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import os
import openai
import gradio as gr
from PIL import Image
import pytesseract
# Access the OpenAI API key from Hugging Face Secrets
api_key = os.getenv("OPENAI_API_KEY")
# Set the OpenAI API key
openai.api_key = api_key
# --- AI Features ---
def generate_lesson_plan(subject, grade, image=None):
image_text = extract_text(image) if image else ""
prompt = f"Create a detailed lesson plan for {subject} at grade {grade} level.\n\n{image_text}"
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt,
max_tokens=500
)
return response.choices[0].text.strip()
def grade_student_answer(question, student_answer, image=None):
image_text = extract_text(image) if image else ""
prompt = f"Question: {question}\nStudent's Answer: {student_answer}\n\n{image_text}\n\nGrade this answer and provide feedback."
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt,
max_tokens=500
)
return response.choices[0].text.strip()
def track_progress(notes, image=None):
image_text = extract_text(image) if image else ""
prompt = f"Summarize and analyze the following student progress notes:\n{notes}\n\n{image_text}"
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt,
max_tokens=500
)
return response.choices[0].text.strip()
def extract_text_from_image(image):
text = pytesseract.image_to_string(image)
prompt = f"Extracted text from image:\n{text}\n\nProvide educational insight or summary."
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt,
max_tokens=500
)
return response.choices[0].text.strip()
def extract_text(image):
if image is not None:
return pytesseract.image_to_string(image)
return ""
# --- Gradio Interface ---
with gr.Blocks() as demo:
gr.Markdown("## 👩🏫 Teacher's AI Assistant")
with gr.Tabs():
with gr.TabItem("📘 Lesson Plan Generator"):
subject = gr.Dropdown(choices=["Math", "Science", "English", "History"], label="Subject")
grade = gr.Dropdown(choices=[str(i) for i in range(1, 13)], label="Grade Level")
image1 = gr.Image(type="pil", label="Optional: Upload related material")
lesson_btn = gr.Button("Generate Lesson Plan")
lesson_output = gr.Textbox(label="AI Lesson Plan", lines=15)
lesson_btn.click(generate_lesson_plan, [subject, grade, image1], lesson_output)
with gr.TabItem("📝 Grade Answer"):
question = gr.Textbox(label="Question")
student_answer = gr.Textbox(label="Student's Answer")
image2 = gr.Image(type="pil", label="Optional: Upload student work")
grade_btn = gr.Button("Grade")
grade_output = gr.Textbox(label="Feedback", lines=8)
grade_btn.click(grade_student_answer, [question, student_answer, image2], grade_output)
with gr.TabItem("📈 Progress Tracker"):
notes = gr.Textbox(label="Student Progress Notes", lines=8)
image3 = gr.Image(type="pil", label="Optional: Upload progress notes")
progress_btn = gr.Button("Analyze")
progress_output = gr.Textbox(label="Analysis", lines=10)
progress_btn.click(track_progress, [notes, image3], progress_output)
with gr.TabItem("🖼️ Image Upload"):
image_input = gr.Image(type="pil", label="Upload an image of handwritten or printed work")
img_btn = gr.Button("Analyze Image")
image_output = gr.Textbox(label="AI Response from Image", lines=12)
img_btn.click(extract_text_from_image, image_input, image_output)
demo.launch(debug=True, share=True)
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