import gradio as gr
import pandas as pd
import json
import os
import re
from PyPDF2 import PdfReader
from collections import defaultdict
from typing import Dict, List, Optional, Tuple, Union
import html
from pathlib import Path
import fitz # PyMuPDF
import pytesseract
from PIL import Image
import io
import secrets
import string
from huggingface_hub import HfApi, HfFolder
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import time
import logging
import asyncio
from functools import lru_cache
import hashlib
from concurrent.futures import ThreadPoolExecutor
from pydantic import BaseModel
# ========== CONFIGURATION ==========
PROFILES_DIR = "student_profiles"
ALLOWED_FILE_TYPES = [".pdf", ".png", ".jpg", ".jpeg"]
MAX_FILE_SIZE_MB = 5
MIN_AGE = 5
MAX_AGE = 120
SESSION_TOKEN_LENGTH = 32
HF_TOKEN = os.getenv("HF_TOKEN")
SESSION_TIMEOUT = 3600 # 1 hour session timeout
# Initialize logging
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
filename='transcript_parser.log'
)
# Model configuration - Using smaller model
MODEL_NAME = "deepseek-ai/deepseek-llm-1.3b"
# Initialize Hugging Face API
if HF_TOKEN:
try:
hf_api = HfApi(token=HF_TOKEN)
HfFolder.save_token(HF_TOKEN)
except Exception as e:
logging.error(f"Failed to initialize Hugging Face API: {str(e)}")
# ========== MODEL LOADER ==========
class ModelLoader:
def __init__(self):
self.model = None
self.tokenizer = None
self.loaded = False
self.loading = False
self.error = None
self.device = "cuda" if torch.cuda.is_available() else "cpu"
def load_model(self, progress: gr.Progress = None) -> Tuple[Optional[AutoModelForCausalLM], Optional[AutoTokenizer]]:
"""Lazy load the model with progress feedback"""
try:
if progress:
progress(0.1, desc="Checking GPU availability...")
torch.cuda.empty_cache()
if progress:
progress(0.2, desc="Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(
MODEL_NAME,
trust_remote_code=True
)
if progress:
progress(0.5, desc="Loading model (this may take a few minutes)...")
model_kwargs = {
"trust_remote_code": True,
"torch_dtype": torch.float16 if self.device == "cuda" else torch.float32,
"device_map": "auto" if self.device == "cuda" else None,
"low_cpu_mem_usage": True,
"offload_folder": "offload"
}
try:
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
**model_kwargs
)
except torch.cuda.OutOfMemoryError:
model_kwargs["device_map"] = None
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
**model_kwargs
).to('cpu')
self.device = 'cpu'
test_input = tokenizer("Test", return_tensors="pt").to(self.device)
_ = model.generate(**test_input, max_new_tokens=1)
self.model = model.eval()
self.tokenizer = tokenizer
self.loaded = True
return model, tokenizer
except Exception as e:
self.error = f"Model loading failed: {str(e)}"
logging.error(self.error)
return None, None
# Initialize model loader
model_loader = ModelLoader()
@lru_cache(maxsize=1)
def get_model_and_tokenizer():
return model_loader.load_model()
# ========== UTILITY FUNCTIONS ==========
def generate_session_token() -> str:
alphabet = string.ascii_letters + string.digits
return ''.join(secrets.choice(alphabet) for _ in range(SESSION_TOKEN_LENGTH))
def sanitize_input(text: str) -> str:
if not text:
return ""
text = html.escape(text.strip())
text = re.sub(r'<[^>]*>', '', text)
text = re.sub(r'[^\w\s\-.,!?@#\$%^&*()+=]', '', text)
return text
def validate_name(name: str) -> str:
name = name.strip()
if not name:
raise ValueError("Name cannot be empty.")
if len(name) > 100:
raise ValueError("Name is too long (maximum 100 characters).")
if any(c.isdigit() for c in name):
raise ValueError("Name cannot contain numbers.")
return name
def validate_age(age: Union[int, float, str]) -> int:
try:
age_int = int(age)
if not MIN_AGE <= age_int <= MAX_AGE:
raise ValueError(f"Age must be between {MIN_AGE} and {MAX_AGE}.")
return age_int
except (ValueError, TypeError):
raise ValueError("Please enter a valid age number.")
def validate_file(file_obj) -> None:
if not file_obj:
raise ValueError("Please upload a file first")
file_ext = os.path.splitext(file_obj.name)[1].lower()
if file_ext not in ALLOWED_FILE_TYPES:
raise ValueError(f"Invalid file type. Allowed types: {', '.join(ALLOWED_FILE_TYPES)}")
file_size = os.path.getsize(file_obj.name) / (1024 * 1024)
if file_size > MAX_FILE_SIZE_MB:
raise ValueError(f"File too large. Maximum size is {MAX_FILE_SIZE_MB}MB.")
