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 (UPDATED) ==========
def extract_courses_with_grade_levels(text):
    grade_level_pattern = r"(Grade|Year)\s*[:]?\s*(\d+|Freshman|Sophomore|Junior|Senior)"
    grade_match = re.search(grade_level_pattern, text, re.IGNORECASE)
    current_grade_level = grade_match.group(2) if grade_match else "Unknown"

    course_pattern = r"""
        (?:^|\n)
        (?: (Grade|Year)\s*[:]?\s*(\d+|Freshman|Sophomore|Junior|Senior)\s*[\n-]* )?
        (
            (?:[A-Z]{2,}\s?\d{3})
            |
            [A-Z][a-z]+(?:\s[A-Z][a-z]+)*
        )
        \s*
        (?: [:\-]?\s* ([A-F][+-]?|\d{2,3}%)? )?
    """

    courses_by_grade = defaultdict(list)
    current_grade = current_grade_level

    for match in re.finditer(course_pattern, text, re.VERBOSE | re.MULTILINE):
        grade_context, grade_level, course, grade = match.groups()

        if grade_context:
            current_grade = grade_level

        if course:
            course_info = {"course": course.strip()}
            if grade:
                course_info["grade"] = grade.strip()
            courses_by_grade[current_grade].append(course_info)

    return dict(courses_by_grade)

def parse_transcript(file):
    if file.name.endswith('.csv'):
        df = pd.read_csv(file)
    elif file.name.endswith('.xlsx'):
        df = pd.read_excel(file)
    elif 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"

        # Enhanced 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'Cumulative GPA \(Weighted\)[\s:]*(\d\.\d{1,2})',
            r'Unweighted GPA[\s:]*(\d\.\d{1,2})',
            r'GPA \(Unweighted\)[\s:]*(\d\.\d{1,2})',
            r'Cumulative 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

        courses_by_grade = extract_courses_with_grade_levels(text)

        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"

        output_text += "\n(Courses not shown here)"

        return output_text, {
            "gpa": gpa_data,
            "grade_level": grade_level,
            "courses": courses_by_grade
        }
    else:
        return "Unsupported file format", None

    # For CSV/XLSX fallback
    gpa = "N/A"
    for col in ['GPA', 'Grade Point Average', 'Cumulative GPA']:
        if col in df.columns:
            gpa = df[col].iloc[0] if isinstance(df[col].iloc[0], (float, int)) else "N/A"
            break

    grade_level = "N/A"
    for col in ['Grade Level', 'Grade', 'Class', 'Year']:
        if col in df.columns:
            grade_level = df[col].iloc[0]
            break

    courses = []
    for col in ['Course', 'Subject', 'Course Name', 'Class']:
        if col in df.columns:
            courses = df[col].tolist()
            break

    output_text = f"Grade Level: {grade_level}\nGPA: {gpa}\n\nCourses:\n"
    output_text += "\n".join(f"- {course}" for course in courses)

    return output_text, {
        "gpa": {"unweighted": gpa, "weighted": "N/A"},
        "grade_level": grade_level,
        "courses": courses
    }

# ========== SAVE STUDENT PROFILE FUNCTION ==========

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)

    markdown_summary = f"""### Student Profile: {name}

**Age:** {age}  
**Interests:** {interests}  
**Learning Style:** {learning_style}  

#### Transcript:
{transcript_display(transcript)}

#### Favorites:
- Movie: {favorites['movie']} ({favorites['movie_reason']})
- Show: {favorites['show']} ({favorites['show_reason']})
- Book: {favorites['book']} ({favorites['book_reason']})
- Character: {favorites['character']} ({favorites['character_reason']})

#### Blog:
{blog if blog else "_No blog provided_"}
"""
    return markdown_summary

def transcript_display(transcript_dict):
    if not transcript_dict:
        return "No transcript uploaded."
    if isinstance(transcript_dict, dict) and all(isinstance(v, list) for v in transcript_dict.values()):
        display = ""
        for grade_level, courses in transcript_dict.items():
            display += f"\n**Grade {grade_level}**\n"
            for course in courses:
                display += f"- {course['course']}"
                if 'grade' in course:
                    display += f" (Grade: {course['grade']})"
                display += "\n"
        return display
    return "\n".join([f"- {course}" for course in transcript_dict["courses"]] +
                     [f"Grade Level: {transcript_dict['grade_level']}", f"GPA: {transcript_dict['gpa']}"])

# ========== GRADIO INTERFACE ==========

with gr.Blocks() as app:
    with gr.Tab("Step 1: Upload Transcript"):
        transcript_file = gr.File(label="Upload your transcript (CSV, Excel, or PDF)")
        transcript_output = gr.Textbox(label="Transcript Output")
        transcript_data = gr.State()
        transcript_file.change(fn=parse_transcript, inputs=transcript_file, outputs=[transcript_output, transcript_data])

    with gr.Tab("Step 2: Learning Style Quiz"):
        q1 = gr.Radio(choices=[
            "I remember something better when I see it written down.",
            "I remember best by listening to a lecture or a recording.",
            "I remember best by reading information on my own."
        ], label="1. How do you best remember information?")
        q2 = gr.Radio(choices=q1.choices, label="2. What’s your preferred study method?")
        q3 = gr.Radio(choices=q1.choices, label="3. What helps you understand new topics?")
        q4 = gr.Radio(choices=q1.choices, label="4. How do you prefer to take notes?")
        q5 = gr.Radio(choices=q1.choices, label="5. When you visualize concepts, what helps most?")
        learning_output = gr.Textbox(label="Learning Style Result")
        gr.Button("Submit Quiz").click(learning_style_quiz, inputs=[q1, q2, q3, q4, q5], 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(fn=lambda x: gr.update(visible=x), inputs=blog_checkbox, outputs=blog_text)

    with gr.Tab("Step 4: Save & Review"):
        output_summary = gr.Markdown()
        save_btn = gr.Button("Save Profile")

        def gather_and_save(name, age, interests, movie, movie_reason, show, show_reason,
                            book, book_reason, character, character_reason, blog, transcript, learning_style):
            favorites = {
                "movie": movie,
                "movie_reason": movie_reason,
                "show": show,
                "show_reason": show_reason,
                "book": book,
                "book_reason": book_reason,
                "character": character,
                "character_reason": character_reason,
            }
            return save_profile(name, age, interests, transcript, learning_style, favorites, blog)

        save_btn.click(fn=gather_and_save,
                       inputs=[name, age, interests, movie, movie_reason, show, show_reason,
                               book, book_reason, character, character_reason, blog_text,
                               transcript_data, learning_output],
                       outputs=output_summary)

app.launch()