Remove historical requirement files and chatbot configuration scripts

.history/.gitattributes_20250202080908

@@ -1,35 +0,0 @@
-*.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

@@ -1,36 +0,0 @@
-*.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

@@ -1,57 +0,0 @@
-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

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

.history/configs/chatbot_config_20250202080908.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: # 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_20250202081215.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: # Using a shorter key for easier referencing in code
+    name: "Gemini 2.0 Flash (Exp 01-21)"
+    model_id: "google/gemini-2.0-flash-thinking-exp-01-21"
+
+model_selection:
+  suggested_models: # (Keep suggested models - might be useful later)
+    - "mistralai/Mistral-7B-Instruct-v0.2"
+    - "google/flan-t5-xl"
+    - "facebook/bart-large"
+  criteria_prompt: "Consider these criteria when selecting a model: {rules.model_selection}"
+
+response_generation:
+  error_message: "Sorry, I encountered an issue. Please check your input and project files."
+  default_instruction: "How can I help you with your project?"

.history/requirements_20250202080908.txt

@@ -1,4 +0,0 @@
-gradio
-PyYAML
-transformers
-torch

.history/requirements_20250202081148.txt

@@ -1,5 +0,0 @@
-gradio
-PyYAML
-transformers
-torch
-accelerate>=0.26.0

.history/requirements_20250202081149.txt

@@ -1,5 +0,0 @@
-gradio
-PyYAML
-transformers
-torch
-accelerate>=0.26.0

.history/requirements_20250202081150.txt

@@ -1,5 +0,0 @@
-gradio
-PyYAML
-transformers
-torch
-accelerate>=0.26.0

.history/{requirements_20250202081153.txt → requirements_20250202083728.txt}

@@ -2,4 +2,5 @@ gradio
 PyYAML
 transformers
 torch
-accelerate
+accelerate
+bitsandbytes

.history/rules_20250202080908.yaml

@@ -1,78 +0,0 @@
-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

@@ -1,78 +0,0 @@
-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

@@ -1,78 +0,0 @@
-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_20250202080928.py → chatbot_logic_20250202083642.py}

@@ -1,11 +1,10 @@
 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
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig  # Import BitsAndBytesConfig
+import yaml
+import logging
 
-# Set up logging
-logging.basicConfig(level=logging.ERROR,  # Set default logging level to ERROR
+logging.basicConfig(level=logging.ERROR,
                     format='%(asctime)s - %(levelname)s - %(message)s')
 
 class ProjectGuidanceChatbot:
@@ -28,22 +27,20 @@ class ProjectGuidanceChatbot:
         self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
 
         self.current_phase = None
-        self.active_model_key = self.chatbot_config.get('default_llm_model_id') # Get default model key
-        self.active_model_info = self.available_models_config.get(self.active_model_key) # Get model info from config
+        self.active_model_key = self.chatbot_config.get('default_llm_model_id')
+        self.active_model_info = self.available_models_config.get(self.active_model_key)
 
-        # 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
+        self.llm_model = None
+        self.llm_tokenizer = None
+        self.load_llm_model(self.active_model_info)
 
+        self.update_mode_active = False
 
     def load_llm_model(self, model_info):
-        """Loads the LLM model and tokenizer based on model_info."""
+        """Loads the LLM model and tokenizer based on model_info with 4-bit quantization."""
         if not model_info:
             error_message = "Error: Model information not provided."
-            logging.error(error_message) # Log the error
+            logging.error(error_message)
             self.llm_model = None
             self.llm_tokenizer = None
             return
@@ -52,19 +49,28 @@ class ProjectGuidanceChatbot:
         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
+            logging.error(error_message)
             self.llm_model = None
             self.llm_tokenizer = None
             return
 