# ========== TEXT EXTRACTION FUNCTIONS ==========
def extract_text_from_file(file_path: str, file_ext: str) -> str:
text = ""
try:
if file_ext == '.pdf':
try:
# First try pdfplumber for better table extraction
import pdfplumber
with pdfplumber.open(file_path) as pdf:
for page in pdf.pages:
text += page.extract_text() + '\n'
if not text.strip():
raise ValueError("PDFPlumber returned empty text")
except Exception as e:
logging.warning(f"PDFPlumber failed: {str(e)}. Trying PyMuPDF...")
doc = fitz.open(file_path)
for page in doc:
text += page.get_text("text") + '\n'
if not text.strip():
logging.warning("PyMuPDF returned empty text, trying OCR fallback...")
text = extract_text_from_pdf_with_ocr(file_path)
elif file_ext in ['.png', '.jpg', '.jpeg']:
text = extract_text_with_ocr(file_path)
text = clean_extracted_text(text)
if not text.strip():
raise ValueError("No text could be extracted.")
return text
except Exception as e:
logging.error(f"Text extraction error: {str(e)}")
raise gr.Error(f"Failed to extract text: {str(e)}\n\nPossible solutions:\n1. Try a different file format\n2. Ensure text is clear and not handwritten\n3. Check file size (<5MB)")
def extract_text_from_pdf_with_ocr(file_path: str) -> str:
try:
import pdf2image
images = pdf2image.convert_from_path(file_path, dpi=300)
custom_config = r'--oem 3 --psm 6 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;()-/ '
text = ""
for i, img in enumerate(images):
# Pre-process image
img = img.convert('L') # Grayscale
img = img.point(lambda x: 0 if x < 140 else 255) # Increase contrast
# OCR with retry logic
try:
page_text = pytesseract.image_to_string(img, config=custom_config)
if len(page_text.strip()) > 20: # Minimum viable text
text += f"PAGE {i+1}:\n{page_text}\n\n"
except Exception as e:
logging.warning(f"OCR failed on page {i+1}: {str(e)}")
return text if text else "No readable text found"
except Exception as e:
raise ValueError(f"OCR processing failed: {str(e)}")
def extract_text_with_ocr(file_path: str) -> str:
try:
image = Image.open(file_path)
image = image.convert('L')
image = image.point(lambda x: 0 if x < 128 else 255, '1')
custom_config = r'--oem 3 --psm 6'
text = pytesseract.image_to_string(image, config=custom_config)
return text
except Exception as e:
raise ValueError(f"OCR processing failed: {str(e)}")
def clean_extracted_text(text: str) -> str:
text = re.sub(r'\s+', ' ', text).strip()
replacements = {
'|': 'I',
'‘': "'",
'’': "'",
'“': '"',
'”': '"',
'fi': 'fi',
'fl': 'fl'
}
for wrong, right in replacements.items():
text = text.replace(wrong, right)
return text
def remove_sensitive_info(text: str) -> str:
text = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[REDACTED]', text)
text = re.sub(r'\b\d{6,9}\b', '[ID]', text)
text = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z9.-]+\.[A-Z|a-z]{2,}\b', '[EMAIL]', text)
return text
# ========== TRANSCRIPT PARSING ==========
class Course(BaseModel):
requirement: str
school_year: str
grade_level: str
course_code: str
description: str
term: str
district_number: str
fg: str
included: str
credits: str
class GraduationProgress(BaseModel):
student_name: str
student_id: str
current_grade: str
year_of_graduation: str
unweighted_gpa: float
weighted_gpa: float
