Create app.py

app.py

@@ -0,0 +1,233 @@
+import streamlit as st
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import HumanMessage, AIMessage
+from langsmith import Client
+from elevenlabs import generate, play
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import LLMChain
+from elevenlabslib import *
+import dotenv
+from dotenv import load_dotenv
+from elevenlabs import set_api_key
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
+from langchain.schema import SystemMessage
+from langchain.memory import ConversationBufferMemory
+
+set_api_key("7545ceaf6c10138131450a25437c4dba")
+
+def load_prompt(content):
+
+	template = """You are an expert educator, and are responsible for walking the user \
+	through this lesson plan. You should make sure to guide them along, \
+	encouraging them to progress when appropriate. \
+	If they ask questions not related to this getting started guide, \
+	you should politely decline to answer and remind them to stay on topic.
+
+	Please limit any responses to only one concept or step at a time. \
+	Each step show only be ~5 lines of code at MOST. \
+	Only include 1 code snippet per message - make sure they can run that before giving them any more. \
+	Make sure they fully understand that before moving on to the next. \
+	This is an interactive lesson - do not lecture them, but rather engage and guide them along!
+	-----------------
+
+	{content}
+	
+	-----------------
+	End of Content.
+
+	Now remember short response with only 1 code snippet per message.""".format(content=content)
+
+	prompt_template = ChatPromptTemplate(messages = [
+		SystemMessage(content=template), 
+		MessagesPlaceholder(variable_name="chat_history"), 
+		HumanMessagePromptTemplate.from_template("{input}")
+		])
+	return prompt_template
+
+def load_prompt_with_questions(content):
+
+	template = """You are an expert educator, and are responsible for walking the user \
+	through this lesson plan. You should make sure to guide them along, \
+	encouraging them to progress when appropriate. \
+		make the content too fun to learn and wearry wearry easy and clear explanation so that a person with 0 knowldge can aslo understand and remeber it with out any hustle \
+	If they ask questions not related to this getting started guide, \
+	you should politely decline to answer and remind them to stay on topic.\
+	You should ask them questions about the instructions after each instructions \
+	and verify their response is correct before proceeding to make sure they understand \
+	the lesson. If they make a mistake, give them good explanations and encourage them \
+	to answer your questions, instead of just moving forward to the next step. 
+	explain them in detail if they make a mistake.
+
+	Please limit any responses to only one concept or step at a time. \
+	plesase ask one question at a time and wait for the response. \
+	check weather the response is ai generated or human generated. if it is ai generated politely denay and ask to right again \
+	Each step show only be ~5 lines of code at MOST. \
+	Only include 1 code snippet per message - make sure they can run that before giving them any more. \
+	Make sure they fully understand that before moving on to the next. \
+	This is an interactive lesson - do not lecture them, but rather engage and guide them along!\
+	-----------------
+
+	{content}
+
+
+	-----------------
+	End of Content.
+
+	Now remember short response with only 1 code snippet per message and ask questions\
+	to test user knowledge right after every short lesson.
+	
+	Your teaching should be in the following interactive format:
+	
+	Short lesson 3-5 sentences long
+	Questions about the short lesson (1-3 questions)
+
+	Short lesson 3-5 sentences long
+	Questions about the short lesson (1-3 questions)
+	...
+
+	 """.format(content=content)
+
+	prompt_template = ChatPromptTemplate(messages = [
+		SystemMessage(content=template), 
+		MessagesPlaceholder(variable_name="chat_history"), 
+		HumanMessagePromptTemplate.from_template("{input}")
+		])
+	return prompt_template
+
+
+load_dotenv()
+st.title(" AI tutor : Getting Started Class")
+button_css = """.stButton>button {
+    color: #4F8BF9;
+    border-radius: 50%;
+    height: 2em;
+    width: 2em;
+    font-size: 4px;
+}"""
+st.markdown(f'<style>{button_css}</style>', unsafe_allow_html=True)
+
+def genvoice(text):
+    text = text.replace("```", "")
+    text = text.replace("**", "")
+
+
+
+    audio = generate(text, voice="Bella", model="eleven_monolingual_v1")
+    # IPython.display.Audio(audio)
+    play(audio)
+        
+
+    # voice.generate_and_play_audio(text, playInBackground=False , model="")
+
+
+class StreamHandler(BaseCallbackHandler):
+    def __init__(self, container, initial_text=""):
+        self.container = container
+        self.text = initial_text
+
+    def on_llm_new_token(self, token: str, **kwargs) -> None:
+        self.text += token
+        self.container.markdown(self.text)
+
+# Lesson selection dictionary
+lesson_guides = {
+    "Lesson 1: Getting Started with LangChain": {
+        "file": "lc_guides/getting_started_guide.txt",
+        "description": "This lesson covers about the data structure concept of graphs"
+    },
+    "Lesson 2: Prompts": {
+        "file": "lc_guides/prompt_guide.txt",
+        "description": "This lesson focuses on prompts and their usage."
