app.py

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 extract_courses_with_grade_levels(text):
    # First extract the current grade level
    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"

    # Improved course pattern to better match course codes and names
    course_pattern = r"""
        (?:^|\n)
        (?: (Grade|Year)\s*[:]?\s*(\d+|Freshman|Sophomore|Junior|Senior)\s*[\n-]* )?  # Optional grade level context
        (
            (?:[A-Z]{2,}\s?\d{3}[A-Z]?\b)  # Course codes like MATH101 or CS 201A
            |
            (?:[A-Z][a-z]+(?:\s+[A-Z][a-z]+)+)  # Course names like "Calculus I" or "World History"
        )
        \s*
        (?: [:\-]?\s* ([A-F][+-]?|\d{2,3}%)? )?  # Optional grade
        \s*
        (?: [:\-]?\s* (\d\.\d{1,2})? )?  # Optional credit hours
    """

    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, credits = match.groups()

        if grade_context:
            current_grade = grade_level

        if course:
            course_info = {
                "course": course.strip(),
                "grade_level": current_grade
            }
            if grade:
                course_info["grade"] = grade.strip()
            if credits:
                course_info["credits"] = credits.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"

        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

    return f"Grade Level: {grade_level}\nGPA: {gpa}", {
        "gpa": {"unweighted": gpa, "weighted": "N/A"},
        "grade_level": grade_level,
        "courses": courses
    }

# ... [keep all other functions the same until transcript_display] ...

def transcript_display(transcript_dict):
    if not transcript_dict:
        return "No transcript uploaded."
    
    if isinstance(transcript_dict, dict) and "courses" in transcript_dict:
        if isinstance(transcript_dict["courses"], dict):
            display = "### Course History\n\n"
            for grade_level, courses in transcript_dict["courses"].items():
                display += f"**Grade {grade_level}**\n"
                for course in courses:
                    display += f"- {course.get('course', 'N/A')}"
                    if 'grade' in course:
                        display += f" (Grade: {course['grade']})"
                    if 'credits' in course:
                        display += f" | Credits: {course['credits']}"
                    display += "\n"
                display += "\n"
            return display
        elif isinstance(transcript_dict["courses"], list):
            return "### Courses\n" + "\n".join([f"- {course}" for course in transcript_dict["courses"]])
    
    return "No course information available in the expected format."

# ========== 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:",
    "When you're assembling furniture or a gadget, you:",
    "When choosing a restaurant, you rely most on:",
    "When you're in a waiting room, you typically:",
    "When giving someone instructions, you tend to:",
    "When you're trying to recall information, you:",
    "When you're at a museum or exhibit, you:",
    "When you're learning a new language, you prefer:",
    "When you're taking notes in class, you:",
    "When you're explaining something complex, you:",
    "When you're at a party, you enjoy:",
    "When you're trying to remember a phone number, you:",
    "When you're relaxing, you prefer to:",
    "When you're learning to use new software, you:",
    "When you're giving a presentation, you rely on:",
    "When you're solving a difficult problem, you:"
]

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)"],
    ["Read the instructions carefully (Reading/Writing)", "Look at the diagrams (Visual)", "Ask someone to explain (Auditory)", "Start putting pieces together (Kinesthetic)"],
    ["Online photos of the food (Visual)", "Recommendations from friends (Auditory)", "Reading the menu online (Reading/Writing)", "Remembering how it felt to eat there (Kinesthetic)"],
    ["Read magazines (Reading/Writing)", "Listen to music (Auditory)", "Watch TV (Visual)", "Fidget or move around (Kinesthetic)"],
    ["Write them down (Reading/Writing)", "Explain verbally (Auditory)", "Demonstrate (Visual)", "Guide them physically (Kinesthetic)"],
    ["See written words in your mind (Visual)", "Hear the information in your head (Auditory)", "Write it down to remember (Reading/Writing)", "Associate it with physical actions (Kinesthetic)"],
    ["Read all the descriptions (Reading/Writing)", "Listen to audio guides (Auditory)", "Look at the displays (Visual)", "Touch interactive exhibits (Kinesthetic)"],
    ["Study grammar rules (Reading/Writing)", "Listen to native speakers (Auditory)", "Use flashcards with images (Visual)", "Practice conversations (Kinesthetic)"],
    ["Write detailed paragraphs (Reading/Writing)", "Record the lecture (Auditory)", "Draw diagrams and charts (Visual)", "Doodle while listening (Kinesthetic)"],
    ["Write detailed steps (Reading/Writing)", "Explain verbally with examples (Auditory)", "Draw diagrams (Visual)", "Use physical objects to demonstrate (Kinesthetic)"],
    ["Conversations with people (Auditory)", "Watching others or the environment (Visual)", "Writing notes or texting (Reading/Writing)", "Dancing or physical activities (Kinesthetic)"],
    ["See the numbers in your head (Visual)", "Say them aloud (Auditory)", "Write them down (Reading/Writing)", "Dial them on a keypad (Kinesthetic)"],
    ["Read a book (Reading/Writing)", "Listen to music (Auditory)", "Watch TV/movies (Visual)", "Do something physical (Kinesthetic)"],
    ["Read the manual (Reading/Writing)", "Ask someone to show you (Visual)", "Call tech support (Auditory)", "Experiment with the software (Kinesthetic)"],
    ["Detailed notes (Reading/Writing)", "Verbal explanations (Auditory)", "Visual slides (Visual)", "Physical demonstrations (Kinesthetic)"],
    ["Write out possible solutions (Reading/Writing)", "Talk through it with someone (Auditory)", "Draw diagrams (Visual)", "Build a model or prototype (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())
    total_questions = len(learning_style_questions)
    