community_service_hours: int
community_service_date: str
total_credits_earned: float
virtual_grade: str
requirements: Dict[str, Dict[str, float]]
courses: List[Course]
assessments: Dict[str, str]
class TranscriptParser:
def __init__(self):
self.student_data = {}
self.requirements = {}
self.current_courses = []
self.course_history = []
self.graduation_status = {}
def parse_transcript(self, text: str) -> Dict:
"""Parse transcript text and return structured data"""
try:
# First try the new detailed parser
parsed_data = self._parse_detailed_transcript(text)
if parsed_data:
return parsed_data
# Fall back to simplified parser if detailed parsing fails
return self._parse_simplified_transcript(text)
except Exception as e:
logging.error(f"Error parsing transcript: {str(e)}")
raise ValueError(f"Couldn't parse transcript: {str(e)}")
def _parse_detailed_transcript(self, text: str) -> Optional[Dict]:
"""Parse detailed transcript format"""
try:
parsed_data = {
'student_info': {},
'requirements': {},
'course_history': [],
'assessments': {}
}
# Extract student info
student_info_match = re.search(r"(\d{7}) - (.*?)\n", text)
if student_info_match:
parsed_data['student_info']['id'] = student_info_match.group(1)
parsed_data['student_info']['name'] = student_info_match.group(2).strip()
current_grade_match = re.search(r"Current Grade: (\d+)", text)
if current_grade_match:
parsed_data['student_info']['grade'] = current_grade_match.group(1)
yog_match = re.search(r"YOG (\d{4})", text)
if yog_match:
parsed_data['student_info']['year_of_graduation'] = yog_match.group(1)
unweighted_gpa_match = re.search(r"Un-weighted GPA (\d+\.\d+)", text)
if unweighted_gpa_match:
parsed_data['student_info']['unweighted_gpa'] = float(unweighted_gpa_match.group(1))
weighted_gpa_match = re.search(r"Weighted GPA (\d+\.\d+)", text)
if weighted_gpa_match:
parsed_data['student_info']['weighted_gpa'] = float(weighted_gpa_match.group(1))
service_hours_match = re.search(r"Comm Serv Hours (\d+)", text)
if service_hours_match:
parsed_data['student_info']['community_service_hours'] = int(service_hours_match.group(1))
service_date_match = re.search(r"Comm Serv Date (\d{2}/\d{2}/\d{4})", text)
if service_date_match:
parsed_data['student_info']['community_service_date'] = service_date_match.group(1)
credits_match = re.search(r"Total Credits Earned (\d+\.\d+)", text)
if credits_match:
parsed_data['student_info']['total_credits'] = float(credits_match.group(1))
virtual_grade_match = re.search(r"Virtual Grade (\w+)", text)
if virtual_grade_match:
parsed_data['student_info']['virtual_grade'] = virtual_grade_match.group(1)
# Extract requirements
req_pattern = re.compile(r"([A-Z]-.*?)\s*\|\s*(.*?)\s*\|\s*(\d+\.\d+)\s*\|\s*(\d+\.\d+)\s*\|\s*(\d+\.\d+)\s*\|\s*(\d+) %")
for match in req_pattern.finditer(text):
code = match.group(1).strip()
desc = match.group(2).strip()
required = float(match.group(3))
waived = float(match.group(4))
completed = float(match.group(5))
percent = float(match.group(6))
parsed_data['requirements'][code] = {
"description": desc,
"required": required,
"waived": waived,
"completed": completed,
"percent_complete": percent
}
# Extract assessments
assess_pattern = re.compile(r"Z-Assessment: (.*?)\s*\|\s*(.*?)\s*\|\s*(\w+)\s*\|\s*(\d+) %")
for match in assess_pattern.finditer(text):
name = f"Assessment: {match.group(1)}"
status = match.group(3)
parsed_data['assessments'][name] = status
for z_item in ["Community Service Hours", "GPA"]:
if re.search(fr"Z-{z_item.replace(' ', '.*?')}\s*\|\s*(.*?)\s*\|\s*(\w+)\s*\|\s*(\d+) %", text):
status = re.search(fr"Z-{z_item.replace(' ', '.*?')}\s*\|\s*(.*?)\s*\|\s*(\w+)\s*\|\s*(\d+) %", text).group(2)
parsed_data['assessments'][z_item] = status
# Extract courses (simplified for now - can be enhanced)
course_pattern = r'([A-Z]{2,4}\s?\d{3})\s+(.*?)\s+([A-F][+-]?)\s+([0-9.]+)'
courses = re.findall(course_pattern, text)
for course in courses:
parsed_data['course_history'].append({
'course_code': course[0],
'description': course[1],
'grade': course[2],
'credits': float(course[3])
})
return parsed_data
except Exception as e:
logging.warning(f"Detailed transcript parsing failed, falling back to simple parser: {str(e)}")
return None
def _parse_simplified_transcript(self, text: str) -> Dict:
"""Fallback simplified transcript parser with multiple pattern attempts"""
patterns = [
(r'(?:Course|Subject)\s*Code.*?Grade.*?Credits(.*?)(?:\n\s*\n|\Z)', 'table'),
(r'([A-Z]{2,4}\s?\d{3}[A-Z]?)\s+(.*?)\s+([A-F][+-]?)\s+(\d+\.?\d*)', 'line'),
(r'(.*?)\s+([A-F][+-]?)\s+(\d+\.?\d*)', 'minimal')
]
for pattern, pattern_type in patterns:
try:
if pattern_type == 'table':
# Parse tabular data
courses = re.findall(r'([A-Z]{2,4}\s?\d{3}[A-Z]?)\s+(.*?)\s+([A-F][+-]?)\s+(\d+\.?\d*)',
re.search(pattern, text, re.DOTALL).group(1))
elif pattern_type == 'line':
courses = re.findall(pattern, text)
else:
courses = re.findall(pattern, text)
if courses:
parsed_data = {'course_history': []}
for course in courses:
parsed_data['course_history'].append({
'course_code': course[0].strip(),
'description': course[1].strip() if len(course) > 1 else '',
'grade': course[2].strip() if len(course) > 2 else '',
'credits': float(course[3]) if len(course) > 3 else 0.0
})
return parsed_data
except:
continue
raise ValueError("Could not identify course information in transcript")
def parse_transcript(file_obj, progress=gr.Progress()) -> Tuple[str, Optional[Dict]]:
"""Process transcript file and return simple confirmation"""
try:
if not file_obj:
raise gr.Error("Please upload a transcript file first (PDF or image)")
validate_file(file_obj)
file_ext = os.path.splitext(file_obj.name)[1].lower()
# Additional PDF validation
if file_ext == '.pdf':
try:
with open(file_obj.name, 'rb') as f:
PdfReader(f) # Test if PDF is readable
except Exception as e:
raise gr.Error(f"Invalid PDF file: {str(e)}. Please upload a non-corrupted PDF.")
if progress:
progress(0.2, desc="Extracting text from file...")
try:
text = extract_text_from_file(file_obj.name, file_ext)
except Exception as e:
raise ValueError(f"Failed to extract text: {str(e)}. The file may be corrupted or in an unsupported format.")
if not text.strip():
raise ValueError("The file appears to be empty or contains no readable text.")
if progress:
progress(0.5, desc="Parsing transcript...")
parser = TranscriptParser()
try:
parsed_data = parser.parse_transcript(text)
except Exception as e:
raise ValueError(f"Couldn't parse transcript content. Error: {str(e)}")
confirmation = "Transcript processed successfully."
