app.py

import os
import re
import sys
import logging
import nest_asyncio
#import time

import panel as pn
import tiktoken
import chromadb

from llama_index.core import (
    Settings, 
    VectorStoreIndex, 
    PromptTemplate, 
    PromptHelper, 
    StorageContext
)
from llama_index.core.text_splitter import SentenceSplitter
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.readers.web import SimpleWebPageReader
from llama_index.vector_stores.chroma import ChromaVectorStore

nest_asyncio.apply()

FORMAT = "%(asctime)s | %(levelname)s | %(name)s | %(message)s"

@pn.cache
def get_logger(name, format_=FORMAT, level=logging.INFO):
    logger = logging.getLogger(name)

    logger.handlers.clear()

    handler = logging.StreamHandler()
    handler.setStream(sys.stdout)
    formatter = logging.Formatter(format_)
    handler.setFormatter(formatter)
    logger.addHandler(handler)
    logger.propagate = False

    logger.setLevel(level)
    logger.info("Logger successfully configured")
    return logger

####################
# Global Constants #
####################

pn.extension("codeeditor", sizing_mode="stretch_width")

TTL = 1800  # 30 minutes
ACCENT = "#2EB872"
THEME = pn.config.theme

CHAT_GPT_LOGO = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/ChatGPT_logo.svg/512px-ChatGPT_logo.svg.png"
CHAT_GPT_URL = "https://chat.openai.com/"
LLAMA_INDEX_LOGO = "https://asset.brandfetch.io/id6a4s3gXI/idncpUsO_z.jpeg"
LLAMA_INDEX_URL = "https://www.llamaindex.ai/"

LLM_VERSION = "gpt-3.5-turbo-1106"

pn.chat.ChatMessage.default_avatars.update(
    {
        "assistant": CHAT_GPT_LOGO,
        "user": "🦙",
    }
)
pn.chat.ChatMessage.show_reaction_icons = False

EXPLANATION = f"""
## ScaleUp - (Level up your Python abilities)
---

**ScaleUp** is a powerful Python coding assistant app that leverages `OpenAI` and `LlamaIndex` to provide an interactive, 
AI-powered learning experience.

It acts as a virtual mentor, offering expert guidance, contextually relevant responses, and an integrated code editor for writing and testing Python code.

### Key Features:

- **Expert Python Guidance**: Get insightful and accurate answers to your Python queries.
- **Interactive Code Editor**: Write and test your code, with suggestions and code snippets from the AI.
- **Context-Aware Responses**: Responses are tailored based on your provided information and a comprehensive knowledge base.
- **Streaming Responses**: Receive real-time, up-to-date responses as the AI generates them.

## OpenAI GPT
---
We are using the OpenAI `{LLM_VERSION}` to power the coding assistant.

## Getting Started
---

Ask your Python-related questions, share your code snippets, or request guidance on specific topics.

The AI will respond with detailed explanations, code examples, and insightful suggestions to help you learn and improve your Python skills.
"""

SYSTEM_PROMPT = (
    "You are an expert Python developer with years of experience writing Python code and teaching Python to other programmers. "
    "You have vast experience mentoring people who are learning Python. "
    "I want you to be my mentor while I learn Python myself. "
    "Your goal is to provide insightful, accurate, and concise answers to questions in this domain. "
    "When generating code, please explicitly state the sources you reference.\n\n"
    "Here is some context related to the query:\n"
    "-----------------------------------------\n"
    "{context_str}\n"
    "-----------------------------------------\n"
    "Considering the above information, please respond to the following inquiry with detailed references to applicable principles, "
    "libraries, design patterns, or debugging methodology where appropriate:\n\n"
    "Question: {query_str}\n\n"
    "Answer succinctly, and ensure your response is understandable to someone with extreme enthusiasm to learn Python programming."
)

# URL's for context with RAG Based Data 
URLS = [
    "https://thewhitetulip.gitbook.io/py",
    "https://docs.python.org/3/tutorial/",
    "https://awesomepython.org/",
    "https://awesome-python.com/",
]

##########################################
# Data Processing and handling functions #
##########################################

USER_CONTENT_FORMAT = """
Request:
{content}
Code:
```python
{code}
```
""".strip()

DEFAULT_CODE_EXAMPLE = """
print("Hello World")
""".strip()

# Sample Python programming questions
EXAMPLE_QUESTIONS = f"""
## Python Programming Questions

### Basic

- Write a Python function to find the maximum of three numbers.
- Write a Python program to reverse a string.
- Write a Python program to check if a given number is prime or not.
- Write a Python program to find the factorial of a number.
- Write a Python program to check if a string is a palindrome or not.
- Write a Python program to find the largest number in a list. 
- Write a Python program to find the sum of all numbers in a list.
- Write a Python program to find the second largest number in a list.
- Write a Python program to remove duplicates from a list.
- Write a Python program to implement a simple calculator.
- Write a Python program to check if a string is a palindrome. 
- Write a Python program to find the Fibonacci sequence up to a given number.
- Write a Python program to Solve the Fizbuzz Algorithm in the most simple way you can think of ...

