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| import gradio as gr | |
| import pandas as pd | |
| import json | |
| import os | |
| import re | |
| from PyPDF2 import PdfReader | |
| from collections import defaultdict | |
| from transformers import pipeline | |
| from typing import List, Dict, Union | |
| import pdfplumber | |
| # Initialize NER model (will load only if transformers is available) | |
| try: | |
| ner_pipeline = pipeline("ner", model="dslim/bert-base-NER") | |
| except Exception as e: | |
| print(f"Could not load NER model: {e}") | |
| ner_pipeline = None | |
| # ========== IMPROVED TRANSCRIPT PARSING ========== | |
| class UniversalTranscriptParser: | |
| def __init__(self): | |
| # Patterns for different transcript types | |
| self.patterns = { | |
| 'miami_dade': self._compile_miami_dade_patterns(), | |
| 'homeschool': self._compile_homeschool_patterns(), | |
| 'doral_academy': self._compile_doral_academy_patterns() | |
| } | |
| # Grade level mappings | |
| self.grade_level_map = { | |
| '09': '9th Grade', '10': '10th Grade', '11': '11th Grade', '12': '12th Grade', | |
| '07': '7th Grade', '08': '8th Grade', 'MA': 'Middle School' | |
| } | |
| def parse_transcript(self, text: str) -> Dict[str, Union[Dict, List[Dict]]: | |
| """Determine transcript type and parse accordingly""" | |
| transcript_type = self._identify_transcript_type(text) | |
| if transcript_type == 'homeschool': | |
| return self._parse_homeschool(text) | |
| elif transcript_type == 'doral_academy': | |
| return self._parse_doral_academy(text) | |
| else: # Default to Miami-Dade pattern | |
| return self._parse_miami_dade(text) | |
| def _identify_transcript_type(self, text: str) -> str: | |
| """Identify which type of transcript we're processing""" | |
| if re.search(r'Sample OFFICIAL HIGH SCHOOL TRANSCRIPT', text): | |
| return 'homeschool' | |
| elif re.search(r'DORAL ACADEMY HIGH SCHOOL', text): | |
| return 'doral_academy' | |
| return 'miami_dade' | |
| def _parse_homeschool(self, text: str) -> Dict[str, Union[Dict, List[Dict]]: | |
| """Parse homeschool transcript format""" | |
| courses = [] | |
| current_grade = None | |
| current_year = None | |
| # Extract student info | |
| student_info = {} | |
| name_match = re.search(r'Student Name:\s*(.+)\s*SSN:', text) | |
| if name_match: | |
| student_info['name'] = name_match.group(1).strip() | |
| # Process each line | |
| for line in text.split('\n'): | |
| # Check for grade level header | |
| grade_match = re.match(r'^\|?\s*(\d+th Grade)\s*\|.*(\d{4}-\d{4})', line) | |
| if grade_match: | |
| current_grade = grade_match.group(1) | |
| current_year = grade_match.group(2) | |
| continue | |
| # Course line pattern | |
| course_match = re.match( | |
| r'^\|?\s*([^\|]+?)\s*\|\s*([A-Z][+*]?)\s*\|\s*([^\|]+)\s*\|\s*(\d+\.?\d*)\s*\|\s*(\d+)', | |
| line | |
| ) | |
| if course_match and current_grade: | |
| course_name = course_match.group(1).strip() | |
| # Clean course names that start with | or have extra spaces | |
| course_name = re.sub(r'^\|?\s*', '', course_name) | |
| courses.append({ | |
| 'name': course_name, | |
| 'grade_level': current_grade, | |
| 'school_year': current_year, | |
| 'grade': course_match.group(2), | |
| 'credit_type': course_match.group(3).strip(), | |
| 'credits': float(course_match.group(4)), | |
| 'quality_points': int(course_match.group(5)), | |
| 'transcript_type': 'homeschool' | |
| }) | |
