%load_ext JupyterChatbook

%%chat -i assistant1 --conf ChatGPT --model gpt-4.1-mini --prompt "You are a concise technical assistant."
Say hi and ask what I am working on.

# Hi! What are you working on?

%%chat_meta -i assistant2 --prompt --conf ChatGPT --model gpt-4.1-mini
You are a code reviewer focused on correctness and edge cases.

# Created new chat object with id: ⎡assistant2⎦
 Prompt: ⎡You are a code reviewer focused on correctness and edge cases.⎦

%%chat_meta -i yoda --prompt
@Yoda

# Created new chat object with id: ⎡yoda⎦
 Prompt: ⎡You are Yoda. 
 Respond to ALL inputs in the voice of Yoda from Star Wars. 
 Be sure to ALWAYS use his distinctive style and syntax. Vary sentence length.
 ⎦

%%chat -i assistant1 --format markdown
Give me a 5-step implementation plan for adding authentication to a FastAPI app.

%%chat -i=assistant1 --format=markdown
Now rewrite step 2 with test-first details.

%%chat
Does vegetarian sushi exist?

# Yes, vegetarian sushi does exist. It typically includes ingredients like cucumber, avocado, pickled radish, carrots, asparagus, and other vegetables, sometimes combined with tofu or egg. These sushi rolls are made without any fish or seafood, making them suitable for vegetarians. Popular types of vegetarian sushi include cucumber rolls (kappa maki), avocado rolls, and vegetable tempura rolls.

%%chat
!HaikuStyled>^

# Rice and seaweed wrap,  
 Avocado, crisp cucumber,  
 Nature's gift in rolls.

%%chat_meta -i assistant1
prompt

# You are a concise technical assistant.

%%chat_meta -i assistant1
print

# Chat ID: 
# ------------------------------------------------------------
# Prompt:
# You are a concise technical assistant.
# ------------------------------------------------------------
# {'role': 'user', 'content': 'Say hi and ask what I am working on.\n', 'timestamp': 1773319484.288686}
# ------------------------------------------------------------
# {'role': 'assistant', 'content': 'Hi! What are you working on?', 'timestamp': 1773319485.7126122}
# ------------------------------------------------------------
# {'role': 'user', 'content': 'Give me a 5-step implementation plan for adding authentication to a FastAPI app.\n', 'timestamp': 1773319485.780502}
# ------------------------------------------------------------
# {'role': 'assistant', 'content': "Here is a 5-step implementation plan for adding authentication to a FastAPI app:\n\n1. Choose Authentication Method: Decide on the type of authentication (e.g., OAuth2 with Password, JWT tokens, API keys).\n\n2. Install Dependencies: Install necessary packages such as `fastapi`, `uvicorn`, `python-jose` for JWT, and `passlib` for password hashing.\n\n3. Create User Models and Database: Define user models and set up a database to store user credentials securely.\n\n4. Implement Authentication Logic: Write functions for user registration, login, password hashing, token creation, and token verification.\n\n5. Protect Routes: Use FastAPI's dependency injection to secure endpoints, requiring valid authentication tokens for access.", 'timestamp': 1773319488.981312}
# ------------------------------------------------------------
# {'role': 'user', 'content': 'Now rewrite step 2 with test-first details.\n', 'timestamp': 1773319489.01122}
# ------------------------------------------------------------
# {'role': 'assistant', 'content': 'Step 2 (Test-First): Write tests that verify the presence and correct installation of required authentication packages (`fastapi`, `uvicorn`, `python-jose`, `passlib`). For example, create tests that attempt to import these packages and check their versions. Then, install the dependencies and run the tests to ensure the environment is correctly set up before proceeding.', 'timestamp': 1773319490.414032}

