import gradio as gr
import pandas as pd
import json
import os
import re
from PyPDF2 import PdfReader
from collections import defaultdict

# ========== TRANSCRIPT PARSING FUNCTIONS ==========
def parse_transcript(file):
    if file.name.endswith('.pdf'):
        text = ''
        reader = PdfReader(file)
        for page in reader.pages:
            page_text = page.extract_text()
            if page_text:
                text += page_text + '\n'

        # Grade level extraction
        grade_match = re.search(r'(Grade|Year)[\s:]*(\d+|Freshman|Sophomore|Junior|Senior)', text, re.IGNORECASE)
        grade_level = grade_match.group(2) if grade_match else "Unknown"

        # GPA extraction
        gpa_data = {'weighted': "N/A", 'unweighted': "N/A"}
        gpa_patterns = [
            r'Weighted GPA[\s:]*(\d\.\d{1,2})',
            r'GPA \(Weighted\)[\s:]*(\d\.\d{1,2})',
            r'Unweighted GPA[\s:]*(\d\.\d{1,2})',
            r'GPA \(Unweighted\)[\s:]*(\d\.\d{1,2})',
            r'GPA[\s:]*(\d\.\d{1,2})'
        ]
        
        for pattern in gpa_patterns:
            for match in re.finditer(pattern, text, re.IGNORECASE):
                gpa_value = match.group(1)
                if 'weighted' in pattern.lower():
                    gpa_data['weighted'] = gpa_value
                elif 'unweighted' in pattern.lower():
                    gpa_data['unweighted'] = gpa_value
                else:
                    if gpa_data['unweighted'] == "N/A":
                        gpa_data['unweighted'] = gpa_value
                    if gpa_data['weighted'] == "N/A":
                        gpa_data['weighted'] = gpa_value

        output_text = f"Grade Level: {grade_level}\n\n"
        if gpa_data['weighted'] != "N/A" or gpa_data['unweighted'] != "N/A":
            output_text += "GPA Information:\n"
            if gpa_data['unweighted'] != "N/A":
                output_text += f"- Unweighted GPA: {gpa_data['unweighted']}\n"
            if gpa_data['weighted'] != "N/A":
                output_text += f"- Weighted GPA: {gpa_data['weighted']}\n"
        else:
            output_text += "No GPA information found\n"

        return output_text, {
            "gpa": gpa_data,
            "grade_level": grade_level
        }
    else:
        return "Currently only PDF transcripts are supported", None

# ========== LEARNING STYLE QUIZ ==========
learning_style_questions = [
    "When you study for a test, you prefer to:",
    "When you need directions to a new place, you prefer:",
    "When you learn a new skill, you prefer to:",
    "When you're trying to concentrate, you:",
    "When you meet new people, you remember them by:"
]

learning_style_options = [
    ["Read the textbook (Reading/Writing)", "Listen to lectures (Auditory)", "Use diagrams/charts (Visual)", "Practice problems (Kinesthetic)"],
    ["Look at a map (Visual)", "Have someone tell you (Auditory)", "Write down directions (Reading/Writing)", "Try walking/driving there (Kinesthetic)"],
    ["Read instructions (Reading/Writing)", "Have someone show you (Visual)", "Listen to explanations (Auditory)", "Try it yourself (Kinesthetic)"],
    ["Need quiet (Reading/Writing)", "Need background noise (Auditory)", "Need to move around (Kinesthetic)", "Need visual stimulation (Visual)"],
    ["Their face (Visual)", "Their name (Auditory)", "What you talked about (Reading/Writing)", "What you did together (Kinesthetic)"]
]

def learning_style_quiz(*answers):
    scores = {
        "Visual": 0,
        "Auditory": 0,
        "Reading/Writing": 0,
        "Kinesthetic": 0
    }
    
    for i, answer in enumerate(answers):
        if answer == learning_style_options[i][0]:
            scores["Reading/Writing"] += 1
        elif answer == learning_style_options[i][1]:
            scores["Auditory"] += 1
        elif answer == learning_style_options[i][2]:
            scores["Visual"] += 1
        elif answer == learning_style_options[i][3]:
            scores["Kinesthetic"] += 1
    
    max_score = max(scores.values())
    dominant_styles = [style for style, score in scores.items() if score == max_score]
    
    if len(dominant_styles) == 1:
        return f"Your primary learning style is: {dominant_styles[0]}"
    else:
        return f"You have multiple strong learning styles: {', '.join(dominant_styles)}"

# ========== PROFILE MANAGEMENT ==========
def save_profile(name, age, interests, transcript, learning_style, favorites, blog):
    data = {
        "name": name,
        "age": age,
        "interests": interests,
        "transcript": transcript,
        "learning_style": learning_style,
        "favorites": favorites,
        "blog": blog
    }
    os.makedirs("student_profiles", exist_ok=True)
    json_path = os.path.join("student_profiles", f"{name.replace(' ', '_')}_profile.json")
    with open(json_path, "w") as f:
        json.dump(data, f, indent=2)
    
    return "Profile saved successfully!"

def load_profile():
    files = [f for f in os.listdir("student_profiles") if f.endswith('.json')]
    if files:
        with open(os.path.join("student_profiles", files[0]), "r") as f:
            return json.load(f)
    return {}

# ========== RULE-BASED TEACHING ASSISTANT ==========
def generate_response(message, history, profile_data):
    # Get learning style from profile
    learning_style = profile_data.get("learning_style", "")
    
    # Common responses
    greetings = ["hi", "hello", "hey"]
    study_help = ["study", "learn", "prepare"]
    grade_help = ["grade", "gpa", "score"]
    
    if any(greet in message.lower() for greet in greetings):
        return f"Hello {profile_data.get('name', 'there')}! How can I help you today?"
    
    elif any(word in message.lower() for word in study_help):
        if "Visual" in learning_style:
            return ("Based on your visual learning style, I recommend:\n"
                   "- Creating mind maps or diagrams\n"
                   "- Using color-coded notes\n"
                   "- Watching educational videos")
        elif "Auditory" in learning_style:
            return ("Based on your auditory learning style, I recommend:\n"
                   "- Recording lectures and listening to them\n"
                   "- Participating in study groups\n"
                   "- Explaining concepts out loud")
        elif "Reading/Writing" in learning_style:
            return ("Based on your reading/writing learning style, I recommend:\n"
                   "- Writing detailed notes\n"
                   "- Creating summaries in your own words\n"
                   "- Reading textbooks and articles")
        elif "Kinesthetic" in learning_style:
            return ("Based on your kinesthetic learning style, I recommend:\n"
                   "- Hands-on practice\n"
                   "- Creating physical models\n"
                   "- Taking frequent movement breaks")
        else:
            return ("Here are some general study tips:\n"
                   "- Break study sessions into 25-minute chunks\n"
                   "- Review material regularly\n"
                   "- Teach concepts to someone else")
    
    elif any(word in message.lower() for word in grade_help):
        gpa = profile_data.get("transcript", {}).get("gpa", {})
        return (f"Your GPA information:\n"
               f"- Unweighted: {gpa.get('unweighted', 'N/A')}\n"
               f"- Weighted: {gpa.get('weighted', 'N/A')}\n\n"
               "To improve your grades, try:\n"
               "- Setting specific goals\n"
               "- Meeting with teachers\n"
               "- Developing a study schedule")
    
    elif "help" in message.lower():
        return ("I can help with:\n"
               "- Study tips based on your learning style\n"
               "- GPA and grade information\n"
               "- General academic advice\n\n"
               "Try asking about study strategies or your grades!")
    
    else:
        return ("I'm your personalized teaching assistant. "
               "I can help with study tips, grade information, and academic advice. "
               "Try asking about how to study for your classes!")

# ========== GRADIO INTERFACE ==========
with gr.Blocks() as app:
    # Profile tabs
    with gr.Tab("Step 1: Upload Transcript"):
        transcript_file = gr.File(label="Upload your transcript (PDF)")
        transcript_output = gr.Textbox(label="Transcript Output")
        transcript_data = gr.State()
        transcript_file.change(parse_transcript, inputs=transcript_file, outputs=[transcript_output, transcript_data])

    with gr.Tab("Step 2: Learning Style Quiz"):
        quiz_components = []
        for i, (question, options) in enumerate(zip(learning_style_questions, learning_style_options)):
            quiz_components.append(gr.Radio(options, label=f"{i+1}. {question}"))
        
        learning_output = gr.Textbox(label="Learning Style Result")
        gr.Button("Submit Quiz").click(
            learning_style_quiz,
            inputs=quiz_components,
            outputs=learning_output
        )

    with gr.Tab("Step 3: Personal Questions"):
        name = gr.Textbox(label="What's your name?")
        age = gr.Number(label="How old are you?")
        interests = gr.Textbox(label="What are your interests?")
        movie = gr.Textbox(label="Favorite movie?")
        movie_reason = gr.Textbox(label="Why do you like that movie?")
        show = gr.Textbox(label="Favorite TV show?")
        show_reason = gr.Textbox(label="Why do you like that show?")
        book = gr.Textbox(label="Favorite book?")
        book_reason = gr.Textbox(label="Why do you like that book?")
        character = gr.Textbox(label="Favorite character?")
        character_reason = gr.Textbox(label="Why do you like that character?")
        blog_checkbox = gr.Checkbox(label="Do you want to write a blog?", value=False)
        blog_text = gr.Textbox(label="Write your blog here", visible=False, lines=5)
        blog_checkbox.change(lambda x: gr.update(visible=x), inputs=blog_checkbox, outputs=blog_text)

    with gr.Tab("Step 4: Save Profile"):
        save_btn = gr.Button("Save Profile")
        save_output = gr.Textbox(label="Save Status")
        
        save_btn.click(
            save_profile,
            inputs=[name, age, interests, transcript_data, learning_output, 
                   {"movie": movie, "movie_reason": movie_reason,
                    "show": show, "show_reason": show_reason,
                    "book": book, "book_reason": book_reason,
                    "character": character, "character_reason": character_reason},
                   blog_text],
            outputs=save_output
        )

    # AI Teaching Assistant Tab
    with gr.Tab("🤖 Teaching Assistant"):
        gr.Markdown("## Your Personalized Learning Assistant")
        chatbot = gr.ChatInterface(
            fn=lambda message, history: generate_response(message, history, load_profile()),
            examples=[
                "How should I study for my next test?",
                "What's my GPA information?",
                "Help me with study strategies",
                "How can I improve my grades?"
            ]
        )

app.launch()