| # Extract GPA information from homeschool transcript | |
| gpa_data = {} | |
| gpa_match = re.search(r'Cum\. GPA\s*\|\s*([\d\.]+)', text) | |
| if gpa_match: | |
| gpa_data['unweighted'] = gpa_match.group(1) | |
| gpa_data['weighted'] = gpa_match.group(1) # Homeschool often has same weighted/unweighted | |
| return { | |
| 'student_info': student_info, | |
| 'courses': {'All': courses}, # Homeschool doesn't separate by grade in same way | |
| 'gpa': gpa_data, | |
| 'grade_level': current_grade.replace('th Grade', '') if current_grade else "Unknown" | |
| } | |
| def _parse_doral_academy(self, text: str) -> Dict[str, Union[Dict, List[Dict]]]: | |
| """Parse Doral Academy specific format""" | |
| courses = [] | |
| # Extract student info | |
| student_info = {} | |
| name_match = re.search(r'LEGAL NAME:\s*([^\n]+)', text) | |
| if name_match: | |
| student_info['name'] = name_match.group(1).strip() | |
| # Extract school year information | |
| year_pattern = re.compile(r'YEAR:\s*(\d{4}-\d{4})\s*GRADE LEVEL:\s*(\d{2})', re.MULTILINE) | |
| year_matches = year_pattern.finditer(text) | |
| # Create mapping of grade levels to years | |
| grade_year_map = {} | |
| for match in year_matches: | |
| grade_year_map[match.group(2)] = match.group(1) | |
| # Course pattern for Doral Academy | |
| course_pattern = re.compile( | |
| r'(\d)\s+(\d{7})\s+([^\n]+?)\s+([A-Z]{2})\s+([A-Z])\s+([A-Z])\s+([A-Z])\s+(\d\.\d{2})\s+(\d\.\d{2})', | |
| re.MULTILINE | |
| ) | |
| courses_by_grade = defaultdict(list) | |
| for match in course_pattern.finditer(text): | |
| grade_level_num = match.group(1) | |
| grade_level = self.grade_level_map.get(grade_level_num, f"Grade {grade_level_num}") | |
| school_year = grade_year_map.get(grade_level_num, "Unknown") | |
| course_info = { | |
| 'course_code': match.group(2), | |
| 'name': match.group(3).strip(), | |
| 'subject_area': match.group(4), | |
| 'grade': match.group(5), | |
| 'inclusion_status': match.group(6), | |
| 'credit_status': match.group(7), | |
| 'credits_attempted': float(match.group(8)), | |
| 'credits': float(match.group(9)), | |
| 'grade_level': grade_level, | |
| 'school_year': school_year, | |
| 'transcript_type': 'doral_academy' | |
| } | |
| courses_by_grade[grade_level_num].append(course_info) | |
| # Extract GPA information from Doral Academy transcript | |
| gpa_data = {} | |
| unweighted_match = re.search(r'Un-weighted GPA\s*([\d\.]+)', text) | |
| weighted_match = re.search(r'Weighted GPA\s*([\d\.]+)', text) | |
| if unweighted_match: | |
| gpa_data['unweighted'] = unweighted_match.group(1) | |
| if weighted_match: | |
| gpa_data['weighted'] = weighted_match.group(1) | |
| # Extract current grade level | |
| grade_match = re.search(r'GRADE LEVEL:\s*12', text) # Adjust as needed | |
| grade_level = "12" if grade_match else "Unknown" | |
| return { | |
| 'student_info': student_info, | |
| 'courses': dict(courses_by_grade), | |
| 'gpa': gpa_data, | |
| 'grade_level': grade_level | |
| } | |
| def _parse_miami_dade(self, text: str) -> Dict[str, Union[Dict, List[Dict]]: | |
| """Parse standard Miami-Dade format""" | |
| courses = [] | |
| courses_by_grade = defaultdict(list) | |
| # Extract student info | |
| student_info = {} | |
| name_match = re.search(r'0783977 - ([^,]+),\s*([^\n]+)', text) | |
| if name_match: | |
| student_info['name'] = f"{name_match.group(2)} {name_match.group(1)}" | |
| # Course pattern for Miami-Dade | |