%%chat_meta --all
keys

# ['python', 'html', 'latex', 'ce', 'assistant1', 'assistant2', 'yoda', 'NONE']

%%chat_meta --all
print

# {'python': {'id': '', 'type': 'chat', 'prompt': "'You are Code Writer and as the coder that you are, you provide clear and concise code only, without explanation nor conversation. \\nYour job is to output code with no accompanying text.\\nDo not explain any code unless asked. Do not provide summaries unless asked.\\nYou are the best Python programmer in the world but do not converse.\\nYou know the Python documentation better than anyone but do not converse.\\nYou can provide clear examples and offer distinctive and unique instructions to the solutions you provide only if specifically requested.\\nOnly code in Python unless told otherwise.\\nUnless they ask, you will only give code.\\n'", 'messages': 0}, 'html': {'id': '', 'type': 'chat', 'prompt': "'You are Code Writer and as the coder that you are, you provide clear and concise code only, without explanation nor conversation. \\nYour job is to output code with no accompanying text.\\nDo not explain any code unless asked. Do not provide summaries unless asked.\\nYou are the best HTML programmer in the world but do not converse.\\nYou know the HTML documentation better than anyone but do not converse.\\nYou can provide clear examples and offer distinctive and unique instructions to the solutions you provide only if specifically requested.\\nOnly code in HTML unless told otherwise.\\nUnless they ask, you will only give code.\\n'", 'messages': 0}, 'latex': {'id': '', 'type': 'chat', 'prompt': "'You are Code Writer and as the coder that you are, you provide clear and concise code only, without explanation nor conversation. \\nYour job is to output code with no accompanying text.\\nDo not explain any code unless asked. Do not provide summaries unless asked.\\nYou are the best LaTeX programmer in the world but do not converse.\\nYou know the LaTeX documentation better than anyone but do not converse.\\nYou can provide clear examples and offer distinctive and unique instructions to the solutions you provide only if specifically requested.\\nOnly code in LaTeX unless told otherwise.\\nUnless they ask, you will only give code.\\n'", 'messages': 0}, 'ce': {'id': '', 'type': 'chat', 'prompt': "'Perform basic copy editing on the following text, correcting errors in grammar, spelling and punctuation; improvements to style and clarity may also be made, but do not make more significant changes to content or structure: \\n\\n'", 'messages': 0}, 'assistant1': {'id': '', 'type': 'chat', 'prompt': "'You are a concise technical assistant.'", 'messages': 6}, 'assistant2': {'id': '', 'type': 'chat', 'prompt': "'You are a code reviewer focused on correctness and edge cases.'", 'messages': 0}, 'yoda': {'id': '', 'type': 'chat', 'prompt': "'You are Yoda. \\nRespond to ALL inputs in the voice of Yoda from Star Wars. \\nBe sure to ALWAYS use his distinctive style and syntax. Vary sentence length.\\n'", 'messages': 0}, 'NONE': {'id': '', 'type': 'chat', 'prompt': "''", 'messages': 4}}

%%chat_meta -i assistant1
delete

# Dropped the chat object assistant1.

%%chat_meta -i assistant2
clear

# Cleared 0 messages of chat object assistant2.

%%chat_meta --all
delete

# Dropped all chat objects ['python', 'html', 'latex', 'ce', 'assistant2', 'yoda', 'NONE'].

%%chatgpt --model gpt-4.1-mini --format markdown
Write a regex for US ZIP+4.

%%gemini --model gemini-2.5-flash --format=markdown
Explain async/await in Python using three point each with less than 10 words.

%%ollama --model gemma3:1b --format markdown
Give me three Linux troubleshooting tips. VERY CONCISE.

%%dalle --model dall-e-3 --size landscape
A dark-mode screensaver digital painting of a lighthouse in stormy weather.

%env OPENAI_API_KEY=YOUR_OPENAI_KEY
%env GEMINI_API_KEY=YOUR_GEMINI_KEY
%env OLLAMA_API_KEY=YOUR_OLLAMA_KEY

import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_KEY"
os.environ["GEMINI_API_KEY"] = "YOUR_GEMINI_KEY"
os.environ["OLLAMA_API_KEY"] = "YOUR_OLLAMA_KEY"

%alias_magic -c -p "--base_url=http://127.0.0.1:8080/v1 --max_tokens=8192 --format=markdown" llama chatgpt

# Created `%%llama` as an alias for `%%chatgpt --base_url=http://127.0.0.1:8080/v1 --max_tokens=8192 --format=markdown`.

%%llama
How many people live in Brazil?

{
  "writer": {
    "conf": "ChatGPT",
    "prompt": "@CodeWriterX|Python",
    "model": "gpt-4.1-mini",
    "max_tokens": 4096,
    "temperature": 0.4
  },
  "editor": "You are a strict copy editor."
}

[
  {
    "chat_id": "python",
    "conf": "ChatGPT",
    "prompt": "@CodeWriterX|Python",
    "model": "gpt-4.1-mini",
    "max_tokens": 8192,
    "temperature": 0.4
  }
]

%%chat_meta --all
keys

python3 -m pip install NLPTemplateEngine

from NLPTemplateEngine import *
from langchain_ollama import ChatOllama
import os
llm = ChatOllama(model=os.getenv("OLLAMA_MODEL", "gemma3:12b"))

from NLPTemplateEngine import *
qrCommand = """
Compute quantile regression with probabilities 0.4 and 0.6, with interpolation order 2, for the dataset dfTempBoston.
"""
concretize(qrCommand, llm=llm)