if 'gpa' in parsed_data.get('student_info', {}):
confirmation += f"\nGPA detected: {parsed_data['student_info']['gpa']}"
return confirmation, parsed_data
except Exception as e:
error_msg = f"Error processing transcript: {str(e)}"
logging.error(error_msg)
raise gr.Error(f"{error_msg}\n\nPossible solutions:\n1. Try a different file format\n2. Ensure text is clear and not handwritten\n3. Check file size (<5MB)")
# ========== LEARNING STYLE QUIZ ==========
class LearningStyleQuiz:
def __init__(self):
self.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:"
]
self.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)"]
]
self.learning_styles = {
"Visual": {
"description": "Visual learners prefer using images, diagrams, and spatial understanding.",
"tips": [
"Use color coding in your notes",
"Create mind maps and diagrams",
"Watch educational videos",
"Use flashcards with images",
"Highlight important information in different colors"
],
"careers": [
"Graphic Designer", "Architect", "Photographer",
"Engineer", "Surgeon", "Pilot"
]
},
"Auditory": {
"description": "Auditory learners learn best through listening and speaking.",
"tips": [
"Record lectures and listen to them",
"Participate in study groups",
"Explain concepts out loud to yourself",
"Use rhymes or songs to remember information",
"Listen to educational podcasts"
],
"careers": [
"Musician", "Journalist", "Lawyer",
"Psychologist", "Teacher", "Customer Service"
]
},
"Reading/Writing": {
"description": "These learners prefer information displayed as words.",
"tips": [
"Write detailed notes",
"Create summaries in your own words",
"Read textbooks and articles",
"Make lists to organize information",
"Rewrite your notes to reinforce learning"
],
"careers": [
"Writer", "Researcher", "Editor",
"Accountant", "Programmer", "Historian"
]
},
"Kinesthetic": {
"description": "Kinesthetic learners learn through movement and hands-on activities.",
"tips": [
"Use hands-on activities",
"Take frequent movement breaks",
"Create physical models",
"Associate information with physical actions",
"Study while walking or pacing"
],
"careers": [
"Athlete", "Chef", "Mechanic",
"Dancer", "Physical Therapist", "Carpenter"
]
}
}
def evaluate_quiz(self, *answers) -> str:
"""Evaluate quiz answers and return learning style results"""
answers = list(answers)
if len(answers) != len(self.questions):
raise gr.Error("Please answer all questions before submitting")
scores = {style: 0 for style in self.learning_styles}
for i, answer in enumerate(answers):
if not answer:
continue
for j, style in enumerate(self.learning_styles):
if answer == self.options[i][j]:
scores[style] += 1
break
total_answered = sum(1 for ans in answers if ans)
if total_answered == 0:
raise gr.Error("No answers provided")
percentages = {style: (score/total_answered)*100 for style, score in scores.items()}
sorted_styles = sorted(scores.items(), key=lambda x: x[1], reverse=True)
result = "## Your Learning Style Results\n\n"
result += "### Scores:\n"
for style, score in sorted_styles:
result += f"- **{style}**: {score}/{total_answered} ({percentages[style]:.1f}%)\n"
max_score = max(scores.values())
primary_styles = [style for style, score in scores.items() if score == max_score]
result += "\n### Analysis:\n"
if len(primary_styles) == 1:
primary_style = primary_styles[0]
style_info = self.learning_styles[primary_style]
result += f"Your primary learning style is **{primary_style}**\n\n"
result += f"**{primary_style} Characteristics**:\n"
result += f"{style_info['description']}\n\n"
result += "**Recommended Study Strategies**:\n"
for tip in style_info['tips']:
result += f"- {tip}\n"
result += "\n**Potential Career Paths**:\n"
for career in style_info['careers'][:6]:
result += f"- {career}\n"
complementary = [s for s in sorted_styles if s[0] != primary_style][0][0]
result += f"\nYou might also benefit from some **{complementary}** strategies:\n"
for tip in self.learning_styles[complementary]['tips'][:3]:
result += f"- {tip}\n"
else:
result += "You have multiple strong learning styles:\n"
for style in primary_styles:
result += f"- **{style}**\n"
result += "\n**Combined Learning Strategies**:\n"
result += "You may benefit from combining different learning approaches:\n"
for style in primary_styles:
result += f"\n**{style}** techniques:\n"
for tip in self.learning_styles[style]['tips'][:2]:
result += f"- {tip}\n"
result += f"\n**{style}** career suggestions:\n"
for career in self.learning_styles[style]['careers'][:3]:
result += f"- {career}\n"
return result
learning_style_quiz = LearningStyleQuiz()
# ========== PROFILE MANAGEMENT ==========
class ProfileManager:
def __init__(self):
self.profiles_dir = Path(PROFILES_DIR)
self.profiles_dir.mkdir(exist_ok=True, parents=True)
self.current_session = None
def set_session(self, session_token: str) -> None:
self.current_session = session_token
def get_profile_path(self, name: str) -> Path:
if self.current_session:
name_hash = hashlib.sha256(name.encode()).hexdigest()[:16]
return self.profiles_dir / f"{name_hash}_{self.current_session}_profile.json"
return self.profiles_dir / f"{name.replace(' ', '_')}_profile.json"
def save_profile(self, name: str, age: Union[int, str], interests: str,
transcript: Dict, learning_style: str,
movie: str, movie_reason: str, show: str, show_reason: str,
book: str, book_reason: str, character: str, character_reason: str,
blog: str) -> str:
try:
name = validate_name(name)
age = validate_age(age)
if not interests.strip():
raise ValueError("Please describe at least one interest or hobby.")