### Advanced

- Write a Python program to sort a list of dictionaries by a specific value.
- Write a Python program to implement a binary search algorithm.
- Write a Python program to implement a merge sort algorithm.
- Write a Python program to implement a linked list data structure.
- Write a Python program to implement a binary tree data structure.
- Implement an LRU (Least Recently Used) Cache.
- Write a function to check if a binary tree is balanced. 
- Implement a stack using two queues. 
- Write a function to calculate the factorial of a number recursively.
- Implement a depth-first search (DFS) algorithm to traverse a graph.  

"""

def _powered_by():
    """Returns a component describing the frameworks powering the chat ui."""
    params = {"height": 40, "sizing_mode": "fixed", "margin": (0, 10)}
    return pn.Column(
        pn.pane.Markdown("### AI Powered By", margin=(10, 5, 10, 0)),
        pn.Row(
            pn.pane.Image(LLAMA_INDEX_LOGO, link_url=LLAMA_INDEX_URL, **params),
            pn.pane.Image(CHAT_GPT_LOGO, link_url=CHAT_GPT_URL, **params),
            align="center",
        ),
    )

llm = OpenAI(temperature=0.1, model=LLM_VERSION, max_tokens=512)
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
text_splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=20)

prompt_helper = PromptHelper(
    context_window=4096,
    num_output=256,
    chunk_overlap_ratio=0.1,
    chunk_size_limit=None,
)

# Settings configuration
Settings.llm = llm
Settings.embed_model = embed_model
Settings.tokenizer = tiktoken.encoding_for_model(LLM_VERSION).encode
Settings.text_splitter = text_splitter
Settings.prompt_helper = prompt_helper

def load_data(data=URLS):
    """
    Initialize the Index
    """
    reader = SimpleWebPageReader(html_to_text=True)
    documents = reader.load_data(data)

    logging.info("index creating with `%d` documents", len(documents))
    chroma_client = chromadb.EphemeralClient()
    chroma_collection = chroma_client.get_or_create_collection("python-data")
    vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model)

    return index


def initialize_query_engine(index):
    """
    Initialize Query Engine
    """
    # Custom Prompt Template
    template = SYSTEM_PROMPT
    qa_template = PromptTemplate(template)

    # build query engine with custom template
    query_engine = index.as_query_engine(text_qa_template=qa_template, similarity_top_k=3)

    return query_engine


def build_chat_engine(index):
    """
    Initialize Chat Engine
    """
    # Custom Prompt Template
    template = SYSTEM_PROMPT
    qa_template = PromptTemplate(template)

    chat_engine = index.as_chat_engine(
        chat_mode="context",
        text_qa_template=qa_template,
        verbose=True,
        streaming=True
    )
    return chat_engine

############
# Main App #
############

logger = get_logger(name="app")

index = load_data()

# Custom Prompt Template
template = SYSTEM_PROMPT
qa_template = PromptTemplate(template)

chat_engine = index.as_chat_engine(
    chat_mode="context", 
    text_qa_template=qa_template,
    verbose = True,
    streaming=True
    )

# Getting the API Key
os.getenv('OPENAI_API_KEY')

async def generate_response(
        contents: str, 
        user: str, 
        instance: pn.chat.ChatInterface
    ):
    """
    Docstring placeholder
    """
    response = await chat_engine.astream_chat(contents)
    text = ""
    async for token in response.async_response_gen():
        text += token
        yield text

    # extract code from LLM response
    llm_code = re.findall(r"```python\n(.*)\n```", text, re.DOTALL)[0]
    code_editor.value = llm_code


#######################
# Panel UI Components #
#######################

chat_interface = pn.chat.ChatInterface(
    callback=generate_response,
    show_send=True,
    show_rerun=False,
    show_undo=True,
    show_clear=True,
    show_button_name=True,
    sizing_mode="stretch_both",
    callback_exception="verbose"
)

chat_interface.send(
    SYSTEM_PROMPT, 
    user="System", 
    respond=False
)

code_editor = pn.widgets.CodeEditor(
    value=DEFAULT_CODE_EXAMPLE,
    language="python",
    sizing_mode="stretch_both",
)

# Create a layout for the widgets
question_layout = pn.Column(
    EXAMPLE_QUESTIONS,
    sizing_mode="stretch_width"
)

# lay them out in tabs
tabs_layout = pn.Tabs(
    ("Code", code_editor),
    ("Example Questions", question_layout),
    sizing_mode = "stretch_both",
)

component = pn.Row(
    chat_interface,
    tabs_layout, 
    sizing_mode="stretch_both"
)

# Serve UI Template
template = pn.template.FastListTemplate(
    title="ScaleUp Code Assistant 🐍",
    sidebar=[
            EXPLANATION,
            _powered_by(), 
            ],
    main=[component],
    main_layout=None,
    accent=ACCENT,
)

template.servable()

##################
# End of the App #
##################