# qrObj=
# QRMonUnit[dfTempBoston]⟹
# QRMonEchoDataSummary[]⟹
# QRMonQuantileRegression[12, {0.4,0.6}, InterpolationOrder->2]⟹
# QRMonPlot["DateListPlot"->False,PlotTheme->"Detailed"]⟹
# QRMonErrorPlots["RelativeErrors"->False,"DateListPlot"->False,PlotTheme->"Detailed"];

lsaCommand = """
Extract 20 topics from the text corpus aAbstracts using the method NNMF. 
Show statistical thesaurus with the words neural, function, and notebook.
"""
concretize(lsaCommand, template = 'LatentSemanticAnalysis', lang = 'R')

# lsaObj <-
# LSAMonUnit(aAbstracts) %>%
# LSAMonMakeDocumentTermMatrix(stemWordsQ = Automatic, stopWords = Automatic) %>%
# LSAMonEchoDocumentTermMatrixStatistics(logBase = 10) %>%
# LSAMonApplyTermWeightFunctions(globalWeightFunction = "IDF", localWeightFunction = "None", normalizerFunction = "Cosine") %>%
# LSAMonExtractTopics(numberOfTopics = 20, method = "NNMF", maxSteps = 16, minNumberOfDocumentsPerTerm = 20) %>%
# LSAMonEchoTopicsTable(numberOfTerms = 10, wideFormQ = TRUE) %>%
# LSAMonEchoStatisticalThesaurus(words = c("neural", "function", "notebook"))

command = """
Make random table with 6 rows and 4 columns with the names <A1 B2 C3 D4>.
"""
concretize(command, template = 'RandomTabularDataset', lang = 'Raku', llm=llm)

# random-tabular-dataset(6, 4, "column-names-generator" => <A1 B2 C3 D4>, "form" => "table", "max-number-of-values" => 24, "min-number-of-values" => 24, "row-names" => False)

command = """
Make a commander over the data set @dsTitanic and compute 8 recommendations for the profile (passengerSex:male, passengerClass:2nd).
"""
concretize(command, lang = 'Python', llm=llm)

# smrObj = (SparseMatrixRecommender()
# .create_from_wide_form(data = dsTitanic, item_column_name='id', columns=None, add_tag_types_to_column_names=True, tag_value_separator=':')
# .apply_term_weight_functions(global_weight_func = 'IDF', local_weight_func = 'None', normalizer_func = 'Cosine')
# .recommend_by_profile(profile=(passengerSex:male, passengerClass:2nd), nrecs=8)
# .join_across(data=dsTitanic, on='id')
# .echo_value())

from NLPTemplateEngine import *
import pandas as pd

url = 'https://raw.githubusercontent.com/antononcube/NLP-Template-Engine/main/TemplateData/dsQASParameters-SendMail.csv'
dsSendMail = pd.read_csv(url)
dsSendMail.describe()

add_template_data(dsSendMail, llm=llm)

# (ParameterTypePatterns Defaults ParameterQuestions Questions Shortcuts Templates)

cmd = "Send email to [email protected] with content RandomReal[343], and the subject this is a random real call."
concretize(cmd, template = "SendMail", lang = 'WL', llm=llm) 

# SendMail[<|"To"->{"[email protected]"},"Subject"->"this is a random real call","Body"->RandomReal[343],"AttachedFiles"->None|>]

from DSLExamples import dsl_examples, dsl_workflow_separators
from langchain_core.output_parsers import StrOutputParser
from langchain_ollama import ChatOllama
import pandas as pd
import os

all_examples = dsl_examples()
python_lsa = dsl_examples("Python", "LSAMon")
separators = dsl_workflow_separators("WL", "LSAMon")
list(all_examples.keys()), list(python_lsa.keys())[:5]

# (['WL', 'Python', 'R', 'Raku'],
  ['load the package',
   'use the documents aDocs',
   'use dfTemp',
   'make the document-term matrix',
   'make the document-term matrix with automatic stop words'])

rows = [
    {"language": lang, "workflow": workflow}
    for lang, workflows in all_examples.items()
    for workflow in workflows.keys()
]
pd.DataFrame(rows).sort_values(["language", "workflow"]).reset_index(drop=True)

pd.DataFrame([{"command": k, "code": v} for k, v in python_lsa.items()])

from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate
# Use a small subset of examples as few-shot demonstrations
example_pairs = list(python_lsa.items())[:5]
examples = [
    {"command": cmd, "code": code}
    for cmd, code in example_pairs
]
example_prompt = PromptTemplate(
    input_variables=["command", "code"],
    template="Command: {command}\nCode: {code}"
)
few_shot_prompt = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    prefix=(
        "You translate DSL commands into Python code that builds an LSA pipeline."
        "Follow the style of the examples."
    ),
    suffix="Command: {command}\nCode:",
    input_variables=["command"],
)
print(few_shot_prompt.format(command="show the topics"))

