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