add users interests

app.py

@@ -5,7 +5,6 @@ import openai
 import pinecone
 import json
 import re
-import os
 
 PINECONE_API_KEY = st.secrets["PINECONE_API_KEY"]
 # Set OpenAI API key from Streamlit Secrets
@@ -19,6 +18,7 @@ class Metadata(TypedDict):
     title: str
     description: str
     slides: str
+    outcome: str
 
 # Initialize Pinecone and OpenAI
 pinecone.init(api_key=PINECONE_API_KEY, environment="asia-southeast1-gcp")
@@ -46,13 +46,16 @@ def get_embeddings(texts: List[str]) -> List[List[float]]:
     # Return the embeddings as a list of lists of floats
     return [result["embedding"] for result in data]
 
+@st.cache_resource
+def load_pinecone_index():
+    index_name = "prequelworkshops"
+    return pinecone.Index(index_name)
+
 # Pinecone fetch function
 def fetch_lesson(query: str):
     vector = get_embeddings([query])[0]
 
-    index_name = "prequelworkshops"
-    index = pinecone.Index(index_name)
-    return index.query(
+    return load_pinecone_index().query(
         vector=vector,
         # filter={
         #     "genre": {"$eq": "documentary"},
@@ -99,32 +102,38 @@ def extract_arrays(s: str) -> Optional[List[str]]:
     else:
         return None 
 
-def generate_curriculum(user_input) -> Optional[List[str]]:
-    prompt = f"You are a world class high school teacher. Create a curriculum for a course based on the student's interest below. Output the curriculum as a javascript array of strings, where each string is a description of the lesson. The output should just be the array and nothing else. Student's interest: {user_input}"
+def generate_curriculum(skills: str) -> Optional[List[str]]:
+    prompt = f"You are a world-class middle and high school educator who develops project-based entrepreneurship curriculum catered to student interests. Create a curriculum of up to 5 lessons for a course based on the student's target skills to learn. Output the curriculum as a javascript array of strings, where each string is a description of the lesson. The output should just be the array and nothing else. Student's target skills: {skills}"
     response = query_openai(prompt)
     return extract_arrays(response)
 
-def get_metadata(lesson) -> Metadata:
-    return lesson.matches[0].metadata
+def generate_application(metadata: Metadata, interests: str) -> str:
+    prompt = f"You are a world-class middle and high school educator who develops project-based entrepreneurship curriculum catered to student interests. You've created a lesson called \"{metadata['title']}\". The description of the lesson is \"{metadata['description']}\". The expected learning outcome is \"{metadata['outcome']}\". Explain how this lesson can be applied to the student's interests in one sentence. Student's interests: {interests}"
+    response = query_openai(prompt)
+    return response
 
-def format_metadata(metadata) -> List[str]:
-    return f"Title: {metadata['title']}\n\nDescription: {metadata['description']}"
+def format_lesson(lesson, interests: str) -> List[str]:
+    metadata = lesson.matches[0].metadata
+    application = generate_application(metadata, interests)
+    return f"Title: [{metadata['title']}]({metadata['slides']})\n\nDescription: {metadata['description']}\n\n{application}"
 
 # Streamlit UI
 st.set_page_config(layout="centered")
 st.title("Personalized Learning Curriculum Generator")
-user_input = st.text_area("Enter what you want to learn:", height=200)
+skills = st.text_area("What skills would you like to learn", height=200)
+interests = st.text_area("What are you interested in? We'll try our best to build a curriculum that could apply to your interests", height=200)
 submit_button = st.button("Generate curriculum")
 status = st.empty()
 
 if submit_button:
     status.text("Generating curriculum...")
-    curriculum = generate_curriculum(user_input)
+    curriculum = generate_curriculum(skills)
 
     if curriculum is not None:
-        status.text("Fetching relevant courses...")
+        status.text("Fetching relevant lessons...")
         lessons = [fetch_lesson(lesson) for lesson in curriculum]
-        lesson_text = "\n\n".join([format_metadata(get_metadata(lesson)) for lesson in lessons])
+        status.text("Characterizing relevance to your interests...")
+        lesson_text = "\n\n".join([format_lesson(lesson, interests) for lesson in lessons])
         status.empty()
         st.markdown(f"**Generated Curriculum:**\n\n{lesson_text}")
     else: