Update app.py

app.py

@@ -1,68 +1,55 @@
-!pip install -q openai gradio pytesseract Pillow
-
+import os
 import openai
 import gradio as gr
-from openai import OpenAI
 from PIL import Image
 import pytesseract
 
-# 🔐 Set your OpenAI key here
-client = OpenAI(api_key="sk-REPLACE_WITH_YOUR_KEY")  # Replace with your actual OpenAI key
+# 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 = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}]
+    response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt=prompt,
+        max_tokens=500
     )
-    return response.choices[0].message.content.strip()
+    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 = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}]
+    response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt=prompt,
+        max_tokens=500
     )
-    return response.choices[0].message.content.strip()
+    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 = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}]
+    response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt=prompt,
+        max_tokens=500
     )
-    return response.choices[0].message.content.strip()
+    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 = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}]
-    )
-    return response.choices[0].message.content.strip()
-
-def generate_quiz(subject, grade, image=None):
-    image_text = extract_text(image) if image else ""
-    prompt = f"Create a 5-question multiple choice quiz for {subject} at grade {grade} level.\n\n{image_text}"
-    response = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}]
+    response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt=prompt,
+        max_tokens=500
     )
-    return response.choices[0].message.content.strip()
-
-def assign_homework(subject, grade, task_description, image=None):
-    image_text = extract_text(image) if image else ""
-    prompt = f"Generate a detailed homework assignment for {subject}, grade {grade}.\nTask: {task_description}\n\n{image_text}"
-    response = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}]
-    )
-    return response.choices[0].message.content.strip()
+    return response.choices[0].text.strip()
 
 def extract_text(image):
     if image is not None:
@@ -70,7 +57,7 @@ def extract_text(image):
     return ""
 
 # --- Gradio Interface ---
-with gr.Blocks(title="Teacher AI Assistant") as app:
+with gr.Blocks() as demo:
     gr.Markdown("## 👩‍🏫 Teacher's AI Assistant")
 
     with gr.Tabs():
@@ -103,21 +90,5 @@ with gr.Blocks(title="Teacher AI Assistant") as app:
             image_output = gr.Textbox(label="AI Response from Image", lines=12)
             img_btn.click(extract_text_from_image, image_input, image_output)
 
-        with gr.TabItem("🧪 Generate Quiz"):
-            quiz_subject = gr.Dropdown(choices=["Math", "Science", "English", "History"], label="Subject")
-            quiz_grade = gr.Dropdown(choices=[str(i) for i in range(1, 13)], label="Grade Level")
-            quiz_image = gr.Image(type="pil", label="Optional: Upload material")
-            quiz_btn = gr.Button("Create Quiz")
-            quiz_output = gr.Textbox(label="Quiz", lines=12)
-            quiz_btn.click(generate_quiz, [quiz_subject, quiz_grade, quiz_image], quiz_output)
-
-        with gr.TabItem("📚 Assign Homework"):
-            hw_subject = gr.Dropdown(choices=["Math", "Science", "English", "History"], label="Subject")
-            hw_grade = gr.Dropdown(choices=[str(i) for i in range(1, 13)], label="Grade Level")
-            task_description = gr.Textbox(label="Describe the homework task")
-            hw_image = gr.Image(type="pil", label="Optional: Upload extra materials")
-            hw_btn = gr.Button("Generate Assignment")
-            hw_output = gr.Textbox(label="Homework Details", lines=10)
-            hw_btn.click(assign_homework, [hw_subject, hw_grade, task_description, hw_image], hw_output)
+demo.launch(debug=True, share=True)
 
-app.launch(debug=True, share=True)