if not transcript:
raise ValueError("Please complete the transcript analysis first.")
if not learning_style or "Your primary learning style is:" not in learning_style:
raise ValueError("Please complete the learning style quiz first.")
favorites = {
"movie": sanitize_input(movie),
"movie_reason": sanitize_input(movie_reason),
"show": sanitize_input(show),
"show_reason": sanitize_input(show_reason),
"book": sanitize_input(book),
"book_reason": sanitize_input(book_reason),
"character": sanitize_input(character),
"character_reason": sanitize_input(character_reason)
}
data = {
"name": name,
"age": age,
"interests": sanitize_input(interests),
"transcript": transcript,
"learning_style": learning_style,
"favorites": favorites,
"blog": sanitize_input(blog) if blog else "",
"session_token": self.current_session,
"last_updated": time.time()
}
filepath = self.get_profile_path(name)
with open(filepath, "w", encoding='utf-8') as f:
json.dump(data, f, indent=2, ensure_ascii=False)
if HF_TOKEN and 'hf_api' in globals():
try:
hf_api.upload_file(
path_or_fileobj=filepath,
path_in_repo=f"profiles/{filepath.name}",
repo_id="your-username/student-learning-assistant",
repo_type="dataset"
)
except Exception as e:
logging.error(f"Failed to upload to HF Hub: {str(e)}")
# Return simple confirmation with GPA if available
confirmation = f"Profile saved successfully for {name}."
if 'gpa' in data.get('transcript', {}).get('student_info', {}):
confirmation += f"\nGPA: {data['transcript']['student_info']['gpa']}"
return confirmation
except Exception as e:
logging.error(f"Profile validation error: {str(e)}")
raise gr.Error(f"Couldn't save profile: {str(e)}")
def load_profile(self, name: str = None, session_token: str = None) -> Dict:
try:
if session_token:
profile_pattern = f"*{session_token}_profile.json"
else:
profile_pattern = "*.json"
profiles = list(self.profiles_dir.glob(profile_pattern))
if not profiles:
return {}
if name:
name_hash = hashlib.sha256(name.encode()).hexdigest()[:16]
if session_token:
profile_file = self.profiles_dir / f"{name_hash}_{session_token}_profile.json"
else:
profile_file = self.profiles_dir / f"{name_hash}_profile.json"
if not profile_file.exists():
if HF_TOKEN and 'hf_api' in globals():
try:
hf_api.download_file(
path_in_repo=f"profiles/{profile_file.name}",
repo_id="your-username/student-learning-assistant",
repo_type="dataset",
local_dir=self.profiles_dir
)
except:
raise gr.Error(f"No profile found for {name}")
else:
raise gr.Error(f"No profile found for {name}")
else:
profile_file = profiles[0]
with open(profile_file, "r", encoding='utf-8') as f:
profile_data = json.load(f)
if time.time() - profile_data.get('last_updated', 0) > SESSION_TIMEOUT:
raise gr.Error("Session expired. Please start a new session.")
return profile_data
except Exception as e:
logging.error(f"Error loading profile: {str(e)}")
return {}
def list_profiles(self, session_token: str = None) -> List[str]:
if session_token:
profiles = list(self.profiles_dir.glob(f"*{session_token}_profile.json"))
else:
profiles = list(self.profiles_dir.glob("*.json"))
profile_names = []
for p in profiles:
with open(p, "r", encoding='utf-8') as f:
try:
data = json.load(f)
profile_names.append(data.get('name', p.stem))
except json.JSONDecodeError:
continue
return profile_names
profile_manager = ProfileManager()
# ========== AI TEACHING ASSISTANT ==========
class TeachingAssistant:
def __init__(self):
self.context_history = []
self.max_context_length = 5
async def generate_response(self, message: str, history: List[List[Union[str, None]]], session_token: str) -> str:
try:
profile = profile_manager.load_profile(session_token=session_token)
if not profile:
return "Please complete and save your profile first."
self._update_context(message, history)
# Focus on GPA if mentioned
if "gpa" in message.lower():
gpa = profile.get("transcript", {}).get("student_info", {}).get("gpa", "unknown")
return f"Your GPA is {gpa}. Would you like advice on improving it?"