    # Calculate percentages
    percentages = {style: (score/total_questions)*100 for style, score in scores.items()}
    
    # Sort styles by score (descending)
    sorted_styles = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    
    # Prepare detailed results
    result = "Your Learning Style Results:\n\n"
    for style, score in sorted_styles:
        result += f"{style}: {score}/{total_questions} ({percentages[style]:.1f}%)\n"
    
    result += "\n"
    
    # Determine primary and secondary styles
    primary_styles = [style for style, score in scores.items() if score == max_score]
    
    if len(primary_styles) == 1:
        result += f"Your primary learning style is: {primary_styles[0]}\n\n"
        # Add personalized tips based on primary style
        if primary_styles[0] == "Visual":
            result += "Tips for Visual Learners:\n"
            result += "- Use color coding in your notes\n"
            result += "- Create mind maps and diagrams\n"
            result += "- Watch educational videos\n"
            result += "- Use flashcards with images\n"
        elif primary_styles[0] == "Auditory":
            result += "Tips for Auditory Learners:\n"
            result += "- Record lectures and listen to them\n"
            result += "- Participate in study groups\n"
            result += "- Explain concepts out loud to yourself\n"
            result += "- Use rhymes or songs to remember information\n"
        elif primary_styles[0] == "Reading/Writing":
            result += "Tips for Reading/Writing Learners:\n"
            result += "- Write detailed notes\n"
            result += "- Create summaries in your own words\n"
            result += "- Read textbooks and articles\n"
            result += "- Make lists to organize information\n"
        else:  # Kinesthetic
            result += "Tips for Kinesthetic Learners:\n"
            result += "- Use hands-on activities\n"
            result += "- Take frequent movement breaks\n"
            result += "- Create physical models\n"
            result += "- Associate information with physical actions\n"
    else:
        result += f"You have multiple strong learning styles: {', '.join(primary_styles)}\n\n"
        result += "You may benefit from combining different learning approaches.\n"
    
    return result

# ========== SAVE STUDENT PROFILE FUNCTION ==========
def save_profile(name, age, interests, transcript, learning_style, movie, movie_reason, show, show_reason, book, book_reason, character, character_reason, blog):
    # Convert age to int if it's a numpy number (from gradio Number input)
    age = int(age) if age else 0
    
    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
    }
    
    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 "courses" in transcript_dict:
        if isinstance(transcript_dict["courses"], dict):
            display = ""
            for grade_level, courses in transcript_dict["courses"].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
        elif isinstance(transcript_dict["courses"], list):
            return "\n".join([f"- {course}" for course in transcript_dict["courses"]])
    return "No course information available"

# ========== AI TEACHING ASSISTANT ==========
def load_profile():
    if not os.path.exists("student_profiles"):
        return {}
    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 {}

def generate_response(message, history):
    profile = load_profile()
    if not profile:
        return "Please complete and save your profile first using the previous tabs."
    
    # Get profile data
    learning_style = profile.get("learning_style", "")
    grade_level = profile.get("transcript", {}).get("grade_level", "unknown")
    gpa = profile.get("transcript", {}).get("gpa", {})
    interests = profile.get("interests", "")
    
    # Common responses
    greetings = ["hi", "hello", "hey"]
    study_help = ["study", "learn", "prepare", "exam"]
    grade_help = ["grade", "gpa", "score"]
    interest_help = ["interest", "hobby", "passion"]
    
    if any(greet in message.lower() for greet in greetings):
        return f"Hello {profile.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:
            response = ("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:
            response = ("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:
            response = ("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:
            response = ("Based on your kinesthetic learning style, I recommend:\n"
                       "- Hands-on practice\n"
                       "- Creating physical models\n"
                       "- Taking frequent movement breaks")
        else:
            response = ("Here are some general study tips:\n"
                       "- Break study sessions into 25-minute chunks\n"
                       "- Review material regularly\n"
                       "- Teach concepts to someone else")
        
        return response
    
    elif any(word in message.lower() for word in grade_help):
        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 any(word in message.lower() for word in interest_help):
        return (f"I see you're interested in: {interests}\n\n"
               "You might want to:\n"
               "- Find clubs or activities related to these interests\n"
               "- Explore career paths that align with them")
    
    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:
    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"):
        gr.Markdown("### Learning Style Quiz (20 Questions)")
        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", lines=10)
        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?", precision=0)
        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 & Review"):
        output_summary = gr.Markdown()
        save_btn = gr.Button("Save Profile")
        
        save_btn.click(
            fn=save_profile,
            inputs=[name, age, interests, transcript_data, learning_output,
                   movie, movie_reason, show, show_reason,
                   book, book_reason, character, character_reason, blog_text],
            outputs=output_summary
        )

    # AI Teaching Assistant Tab
    with gr.Tab("🤖 AI Teaching Assistant"):
        gr.Markdown("## Your Personalized Learning Assistant")
        chatbot = gr.ChatInterface(
            fn=generate_response,
            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?"
            ]
        )

if __name__ == "__main__":
    app.launch()