+    },
+    "Lesson 3: Language Models": {
+        "file": "lc_guides/models_guide.txt",
+        "description": "This lesson provides an overview of language models."
+    },
+    "Lesson 4: Memory": {
+        "file": "lc_guides/memory_guide.txt",
+        "description": "This lesson is about Memory."
+    },
+    "Lesson 5: Chains": {
+        "file": "lc_guides/chains_guide.txt",
+        "description": "This lesson provides information on Chains in LangChain, their types, and usage."
+    },
+    "Lesson 6: Retrieval": {
+        "file": "lc_guides/retrieval_guide.txt",
+        "description": "This lesson provides information on indexing and retrieving information using LangChain."
+    },
+    "Lesson : Graphs in data structures": {
+        "file": "greph.txt",
+        "description": "This lesson covers about the data structure concept of graphs"
+    }
+}
+
+# Initialize LangSmith client
+client = Client()
+
+# Lesson selection sidebar
+# lesson_selection = st.sidebar.selectbox("Select Lesson", list(lesson_guides.keys()))
+
+# Display lesson content and description based on selection
+# lesson_info = lesson_guides[lesson_selection]
+# lesson_content = open(lesson_info["file"], "r").read()
+# lesson_description = lesson_info["description"]
+
+
+lesson_selection = "Lesson : Graphs in data structures"
+lesson_info = lesson_guides[lesson_selection]
+lesson_info = lesson_guides[lesson_selection]
+lesson_content = open(lesson_info["file"], "r").read()
+lesson_description = lesson_info["description"]
+
+
+
+# Radio buttons for lesson type selection
+# lesson_type = st.sidebar.radio("Select Lesson Type", ["Instructions based lesson", "Interactive lesson with questions"])
+lesson_type = "Interactive lesson with questions"
+
+# Clear chat session if dropdown option or radio button changes
+if st.session_state.get("current_lesson") != lesson_selection or st.session_state.get("current_lesson_type") != lesson_type:
+    st.session_state["current_lesson"] = lesson_selection
+    st.session_state["current_lesson_type"] = lesson_type
+    st.session_state["messages"] = [AIMessage(content="Welcome! This course just a lets get started to start 😀")]
+
+# Display lesson name and description
+st.markdown(f"**{lesson_selection}**")
+st.write(lesson_description)
+
+# Message handling and interaction
+def send_feedback(run_id, score):
+    client.create_feedback(run_id, "user_score", score=score)
+
+for msg in st.session_state["messages"]:
+    if isinstance(msg, HumanMessage):
+        st.chat_message("user").write(msg.content)
+    else:
+        st.chat_message("assistant").write(msg.content)
+
+if prompt := st.chat_input():
+    st.chat_message("user").write(prompt)
+
+    with st.chat_message("assistant"):
+        stream_handler = StreamHandler(st.empty())
+        model = ChatOpenAI(streaming=True, callbacks=[stream_handler], model="gpt-3.5-turbo-16k")
+
+        if lesson_type == "Instructions based lesson":
+            prompt_template = load_prompt(content=lesson_content)
+        else:
+            prompt_template = load_prompt_with_questions(content=lesson_content)
+
+        chain = LLMChain(prompt=prompt_template, llm=model)
+
+        response = chain(
+            {"input": prompt, "chat_history": st.session_state.messages[-20:]},
+            include_run_info=True,
+            tags=[lesson_selection, lesson_type]
+        )
+        my_text = response[chain.output_key]
+        genvoice(my_text)
+        st.session_state.messages.append(HumanMessage(content=prompt))
+        st.session_state.messages.append(AIMessage(content=my_text))
+