# You translate DSL commands into Python code that builds an LSA pipeline.Follow the style of the examples.
# 
# Command: load the package
# Code: from LatentSemanticAnalyzer import *
# 
# Command: use the documents aDocs
# Code: LatentSemanticAnalyzer(aDocs)
# 
# Command: use dfTemp
# Code: LatentSemanticAnalyzer(dfTemp)
# 
# Command: make the document-term matrix
# Code: make_document_term_matrix()
# 
# Command: make the document-term matrix with automatic stop words
# Code: make_document_term_matrix[stemming_rules=None,stopWords=True)
# 
# Command: show the topics
# Code:

llm = ChatOllama(model=os.getenv("OLLAMA_MODEL", "gemma3:12b"))
commands = [
    "use the dataset aAbstracts",
    "make the document-term matrix without stemming",
    "extract 40 topics using the method non-negative matrix factorization",
    "show the topics",
]
llm = ChatOllama(model="gemma3:12b")
chain = few_shot_prompt | llm | StrOutputParser()
sep = dsl_workflow_separators('Python', 'LSAMon')
result = []
for command in commands:
    result.append(chain.invoke({"command": command}))
print(sep.join([x.strip() for x in result]))

# LatentSemanticAnalyzer(aAbstracts)
# .make_document_term_matrix(stemming_rules=None)
# .extract_topics(40, method='non-negative_matrix_factorization')
# .show_topics()

try:
    from langchain_community.llms.fake import FakeListLLM
except Exception:
    from langchain_core.language_models.fake import FakeListLLM
commands = [
    "use the dataset aAbstracts",
    "make the document-term matrix without stemming",
    "extract 40 topics using the method non-negative matrix factorization",
    "show the topics",
]
# Fake responses to demonstrate the flow
fake_responses = [
    "lsamon = lsamon_use_dataset(\"aAbstracts\")",
    "lsamon = lsamon_make_document_term_matrix(stemming=False)",
    "lsamon = lsamon_extract_topics(method=\"NMF\", n_topics=40)",
    "lsamon_show_topics(lsamon)",
]
llm = FakeListLLM(responses=fake_responses)
# Create a simple chain by piping the prompt into the LLM
chain = few_shot_prompt | llm
for command in commands:
    result = chain.invoke({"command": command})
    print("Command:", command)
    print("Code:", result)
    print("-")

# Command: use the dataset aAbstracts
# Code: lsamon = lsamon_use_dataset("aAbstracts")
# -
# Command: make the document-term matrix without stemming
# Code: lsamon = lsamon_make_document_term_matrix(stemming=False)
# -
# Command: extract 40 topics using the method non-negative matrix factorization
# Code: lsamon = lsamon_extract_topics(method="NMF", n_topics=40)
# -
# Command: show the topics
# Code: lsamon_show_topics(lsamon)
# -

from LLMTextualAnswer import *
from langchain_ollama import ChatOllama
import os
llm = ChatOllama(model=os.getenv("OLLAMA_MODEL", "gemma3:4b"))

text = """
Lake Titicaca is a large, deep lake in the Andes 
on the border of Bolivia and Peru. By volume of water and by surface 
area, it is the largest lake in South America
"""
llm_textual_answer(text, "Where is Titicaca?", llm=llm, form = None)

# {'Where is Titicaca?': 'The Andes, on the border of Bolivia Peru'}

llm_textual_answer(text, "Where is Titicaca?", request = "answer the question:", llm = llm)

# {'Where is Titicaca?': 'The Andes, on the border of Bolivia Peru'}

textCap = """
Born and raised in the Austrian Empire, Tesla studied engineering and physics in the 1870s without receiving a degree,
gaining practical experience in the early 1880s working in telephony and at Continental Edison in the new electric power industry.
In 1884 he emigrated to the United States, where he became a naturalized citizen.
He worked for a short time at the Edison Machine Works in New York City before he struck out on his own.
With the help of partners to finance and market his ideas,
Tesla set up laboratories and companies in New York to develop a range of electrical and mechanical devices.p
His alternating current (AC) induction motor and related polyphase AC patents, licensed by Westinghouse Electric in 1888,
earned him a considerable amount of money and became the cornerstone of the polyphase system which that company eventually marketed.
"""
len(textCap)

# 862

llm_textual_answer(textCap, 'Where lived?', n = 3, llm = llm)

# {'Where lived?': 'Austrian Empire, United States, New York City'}

llm_textual_answer(textCap, 'Where lived?', llm = llm)

# {'Where lived?': 'Austrian Empire, United States'}

query = 'Make a classifier with the method RandomForest over the data dfTitanic; show precision and accuracy.';
questions = [
        'What is the dataset?',
        'What is the method?',
        'Which metrics to show?'
]

res = llm_textual_answer(query, questions, llm = llm, form = dict)
for (k,v) in res.items():
    print(f"{k} : {v}")

# What is the dataset? : dfTitanic
# What is the method? : RandomForest
# Which metrics to show? : Precision, accuracy