# Generic response otherwise
return "I'm your learning assistant. Ask me about your GPA, courses, or study tips."
except Exception as e:
logging.error(f"Error generating response: {str(e)}")
return "I encountered an error. Please try again."
def _update_context(self, message: str, history: List[List[Union[str, None]]]) -> None:
self.context_history.append({"role": "user", "content": message})
if history:
for h in history[-self.max_context_length:]:
if h[0]:
self.context_history.append({"role": "user", "content": h[0]})
if h[1]:
self.context_history.append({"role": "assistant", "content": h[1]})
self.context_history = self.context_history[-(self.max_context_length*2):]
teaching_assistant = TeachingAssistant()
# ========== GRADIO INTERFACE ==========
def create_interface():
with gr.Blocks(theme=gr.themes.Soft(), title="Student Learning Assistant") as app:
session_token = gr.State(value=generate_session_token())
profile_manager.set_session(session_token.value)
tab_completed = gr.State({
0: False, # Transcript Upload
1: False, # Learning Style Quiz
2: False, # Personal Questions
3: False, # Save & Review
4: False # AI Assistant
})
# Custom CSS
app.css = """
.gradio-container { max-width: 1200px !important; margin: 0 auto !important; }
.tab-content { padding: 20px !important; border: 1px solid #e0e0e0 !important; border-radius: 8px !important; margin-top: 10px !important; }
.completed-tab { background: #4CAF50 !important; color: white !important; }
.incomplete-tab { background: #E0E0E0 !important; }
.nav-message { padding: 10px; margin: 10px 0; border-radius: 4px; background-color: #ffebee; color: #c62828; }
.file-upload { border: 2px dashed #4CAF50 !important; padding: 20px !important; border-radius: 8px !important; text-align: center; }
.file-upload:hover { background: #f5f5f5; }
.progress-bar { height: 5px; background: linear-gradient(to right, #4CAF50, #8BC34A); margin-bottom: 15px; border-radius: 3px; }
.quiz-question { margin-bottom: 15px; padding: 15px; background: #f5f5f5; border-radius: 5px; }
.quiz-results { margin-top: 20px; padding: 20px; background: #e8f5e9; border-radius: 8px; }
.error-message { color: #d32f2f; background-color: #ffebee; padding: 10px; border-radius: 4px; margin: 10px 0; }
.transcript-results { border-left: 4px solid #4CAF50 !important; padding: 15px !important; background: #f8f8f8 !important; }
.error-box { border: 1px solid #ff4444 !important; background: #fff8f8 !important; }
.dark .tab-content { background-color: #2d2d2d !important; border-color: #444 !important; }
.dark .quiz-question { background-color: #3d3d3d !important; }
.dark .quiz-results { background-color: #2e3d2e !important; }
.dark textarea, .dark input { background-color: #333 !important; color: #eee !important; }
.dark .output-markdown { color: #eee !important; }
.dark .chatbot { background-color: #333 !important; }
.dark .chatbot .user, .dark .chatbot .assistant { color: #eee !important; }
"""
# Header
with gr.Row():
with gr.Column(scale=4):
gr.Markdown("""
# Student Learning Assistant
**Your personalized education companion**
Complete each step to get customized learning recommendations.