| course_pattern = re.compile( | |
| r'([A-Z]-[A-Za-z\s&]+)\s*\|\s*(\d{4}-\d{4})\s*\|\s*(\d{2})\s*\|\s*([A-Z0-9]+)\s*\|\s*([^\|]+)\s*\|\s*([^\|]+)\s*\|\s*([^\|]+)\s*\|\s*([A-Z]?)\s*\|\s*([A-Z]?)\s*\|\s*([^\|]+)', | |
| re.MULTILINE | |
| ) | |
| for match in course_pattern.finditer(text): | |
| grade_level = self.grade_level_map.get(match.group(3), match.group(3) | |
| credits = match.group(10).strip() | |
| course_info = { | |
| 'requirement_category': match.group(1).strip(), | |
| 'school_year': match.group(2), | |
| 'grade_level': grade_level if isinstance(grade_level, str) else f"Grade {match.group(3)}", | |
| 'course_code': match.group(4).strip(), | |
| 'name': match.group(5).strip(), | |
| 'term': match.group(6).strip(), | |
| 'district_number': match.group(7).strip(), | |
| 'grade': match.group(8), | |
| 'inclusion_status': match.group(9), | |
| 'credits': 0.0 if 'inProgress' in credits else float(credits.replace(' ', '')), | |
| 'transcript_type': 'miami_dade' | |
| } | |
| courses_by_grade[match.group(3)].append(course_info) | |
| # Extract GPA information | |
| gpa_data = { | |
| 'weighted': extract_gpa(text, 'Weighted GPA'), | |
| 'unweighted': extract_gpa(text, 'Un-weighted GPA') | |
| } | |
| # Extract current grade level | |
| grade_match = re.search(r'Current Grade:\s*(\d+)', text) | |
| grade_level = grade_match.group(1) if grade_match else "Unknown" | |
| return { | |
| 'student_info': student_info, | |
| 'courses': dict(courses_by_grade), | |
| 'gpa': gpa_data, | |
| 'grade_level': grade_level | |
| } | |
| def extract_gpa(text, gpa_type): | |
| pattern = rf'{gpa_type}\s*([\d\.]+)' | |
| match = re.search(pattern, text) | |
| return match.group(1) if match else "N/A" | |
| def parse_transcript(file): | |
| parser = UniversalTranscriptParser() | |
| if file.name.endswith('.pdf'): | |
| text = '' | |
| with pdfplumber.open(file.name) as pdf: | |
| for page in pdf.pages: | |
| text += page.extract_text() + '\n' | |
| parsed_data = parser.parse_transcript(text) | |
| # Prepare detailed output | |
| output_text = f"Student Transcript Summary\n{'='*40}\n" | |
| if 'student_info' in parsed_data and 'name' in parsed_data['student_info']: | |
| output_text += f"Student: {parsed_data['student_info']['name']}\n" | |
| output_text += f"Current Grade Level: {parsed_data.get('grade_level', 'Unknown')}\n" | |
| if 'gpa' in parsed_data: | |
| gpa = parsed_data['gpa'] | |
| output_text += f"Weighted GPA: {gpa.get('weighted', 'N/A')}\n" | |
| output_text += f"Unweighted GPA: {gpa.get('unweighted', 'N/A')}\n\n" | |
| output_text += "Course History:\n{'='*40}\n" | |
| if 'courses' in parsed_data: | |
| courses_by_grade = parsed_data['courses'] | |
| # Sort grades numerically (09, 10, 11, 12) or use original order | |
| try: | |
| grades_sorted = sorted(courses_by_grade.keys(), key=int) | |
| except: | |
| grades_sorted = sorted(courses_by_grade.keys()) | |
| for grade in grades_sorted: | |
| output_text += f"\nGrade {grade}:\n{'-'*30}\n" | |
| for course in courses_by_grade[grade]: | |
| output_text += f"- {course.get('name', 'Unnamed Course')}" | |
| if 'grade' in course and course['grade']: | |
| output_text += f" (Grade: {course['grade']})" | |
| if 'credits' in course: | |
| output_text += f" | Credits: {course['credits']}" | |
| if 'school_year' in course: | |
| output_text += f" | Year: {course['school_year']}" | |
| output_text += "\n" | |
| return output_text, parsed_data | |
