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import zipfile
import gradio as gr
from langchain_openai import ChatOpenAI
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
# Unzip vector DB if not already extracted
if not os.path.exists("geometry_chroma"):
with zipfile.ZipFile("geometry_chroma.zip", 'r') as zip_ref:
zip_ref.extractall(".")
# Load vector DB
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectordb = Chroma(persist_directory="geometry_chroma", embedding_function=embedding_model)
retriever = vectordb.as_retriever()
# Set OpenAI key (use Secrets or .env later)
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.2)
# ✅ Prompt templates
templates = {
"flashcard": PromptTemplate(
input_variables=["context", "query"],
template="""
{context}
Create 5 flashcards based on the topic: "{query}"
Each flashcard should include:
- A clear question
- A short answer
Focus on high school geometry understanding.
"""
),
"lesson plan": PromptTemplate(
input_variables=["context", "query"],
template="""
Given the following retrieved SOL text:
{context}
Generate a Geometry lesson plan based on: "{query}"
Include:
1. Simple explanation of the concept.
2. Real-world example.
3. Engaging class activity.
Be concise and curriculum-aligned for high school.
"""
),
"worksheet": PromptTemplate(
input_variables=["context", "query"],
template="""
{context}
Create a student worksheet for: "{query}"
Include:
- Concept summary
- A worked example
- 3 practice problems
"""
),
"proofs": PromptTemplate(
input_variables=["context", "query"],
template="""
{context}
Generate a proof-focused geometry lesson plan for: "{query}"
Include:
- Student-friendly explanation
- Real-world connection
- One short class activity
"""
),
"general question": PromptTemplate(
input_variables=["context", "query"],
template="""
{context}
Based on the above SOL content, identify the Geometry SOL standard number (e.g., G.RLT.1, G.GPE.3) that best aligns with the question: "{query}"
Just return the SOL code (e.g., G.RLT.1) and a short justification.
"""
)
}
def generate_output(prompt_type, query):
docs = retriever.get_relevant_documents(query)
context = "\n\n".join([doc.page_content for doc in docs])
chain = LLMChain(llm=llm, prompt=templates[prompt_type])
return chain.run({"context": context, "query": query})
# ✅ Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# 📐 Geometry Teaching Assistant")
with gr.Row():
query = gr.Textbox(label="Enter a geometry topic")
prompt_type = gr.Dropdown(
["general question", "lesson plan", "worksheet", "proofs", "flashcard"],
value="flashcard",
label="Prompt Type"
)
output = gr.Textbox(label="Generated Output", lines=12, interactive=True)
btn = gr.Button("Generate")
btn.click(fn=generate_output, inputs=[prompt_type, query], outputs=output)
demo.launch()
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