-        print(f"Loading model: {model_name} ({model_id})...")
+        print(f"Loading model: {model_name} ({model_id}) with 4-bit quantization...") # Indicate quantization
         try:
+            bnb_config = BitsAndBytesConfig(  # Configure 4-bit quantization
+                load_in_4bit=True,
+                bnb_4bit_quant_type="nf4",  # "nf4" is recommended for Llama models
+                bnb_4bit_compute_dtype=torch.bfloat16,  # Or torch.float16 if bfloat16 not supported
+            )
             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.")
+            self.llm_model = AutoModelForCausalLM.from_pretrained(
+                model_id,
+                device_map="auto",
+                quantization_config=bnb_config  # Apply quantization config
+            )
+            print(f"Model {model_name} loaded successfully with 4-bit quantization.") # Indicate quantization success
         except Exception as e:
-            error_message = f"Error loading model {model_name} ({model_id}): {e}"
-            logging.exception(error_message) # Log exception with traceback
+            error_message = f"Error loading model {model_name} ({model_id}) with 4-bit quantization: {e}"
+            logging.exception(error_message)
             self.llm_model = None
             self.llm_tokenizer = None
         self.active_model_info = model_info
@@ -79,8 +85,8 @@ class ProjectGuidanceChatbot:
             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
+            logging.error(error_message)
+            return error_message
 
     def enter_update_mode(self):
         """Enters the chatbot's update mode."""
@@ -110,28 +116,28 @@ class ProjectGuidanceChatbot:
             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
+            logging.exception(error_message)
+            print(error_message)
 
     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?')}"
+        return f"Hello! I am the {self.chatbot_config.get('name', 'Project Guidance Chatbot')}. Currently using **{current_model_name}** (4-bit quantized).  Max response tokens: {self.max_response_tokens}. {self.chatbot_config.get('description', 'How can I help you with your project?')}" # Indicate quantization in greeting
 
     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
+            logging.error(error_message)
+            return error_message
         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
+            outputs = self.llm_model.generate(**inputs, max_length=self.max_response_tokens, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
             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
+            logging.exception(error_message)
+            return self.response_config.get('error_message', "Sorry, something went wrong.")
 
     def process_query(self, user_query):
         secret_command_prefix = "sagor is python/"
@@ -145,8 +151,6 @@ class ProjectGuidanceChatbot:
             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:
@@ -157,19 +161,16 @@ class ProjectGuidanceChatbot:
                         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
+                elif parts and parts[0] == "add_phase":
+                    return "Command 'add_phase' is not yet implemented in this example."
                 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:
@@ -191,7 +192,6 @@ class ProjectGuidanceChatbot:
                         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:
@@ -221,34 +221,32 @@ class ProjectGuidanceChatbot:
         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
+            logging.error(error_message)
+            return error_message
         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
+            logging.error(error_message)
+            return error_message
         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
+            logging.error(error_message)
+            return error_message
 
-        self.rules_data['project_rules'][category][rule_key] = new_rule_text # Update rule in memory
+        self.rules_data['project_rules'][category][rule_key] = new_rule_text
 
         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
+                yaml.dump(self.rules_data, f, indent=2)
+            self.reload_config()
             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
-
+            logging.exception(error_message)
+            return error_message
 
     def get_roadmap_summary(self):
         summary = "Project Roadmap:\n"
@@ -293,7 +291,6 @@ class ProjectGuidanceChatbot:
 
         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:
@@ -307,7 +304,6 @@ class ProjectGuidanceChatbot:
         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(

requirements.txt

@@ -2,4 +2,5 @@ gradio
 PyYAML
 transformers
 torch
-accelerate
+accelerate
+bitsandbytes

scripts/chatbot_logic.py

@@ -1,11 +1,10 @@
 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
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig  # Import BitsAndBytesConfig
+import yaml
+import logging
 
-# Set up logging
-logging.basicConfig(level=logging.ERROR,  # Set default logging level to ERROR
+logging.basicConfig(level=logging.ERROR,
                     format='%(asctime)s - %(levelname)s - %(message)s')
 
 class ProjectGuidanceChatbot:
@@ -28,22 +27,20 @@ class ProjectGuidanceChatbot:
         self.max_response_tokens = self.chatbot_config.get('max_response_tokens', 200)
 
         self.current_phase = None
-        self.active_model_key = self.chatbot_config.get('default_llm_model_id') # Get default model key
-        self.active_model_info = self.available_models_config.get(self.active_model_key) # Get model info from config
+        self.active_model_key = self.chatbot_config.get('default_llm_model_id')
+        self.active_model_info = self.available_models_config.get(self.active_model_key)
 
