app.py

import gradio as gr
import pdfplumber
import docx
import os
import openai

client = openai.OpenAI()  # Uses OPENAI_API_KEY from environment

def extract_text_from_file(file):
    if file.name.endswith(".pdf"):
        with pdfplumber.open(file.name) as pdf:
            return "\n".join(page.extract_text() for page in pdf.pages if page.extract_text())
    elif file.name.endswith(".docx"):
        doc = docx.Document(file.name)
        return "\n".join([p.text for p in doc.paragraphs])
    elif file.name.endswith(".txt"):
        return file.read().decode("utf-8")
    else:
        return "Unsupported file type."

def generate_content(tek_text):
    prompt = f"""
You are an AI education assistant. The following is a list of TEKS (Texas Essential Knowledge and Skills) or similar learning standards:

{tek_text}

For each TEK or standard, generate:
1. A short summary or note explaining the concept.
2. A list of 3-5 important vocabulary words.
3. 2-3 practice problems (multiple choice, fill in the blank, or short answer).

Format:
TEK: <actual text>
Notes: <summary>
Vocabulary: <words>
Practice Problems:
1. <question>
2. <question>
---
"""

    response = client.chat.completions.create(
        model="gpt-3",
        messages=[
            {"role": "system", "content": "You are a helpful AI educator."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.7
    )
    return response.choices[0].message.content

def process_file(file):
    teks_text = extract_text_from_file(file)
    if teks_text.startswith("Unsupported"):
        return teks_text
    return generate_content(teks_text)

with gr.Blocks() as demo:
    gr.Markdown("# 📚 TEKS Learning Generator\\nUpload a TEKS or learning standard document (PDF, DOCX, or TXT), and the AI will generate notes, vocabulary, and practice problems for each TEK.")

    with gr.Row():
        file_input = gr.File(label="Upload TEKS Document")
        output = gr.Textbox(label="AI-Generated Output", lines=30)

    submit_btn = gr.Button("Generate Learning Content")
    submit_btn.click(fn=process_file, inputs=file_input, outputs=output)

if __name__ == "__main__":
    demo.launch()