app.py

from langchain.docstore.document import Document
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.memory.simple import SimpleMemory

from langchain.chains import ConversationChain, LLMChain, SequentialChain
from langchain.memory import ConversationBufferMemory

from langchain.prompts import ChatPromptTemplate, PromptTemplate
from langchain.document_loaders import UnstructuredFileLoader

from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.memory import ConversationSummaryMemory

from langchain.callbacks import PromptLayerCallbackHandler
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)

from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.callbacks.base import BaseCallbackHandler
import gradio as gr

from threading import Thread
from queue import Queue, Empty
from threading import Thread
from collections.abc import Generator
from langchain.llms import OpenAI
from langchain.callbacks.base import BaseCallbackHandler

import itertools
import time
import os
import getpass
import json
import sys
from typing import Any, Dict, List, Union

import promptlayer
import openai
import gradio as gr

from pydantic import BaseModel, Field, validator

#Load the FAISS Model ( vector )
openai.api_key = os.environ["OPENAI_API_KEY"]
db = FAISS.load_local("db", OpenAIEmbeddings())

#API Keys 
promptlayer.api_key = os.environ["PROMPTLAYER"]

MODEL = "gpt-3.5-turbo"
# MODEL = "gpt-4"

from langchain.callbacks import PromptLayerCallbackHandler
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.memory import ConversationSummaryMemory

# Defined a QueueCallback, which takes as a Queue object during initialization. Each new token is pushed to the queue.
class QueueCallback(BaseCallbackHandler):
    """Callback handler for streaming LLM responses to a queue."""

    def __init__(self, q):
        self.q = q

    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        self.q.put(token)

    def on_llm_end(self, *args, **kwargs: Any) -> None:
        return self.q.empty()

MODEL = "gpt-3.5-turbo-16k"

# Defined a QueueCallback, which takes as a Queue object during initialization. Each new token is pushed to the queue.
class QueueCallback(BaseCallbackHandler):
    """Callback handler for streaming LLM responses to a queue."""

    def __init__(self, q):
        self.q = q

    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        self.q.put(token)

    def on_llm_end(self, *args, **kwargs: Any) -> None:
        return self.q.empty()

class UnitGenerator:

  def __init__(self, prompt_template='', model_name=MODEL, verbose=False, temp=0.2):
    self.verbose = verbose
    self.llm = ChatOpenAI(
      model_name=MODEL,
      temperature=temp
      )

    #The zero shot prompt provided at creation
    self.prompt_template = prompt_template

  def chain(self, prompt: PromptTemplate, llm: ChatOpenAI) -> LLMChain:
        return LLMChain(
            llm=llm,
            prompt=prompt,
            verbose=self.verbose,
        )

  def stream(self, input) -> Generator:

  # Create a Queue
    q = Queue()
    job_done = object()

    llm = ChatOpenAI(
        model_name=MODEL,
        callbacks=[QueueCallback(q),
                  PromptLayerCallbackHandler(pl_tags=["unit-generator"])],
        streaming=True,
        )

    prompt = PromptTemplate(
          input_variables=['input'], 
          template=self.prompt_template
        )

    # Create a funciton to call - this will run in a thread
    def task():
        resp = self.chain(prompt,llm).run(
            {'input':input})
        q.put(job_done)

    # Create a thread and start the function
    t = Thread(target=task)
    t.start()

    content = ""

    # Get each new token from the queue and yield for our generator
    while True:
        try:
            next_token = q.get(True, timeout=1)
            if next_token is job_done:
                break
            content += next_token
            yield next_token, content
        except Empty:
            continue

agent_prompt = """
            Take the following class overview (delimited by three dollar signs)
            $$$
            {input}
            $$$
            
            The following is a "portrait of a graduate" (delimited by three hashtags). 
            ###
            CHARACTER & INTEGRITY
              ➢ Honest:
              ■ Truthful and transparent in all interactions, both in word and action.
              ➢ Responsible:
              ■ Accounts for one's actions and decisions and takes responsibility for the
              consequences that arise from them.

              ➢ Respectful:
              ■ Treats others with dignity and compassion while valuing their opinions
              and perspectives.

              ➢ Fair:
              ■ Acts with impartiality and treats all individuals equitably and justly.
              ■ Applies the tenets of sportsmanship and fair play to all endeavors

              ❖ KNOWLEDGE & INQUIRY
              ➢ Curious:
              ■ Possesses a natural curiosity and a desire to learn.
              ■ Asks questions, seeks answers, and explores new ideas.
              ➢ Broad-minded:
              ■ Considers different points of view and revises their own beliefs and
              opinions as they encounter new information and unexpected challenges.
              ■ Understands that meaningful learning includes wellness of mind and body
              ➢ Determined:
              ■ Purposeful, persistent, and willing to work through complex and difficult
              challenges to achieve their goals.

              ➢ Innovative and Creative:
              ■ Thinks differently and cultivates innovative perspectives, outside of those
              traditionally accepted
              ❖ CITIZENSHIP & CIVILITY
              ➢ Participates:
              ■ Actively listens to others, genuinely hearing their perspectives.
              ■ Communicates perspectives with tact and respect for others' opinions
              ➢ Ethical:
              ■ Considers the interests and the well-being of the community, nation, and
              the world.
              ➢ Steward:
              ■ Committed to social justice, equity, and sustainability.
              ■ Takes action to effect constructive, and respectful change.

              ❖ SKILLS & EXPERTISE
              ➢ Analytical:
              ■ Distills information, evaluates arguments, and makes informed decisions.
              ■ Identifies assumptions, biases, and fallacies in arguments.
              ➢ Collaborative:
              ■ Encourages the sharing of ideas and opinions.
              ■ Actively contributes to finding common ground and solutions.
              ➢ Communicative:
              ■ Effectively expresses their ideas clearly and succinctly, listens actively to
              others, and asks clarifying questions.

              ➢ Disciplined:
              ■ Exhibits restraint in the face of distraction
              ■ Effectively prioritizes needs and responsibilities over desires
              ■ Mindful of time and other resource constraints, exhibiting balance in the
              fulfillment of responsibilities
            ###

            Do the following:

            Make a list of 7 big and enduring ideas that students should walk away with.
            Make a list of 7 essential questions we want students to think about.These questions should be open-ended and provocative. Written in "kid friendly" language. Designed to focus instruction for uncovering the important ideas of the content.
            Make a list of 7 ideas, concepts, generalizations and principles we want students to know and understand about the unit or topic we are teaching?
            Make a list of 7 critical skills describing what we want students to be able to do. Each item should begin with "Students will be able to..."
            Make a list of 7 example assessments we can use to give students opportunities to demonstrate their skills. Explain the assessment idea and which key concepts and skills they map to.
            Make a list of 7 creative ways that I might adapt the experience for different learning styles. Explain the way I might adapt the experience to the learning style.
            Make a list of 7 opportunity that this unit can support the learners development towards the "portrait" of a graduate. Each opportunity should identify the trait and how it might be applied. 
        """

unit_generator = UnitGenerator(prompt_template=agent_prompt)

def generate_unit(input):
    for next_token, content in unit_generator.stream(input):
        yield(content)

gr.Interface(generate_unit,
             [gr.Textbox(
                label="Enter your unit vision.",
                info="Provide high level details for the learning experience. Feel free to add any additional integrations, concepts, etc you want incorporated in the unit design."
             )],
              [gr.Textbox(
                label="Unit",
              )],allow_flagging="never").queue().launch(debug=True)