-        # 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
+        self.llm_model = None
+        self.llm_tokenizer = None
+        self.load_llm_model(self.active_model_info)
 
+        self.update_mode_active = False
 
     def load_llm_model(self, model_info):
-        """Loads the LLM model and tokenizer based on model_info."""
+        """Loads the LLM model and tokenizer based on model_info with 4-bit quantization."""
         if not model_info:
             error_message = "Error: Model information not provided."
-            logging.error(error_message) # Log the error
+            logging.error(error_message)
             self.llm_model = None
             self.llm_tokenizer = None
             return
@@ -52,19 +49,28 @@ class ProjectGuidanceChatbot:
         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
+            logging.error(error_message)
             self.llm_model = None
             self.llm_tokenizer = None
             return
 
-        print(f"Loading model: {model_name} ({model_id})...")
+        print(f"Loading model: {model_name} ({model_id}) with 4-bit quantization...") # Indicate quantization
         try:
+            bnb_config = BitsAndBytesConfig(  # Configure 4-bit quantization
+                load_in_4bit=True,
+                bnb_4bit_quant_type="nf4",  # "nf4" is recommended for Llama models
+                bnb_4bit_compute_dtype=torch.bfloat16,  # Or torch.float16 if bfloat16 not supported
+            )
             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.")
+            self.llm_model = AutoModelForCausalLM.from_pretrained(
+                model_id,
+                device_map="auto",
+                quantization_config=bnb_config  # Apply quantization config
+            )
+            print(f"Model {model_name} loaded successfully with 4-bit quantization.") # Indicate quantization success
         except Exception as e:
-            error_message = f"Error loading model {model_name} ({model_id}): {e}"
-            logging.exception(error_message) # Log exception with traceback
+            error_message = f"Error loading model {model_name} ({model_id}) with 4-bit quantization: {e}"
+            logging.exception(error_message)
             self.llm_model = None
             self.llm_tokenizer = None
         self.active_model_info = model_info
@@ -79,8 +85,8 @@ class ProjectGuidanceChatbot:
             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
+            logging.error(error_message)
+            return error_message
 
     def enter_update_mode(self):
         """Enters the chatbot's update mode."""
@@ -110,28 +116,28 @@ class ProjectGuidanceChatbot:
             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
+            logging.exception(error_message)
+            print(error_message)
 
     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?')}"
+        return f"Hello! I am the {self.chatbot_config.get('name', 'Project Guidance Chatbot')}. Currently using **{current_model_name}** (4-bit quantized).  Max response tokens: {self.max_response_tokens}. {self.chatbot_config.get('description', 'How can I help you with your project?')}" # Indicate quantization in greeting
 
     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
+            logging.error(error_message)
+            return error_message
         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
+            outputs = self.llm_model.generate(**inputs, max_length=self.max_response_tokens, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
             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
+            logging.exception(error_message)
+            return self.response_config.get('error_message', "Sorry, something went wrong.")
 
     def process_query(self, user_query):
         secret_command_prefix = "sagor is python/"
@@ -145,8 +151,6 @@ class ProjectGuidanceChatbot:
             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:
@@ -157,19 +161,16 @@ class ProjectGuidanceChatbot:
                         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
+                elif parts and parts[0] == "add_phase":
+                    return "Command 'add_phase' is not yet implemented in this example."
                 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:
@@ -191,7 +192,6 @@ class ProjectGuidanceChatbot:
                         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:
@@ -221,34 +221,32 @@ class ProjectGuidanceChatbot:
         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
+            logging.error(error_message)
+            return error_message
         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
+            logging.error(error_message)
+            return error_message
         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
+            logging.error(error_message)
+            return error_message
 
-        self.rules_data['project_rules'][category][rule_key] = new_rule_text # Update rule in memory
+        self.rules_data['project_rules'][category][rule_key] = new_rule_text
 
         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
+                yaml.dump(self.rules_data, f, indent=2)
+            self.reload_config()
             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
-
+            logging.exception(error_message)
+            return error_message
 
     def get_roadmap_summary(self):
         summary = "Project Roadmap:\n"
@@ -293,7 +291,6 @@ class ProjectGuidanceChatbot:
 
         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:
@@ -307,7 +304,6 @@ class ProjectGuidanceChatbot:
         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(