import streamlit as st
import google.generativeai as genai
import os
import tempfile

MODEL_ID = "gemini-2.0-flash-exp"
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
model = genai.GenerativeModel(MODEL_ID)
chat = model.start_chat()

# Initialize session state variables
if 'essay_question' not in st.session_state:
    st.session_state.essay_question = ""
if 'scoring_rubric' not in st.session_state:
    st.session_state.scoring_rubric = ""
if "subject" not in st.session_state:
    st.session_state.subject = ""
if "topic" not in st.session_state:
    st.session_state.topic = ""

def generate_essay_question(subject, topic, difficulty):
    try:
        prompt = f"Create an essay question on the topic '{topic}' in the context of {subject} with a {difficulty} difficulty level.  Output the essay question only. Do not provide any other information."
        response = model.generate_content(prompt)
        return response.text
    except Exception as e:
        st.error(f"An error occurred: {e}")
        return None

def generate_scoring_rubric(essay_question):
    return f"Scoring Rubric for the essay question: {essay_question}\n\n1. Clarity: 10 points\n2. Argument Strength: 10 points\n3. Grammar: 10 points\n4. Creativity: 10 points\n5. Relevance to Topic: 10 points\n\nTotal: 50 points"

def capture_exam_photo():
    img_file_buffer = None
    image_path = ""

    # Use the camera to capture an image
    img_file_buffer = st.camera_input("Take a picture")

    if img_file_buffer is not None:
            # Save the image to a temporary file
            with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
                temp_file.write(img_file_buffer.read())
                image_path = temp_file.name
            return image_path
    else:
        st.warning("Please capture an image of the exam paper.")
        return None

def main():

    # Streamlit UI
    st.title("Essay Grading System")

    about = """# 📝 Essay Grading System 🎓  

Welcome to the **Essay Grading System**! This interactive app helps educators generate essay questions, create scoring rubrics, and automate grading using AI.  

## 📌 Features  
✅ **Essay Question Generator** – Select a subject, topic, and difficulty to generate thought-provoking essay questions.  
✅ **Scoring Rubric Builder** – Automatically generate a rubric and refine it as needed.  
✅ **AI-Powered Grading** – Upload a photo of an exam paper and let AI assess it based on the rubric.  

## 🎯 How It Works  
1️⃣ **Go to the "Essay Question" tab** – Choose your subject, enter a topic, and set the difficulty.  
2️⃣ **Switch to "Scoring Rubric"** – A grading rubric is generated based on the question, and you can edit it.  
3️⃣ **Move to "Grading Outputs"** – Upload an image of a handwritten essay, and the AI will grade it based on the rubric.  

🚀 **Enhance efficiency and accuracy in grading with this AI-assisted tool!**    

### 📌 About the Creator  
**Created by:** *Louie F. Cervantes, M.Eng. (Information Engineering)*  
**(c) 2025 West Visayas State University**
"""

    with st.expander("About the App"):
        st.markdown(about)

    tabs = ["Essay Question", "Scoring Rubric", "Grading Outputs"]
    selected_tab = st.sidebar.radio("Select a Tab", tabs)

    if selected_tab == "Essay Question":
        st.header("Generate Essay Question")
        if st.session_state.subject != "":
            subject = st.text_input("Enter Subject", st.session_state.subject)
        else:
            subject = st.text_input("Enter Subject")
        if st.session_state.subject != "":
            topic = st.text_input("Enter Topic", st.session_state.topic)
        else:
            topic = st.text_input("Enter Topic")

        difficulty = st.selectbox("Select Difficulty", ["Easy", "Moderate", "Difficult"])
        st.session_state.subject = subject
        st.session_state.topic = topic

        if st.button("Generate Question"):
            if subject and topic and difficulty:
                st.session_state.essay_question = generate_essay_question(subject, topic, difficulty)
                st.success("Essay question generated successfully!")
            else:
                st.warning("Please provide the subject and topic.")        
        if st.session_state.essay_question:
            st.subheader("Generated Essay Question:")
            st.write(st.session_state.essay_question)

    elif selected_tab == "Scoring Rubric":
        st.header("Scoring Rubric")
        if not st.session_state.essay_question:
            st.warning("Please generate an essay question first.")
        else:
            if not st.session_state.scoring_rubric:
                st.session_state.scoring_rubric = generate_scoring_rubric(st.session_state.essay_question)
            
            st.subheader("Generated Scoring Rubric:")
            st.session_state.scoring_rubric = st.text_area("Edit Rubric", st.session_state.scoring_rubric, height=200)

    elif selected_tab == "Grading Outputs":
        st.header("Grading Outputs")
        if not st.session_state.essay_question or not st.session_state.scoring_rubric:
            st.warning("Please generate both an essay question and a scoring rubric first.")
        else:
            exam_image_path = capture_exam_photo()
            mime_type = "image/jpeg"
            if exam_image_path is not None:

                # Upload the file with the correct MIME type
                file_data = genai.upload_file(exam_image_path, mime_type=mime_type)

                multimodal_prompt = f"Extract all the text from the image. Score the essay exam response to the Essay Question: {st.session_state.essay_question}\nScoring Rubric: {st.session_state.scoring_rubric}.  Provide feednack on the essay response."
                
                # Send file and prompt to Gemini API
                response = chat.send_message(
                    [
                        multimodal_prompt, 
                        file_data
                    ]
                )                

                st.subheader("AI Grading Response:")
                # Display Gemini response
                st.markdown(response.text)


if __name__ == "__main__":
    main()