app.py

import streamlit as st
import openai

# Streamlit Session State
if 'learning_objectives' not in st.session_state:
    st.session_state.learning_objectives = ""

# Streamlit User Input Form
st.title("Lesson Plan Generator")

# API Key Input
api_key = st.text_input("Enter your OpenAI API Key:", type="password")

# Model Selection Dropdown
model_choice = st.selectbox(
    "Select the model you want to use:",
    ["gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613", "gpt-3.5-turbo", "gpt-4-0314", "gpt-4-0613", "gpt-4"]
)

# Context, Subject, and Level
context = "Your goal is to create an effective lesson plan."
subject = st.text_input("Subject:", "Mathematics")
level = st.text_input("Education Level:", "High School")

# Initialize OpenAI API
if api_key:
    openai.api_key = api_key

# Learning Objectives
st.write("### Learning Objectives:")
# Initialize autogenerated objectives
autogenerated_objectives = ""
# Initialize status placeholder
learning_status_placeholder = st.empty()
disable_button_bool = False
if subject and level and api_key and st.button("Generate Learning Objectives",key="generate_learning_objectives",disabled=disable_button_bool):

    # Display status message
    learning_status_placeholder.text("Generating learning objectives...")
    # API call to generate objectives
    learning_objectives_response = openai.ChatCompletion.create(
        model=model_choice,
        messages=[
            {"role": "user", "content": f"Generate learning objectives for a {level} level {subject} lesson."}
        ]
    )
    # Extract the generated objectives from the API response
    learning_objectives=learning_objectives_response['choices'][0]['message']['content']
    
    # Save generated objectives to session state
    st.session_state.learning_objectives = learning_objectives.strip()
    
    # Display generated objectives
    learning_status_placeholder.text(f"Learning objectives generated!\n{learning_objectives.strip()}")
    
# Generate Lesson Plan Button
if st.button("Generate Lesson Plan") and api_key:
    
    # Construct the prompt as a dictionary
    prompt_dict = {
        "context": context,
        "subject": subject,
        "level": level,
        "learning_objectives": st.session_state.learning_objectives,
        "tasks": [
            {"task": "Curate educational material", "objective": "To provide accurate and relevant information"},
            {"task": "Evaluate the material", "objective": "To ensure alignment with educational standards"},
            {"task": "Create assessment tools", "objective": "To measure student understanding and retention"},
            {"task": "Design interactive activities", "objective": "To facilitate active learning and engagement"}
        ],
        "output_format": """Present the lesson plan in a structured format.
        \nTitle: 
        \nGrade Level: RESTATED FROM ABOVE
        \nSubject:  RESTATED FROM ABOVE
        \nObjectives: RESTATED FROM ABOVE
        \nActivities: 
        \nAssessment: Application of knowledge and skills through a task
        \nProcedure: Formatted as a list of steps and substeps
        \nResources:
        \nNotes:
        
        """
    }
    
    # Convert the dictionary to a string
    prompt_str = str(prompt_dict)

    # API call to generate the lesson plan
    lesson_plan_response = openai.ChatCompletion.create(
        model=model_choice,
        messages=[
            {"role": "user", "content": f"Create a lesson plan based on the following parameters: {prompt_str}"}
        ]
    )

    # Display status message
    lesson_plan=st.text("Generating lesson plan...")

    # Extract and display the lesson plan
    assistant_reply = lesson_plan_response['choices'][0]['message']['content']
    lesson_plan=st.text(assistant_reply.strip())