| else: | |
| return "Unsupported file format (PDF only for transcript parsing)", 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:", | |
| "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 ========== | |
| 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 or "courses" not in transcript_dict: | |
| return "No course information available" | |
| display = "### Detailed Course History\n" | |
| courses_by_grade = transcript_dict["courses"] | |
| if isinstance(courses_by_grade, dict): | |
| # Sort grades numerically | |
| try: | |
| grades_sorted = sorted(courses_by_grade.keys(), key=int) | |
| except: | |
| grades_sorted = sorted(courses_by_grade.keys()) | |
| for grade in grades_sorted: | |
| display += f"\n**Grade {grade}**\n" | |
| for course in courses_by_grade[grade]: | |
| display += f"- {course.get('name', 'Unnamed Course')}" | |
| if 'grade' in course and course['grade']: | |
| display += f" (Grade: {course['grade']})" | |
| if 'credits' in course: | |
| display += f" | Credits: {course['credits']}" | |
| if 'school_year' in course: | |
| display += f" | Year: {course['school_year']}" | |
| display += "\n" | |
| if 'gpa' in transcript_dict: | |
| gpa = transcript_dict['gpa'] | |
| display += "\n**GPA Information**\n" | |
| display += f"- Unweighted: {gpa.get('unweighted', 'N/A')}\n" | |
| display += f"- Weighted: {gpa.get('weighted', 'N/A')}\n" | |
| return display | |
| # ========== 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", "") | |
| courses = profile.get("transcript", {}).get("courses", {}) | |
| # Common responses | |
| greetings = ["hi", "hello", "hey"] | |
| study_help = ["study", "learn", "prepare", "exam"] | |
| grade_help = ["grade", "gpa", "score"] | |
| interest_help = ["interest", "hobby", "passion"] | |
| course_help = ["courses", "classes", "transcript", "schedule"] | |
| 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 any(word in message.lower() for word in course_help): | |
| response = "Here's a summary of your courses:\n" | |
| if isinstance(courses, dict): | |
| try: | |
| grades_sorted = sorted(courses.keys(), key=int) | |
| except: | |
| grades_sorted = sorted(courses.keys()) | |
| for grade in grades_sorted: | |
| response += f"\nGrade {grade}:\n" | |
| for course in courses[grade]: | |
| response += f"- {course.get('name', 'Unnamed Course')}" | |
| if 'grade' in course: | |
| response += f" (Grade: {course['grade']})" | |
| response += "\n" | |
| else: | |
| response += "No detailed course information available." | |
| return response | |
| elif "help" in message.lower(): | |
| return ("I can help with:\n" | |
| "- Study tips based on your learning style\n" | |
| "- GPA and grade information\n" | |
| "- Course history and schedules\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"): | |
| gr.Markdown("### Upload your transcript (PDF recommended for best results)") | |
| transcript_file = gr.File(label="Transcript file", file_types=[".pdf"]) | |
| transcript_output = gr.Textbox(label="Transcript Results", lines=20) | |
| 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="Your Learning Style", lines=15) | |
| gr.Button("Submit Quiz").click( | |
| fn=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 | |
| ) | |
| 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?", | |
| "Show me my course history", | |
| "How can I improve my grades?" | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| app.launch() | |