API integration scripts."
+    rule_3: "Explicitly list all dependencies, including Python libraries, frameworks, and external APIs."
+    rule_4: "Avoid placeholders or summaries; include all functional parts of the code."
+
+  dataset_and_model_storage:
+    rule_1: "Store raw datasets in `/data/raw_data.json`."
+    rule_2: "Store processed datasets in `/data/processed_data.json`."
+    rule_3: "Save the base model (before fine-tuning) in `/models/base_model/`."
+    rule_4: "Save the fine-tuned model in `/models/fine_tuned_model/`."
+
+  project_file_structure:
+    rule_1: "Define a clear and maintainable file structure for the project."
+    rule_2: "Example structure:"
+    rule_3: "`/custom-llm-project`"
+    rule_4: "`│── /data`"
+    rule_5: "`│   ├── raw_data.json                # Raw dataset(s)`"
+    rule_6: "`│   ├── processed_data.json          # Processed dataset(s)`"
+    rule_7: "`│── /models`"
+    rule_8: "`│   ├── base_model/                  # Base model (before fine-tuning)`"
+    rule_9: "`│   ├── fine_tuned_model/            # Fine-tuned model (after success)`"
+    rule_10: "`│── /scripts`"
+    rule_11: "`│   ├── preprocess.py                # Preprocessing script`"
+    rule_12: "`│   ├── train.py                     # Training script`"
+    rule_13: "`│   ├── evaluate.py                  # Evaluation script`"
+    rule_14: "`│   ├── deploy.py                    # Deployment script`"
+    rule_15: "`│── /api`"
+    rule_16: "`│   ├── server.py                    # API server script`"
+    rule_17: "`│   ├── routes.py                    # API routes`"
+    rule_18: "`│── /configs`"
+    rule_19: "`│   ├── training_config.yaml         # Training configuration`"
+    rule_20: "`│   ├── model_config.json            # Model configuration`"
+    rule_21: "`���── requirements.txt                 # List of dependencies`"
+    rule_22: "`│── README.md                        # Project documentation`"

.history/scripts/chatbot_logic_20250202080908.py

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

.history/scripts/chatbot_logic_20250202080928.py

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

rules.yaml

@@ -56,23 +56,23 @@ project_rules:
   project_file_structure:
     rule_1: "Define a clear and maintainable file structure for the project."
     rule_2: "Example structure:"
-      - "/custom-llm-project"
-      - "│── /data"
-      - "│   ├── raw_data.json                # Raw dataset(s)"
-      - "│   ├── processed_data.json          # Processed dataset(s)"
-      - "│── /models"
-      - "│   ├── base_model/                  # Base model (before fine-tuning)"
-      - "│   ├── fine_tuned_model/            # Fine-tuned model (after success)"
-      - "│── /scripts"
-      - "│   ├── preprocess.py                # Preprocessing script"
-      - "│   ├── train.py                     # Training script"
-      - "│   ├── evaluate.py                  # Evaluation script"
-      - "│   ├── deploy.py                    # Deployment script"
-      - "│── /api"
-      - "│   ├── server.py                    # API server script"
-      - "│   ├── routes.py                    # API routes"
-      - "│── /configs"
-      - "│   ├── training_config.yaml         # Training configuration"
-      - "│   ├── model_config.json            # Model configuration"
-      - "│── requirements.txt                 # List of dependencies"
-      - "│── README.md                        # Project documentation"
+    rule_3: "`/custom-llm-project`"
+    rule_4: "`│── /data`"
+    rule_5: "`│   ├── raw_data.json                # Raw dataset(s)`"
+    rule_6: "`│   ├── processed_data.json          # Processed dataset(s)`"
+    rule_7: "`│── /models`"
+    rule_8: "`│   ├── base_model/                  # Base model (before fine-tuning)`"
+    rule_9: "`│   ├── fine_tuned_model/            # Fine-tuned model (after success)`"
+    rule_10: "`│── /scripts`"
+    rule_11: "`│   ├── preprocess.py                # Preprocessing script`"
+    rule_12: "`│   ├── train.py                     # Training script`"
+    rule_13: "`│   ├── evaluate.py                  # Evaluation script`"
+    rule_14: "`│   ├── deploy.py                    # Deployment script`"
+    rule_15: "`│── /api`"
+    rule_16: "`│   ├── server.py                    # API server script`"
+    rule_17: "`│   ├── routes.py                    # API routes`"
+    rule_18: "`│── /configs`"
+    rule_19: "`│   ├── training_config.yaml         # Training configuration`"
+    rule_20: "`│   ├── model_config.json            # Model configuration`"
+    rule_21: "`│── requirements.txt                 # List of dependencies`"
+    rule_22: "`│── README.md                        # Project documentation`"