Update app.py

app.py

@@ -54,6 +54,9 @@ 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,
@@ -76,76 +79,62 @@ class QueueCallback(BaseCallbackHandler):
     def on_llm_end(self, *args, **kwargs: Any) -> None:
         return self.q.empty()
 
-class DDSAgent:
+MODEL = "gpt-3.5-turbo"
+
+# 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, name, db, prompt_template='', model_name='gpt-4', verbose=False, temp=0.2):
-    self.db = db
+    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="gpt-4",
+      model_name=MODEL,
       temperature=temp
       )
 
     #The zero shot prompt provided at creation
     self.prompt_template = prompt_template
 
-    #The LLM used for conversation summarization
-    self.summary_llm = ChatOpenAI(
-        model_name=model_name,
-        max_tokens=25,
-        callbacks=[PromptLayerCallbackHandler(pl_tags=["froebel"])],
-        streaming=False,
-        )
-
-    #Reviews convesation history and summarizes it to keep the token count down.
-    self.memory = ConversationSummaryMemory(llm=self.summary_llm,
-                                            max_token_limit=200,
-                                            memory_key="memory",
-                                            input_key="input")
-
   def chain(self, prompt: PromptTemplate, llm: ChatOpenAI) -> LLMChain:
         return LLMChain(
             llm=llm,
             prompt=prompt,
             verbose=self.verbose,
-            memory=self.memory
         )
 
-  def lookup(self, input, num_docs=5):
-    docs = self.db.similarity_search(input, k=num_docs)
-    docs_to_string = ""
-    for doc in docs:
-      docs_to_string += str(doc.page_content)
-    return docs_to_string
-
   def stream(self, input) -> Generator:
 
   # Create a Queue
     q = Queue()
     job_done = object()
 
-    #RAG
-    docs = self.lookup(input,5)
-
     llm = ChatOpenAI(
-        model_name='gpt-4',
+        model_name=MODEL,
         callbacks=[QueueCallback(q),
-                  PromptLayerCallbackHandler(pl_tags=["froebel"])],
+                  PromptLayerCallbackHandler(pl_tags=["unit-generator"])],
         streaming=True,
         )
 
     prompt = PromptTemplate(
-          input_variables=['input','docs','history'],
+          input_variables=['input'], 
           template=self.prompt_template
-          # partial_variables={"format_instructions": self.parser.get_format_instructions()}
         )
 
     # Create a funciton to call - this will run in a thread
     def task():
         resp = self.chain(prompt,llm).run(
-            {'input':input,
-             'docs':docs,
-             'history':self.memory})
+            {'input':input})
         q.put(job_done)
 
     # Create a thread and start the function
@@ -165,53 +154,32 @@ class DDSAgent:
         except Empty:
             continue
 
-
-
-
 agent_prompt = """
-            Roleplay
-            You are a UBD ( Understanding by Design ) coach.
-            Educators come to you to develop UBD based learning experiences
-            and curriculum.
-
-            This is the conversation up until now:
-            {history}
-
-            The teacher says:
+            Take the following class overview (delimited by three dollar signs)
+            $$$
             {input}
-
-            As a result, following standards were matched:
-            {docs}
-
-            Respond to the teacher message.
-
-            You have three objectives:
-
-            a) to help them through the design process
-            b) to help simplify the process for the educator
-            c) to help build confidence and understand in the ubd process
-
-            Take it step by step and keep.
-            Keep focused on the current task at hand.
-            Close with a single guiding step in the form of a question.
-            Be encouraging.
-
-            Do not start with "AI:" or any self identifying text.
-
+            $$$
+
+            Make a list of 3 big and enduring ideas that students should walk away with.
+            Make a list of 9 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 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 potential assessments we can use to give students opportunities to demonstrate their skills. 
+            Make a list of 7 ways that I might adapt the experience for different learning styles. Make a list of 5 materials and technologies that I can enhance the learning experience.
+            Make a list of 4 learning activities that support and scaffold the learning experience. 
         """
 
-def ask_agent(input, history):
-    dds = DDSAgent('agent', db, prompt_template=agent_prompt)
-    for next_token, content in dds.stream(input):
+unit_generator = UnitGenerator(prompt_template=agent_prompt)
+
+def generate_unit(input):
+    for next_token, content in unit_generator.stream(input):
         yield(content)
 
-gr.ChatInterface(ask_agent,
-                 title="UBD Coach",
-                  description="""
-                  Using the Understanding By Design framework? I can help. (/◕ヮ◕)/
-                  """,
-                  theme="monochrome",
-                  retry_btn=None,
-                  undo_btn=None,
-                  clear_btn=None
-                 ).queue().launch(debug=True)
+gr.Interface(generate_unit,
+             [gr.Textbox(
+                label="Enter your unit vision.",
+                info="Provide high level details for the learning experience."
+             )],
+              [gr.Textbox(
+                label="Unit",
+              )],allow_flagging="never").queue().launch(debug=True, share=True)