""")
with gr.Column(scale=1):
dark_mode = gr.Checkbox(label="Dark Mode", value=False)
# Navigation buttons
with gr.Row():
with gr.Column(scale=1, min_width=100):
step1 = gr.Button("1. Transcript", elem_classes="incomplete-tab")
with gr.Column(scale=1, min_width=100):
step2 = gr.Button("2. Quiz", elem_classes="incomplete-tab", interactive=False)
with gr.Column(scale=1, min_width=100):
step3 = gr.Button("3. Profile", elem_classes="incomplete-tab", interactive=False)
with gr.Column(scale=1, min_width=100):
step4 = gr.Button("4. Review", elem_classes="incomplete-tab", interactive=False)
with gr.Column(scale=1, min_width=100):
step5 = gr.Button("5. Assistant", elem_classes="incomplete-tab", interactive=False)
nav_message = gr.HTML(visible=False)
# Main tabs
with gr.Tabs(visible=True) as tabs:
# ===== TAB 1: TRANSCRIPT UPLOAD =====
with gr.Tab("Transcript", id=0):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Step 1: Upload Your Transcript")
with gr.Group(elem_classes="file-upload"):
file_input = gr.File(
label="Drag and drop your transcript here (PDF or Image)",
file_types=ALLOWED_FILE_TYPES,
type="filepath"
)
upload_btn = gr.Button("Analyze Transcript", variant="primary")
file_error = gr.HTML(visible=False)
with gr.Column(scale=2):
transcript_output = gr.Textbox(
label="Analysis Results",
lines=5,
interactive=False,
elem_classes="transcript-results"
)
transcript_data = gr.State()
file_input.change(
fn=lambda f: (
gr.update(visible=False),
gr.update(value="File ready for analysis!", visible=True) if f
else gr.update(value="Please upload a file", visible=False)
),
inputs=file_input,
outputs=[file_error, transcript_output]
)
upload_btn.click(
fn=parse_transcript,
inputs=[file_input, tab_completed],
outputs=[transcript_output, transcript_data]
).then(
fn=lambda: {0: True},
inputs=None,
outputs=tab_completed
).then(
fn=lambda: gr.update(elem_classes="completed-tab"),
outputs=step1
).then(
fn=lambda: gr.update(interactive=True),
outputs=step2
)
# ===== TAB 2: LEARNING STYLE QUIZ =====
with gr.Tab("Learning Style Quiz", id=1):
with gr.Column():
gr.Markdown("### Step 2: Discover Your Learning Style")
progress = gr.HTML("")
quiz_components = []
with gr.Accordion("Quiz Questions", open=True):
for i, (question, options) in enumerate(zip(learning_style_quiz.questions, learning_style_quiz.options)):
with gr.Group(elem_classes="quiz-question"):
q = gr.Radio(
options,
label=f"{i+1}. {question}",
show_label=True
)
quiz_components.append(q)
with gr.Row():
quiz_submit = gr.Button("Submit Quiz", variant="primary")
quiz_clear = gr.Button("Clear Answers")
quiz_alert = gr.HTML(visible=False)
learning_output = gr.Markdown(
label="Your Learning Style Results",
visible=False,
elem_classes="quiz-results"
)
for component in quiz_components:
component.change(
fn=lambda *answers: {
progress: gr.HTML(
f""
)
},
inputs=quiz_components,
outputs=progress
)
quiz_submit.click(
fn=lambda *answers: learning_style_quiz.evaluate_quiz(*answers),
inputs=quiz_components,
outputs=learning_output
).then(
fn=lambda: gr.update(visible=True),
outputs=learning_output
).then(
fn=lambda: {1: True},
inputs=None,
outputs=tab_completed
).then(
fn=lambda: gr.update(elem_classes="completed-tab"),
outputs=step2
).then(
fn=lambda: gr.update(interactive=True),
outputs=step3
)
quiz_clear.click(
fn=lambda: [None] * len(quiz_components),
outputs=quiz_components
).then(
fn=lambda: gr.HTML(""),
outputs=progress
)
# ===== TAB 3: PERSONAL QUESTIONS =====
with gr.Tab("Personal Profile", id=2):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Step 3: Tell Us About Yourself")
with gr.Group():
name = gr.Textbox(label="Full Name", placeholder="Your name")
age = gr.Number(label="Age", minimum=MIN_AGE, maximum=MAX_AGE, precision=0)
interests = gr.Textbox(
label="Your Interests/Hobbies",
placeholder="e.g., Science, Music, Sports, Art..."
)
save_personal_btn = gr.Button("Save Information", variant="primary")
save_confirmation = gr.HTML(visible=False)
with gr.Column(scale=1):
gr.Markdown("### Favorites")
with gr.Group():
movie = gr.Textbox(label="Favorite Movie")
movie_reason = gr.Textbox(label="Why do you like it?", lines=2)
show = gr.Textbox(label="Favorite TV Show")
show_reason = gr.Textbox(label="Why do you like it?", lines=2)
book = gr.Textbox(label="Favorite Book")
book_reason = gr.Textbox(label="Why do you like it?", lines=2)
character = gr.Textbox(label="Favorite Character (from any story)")
character_reason = gr.Textbox(label="Why do you like them?", lines=2)
with gr.Accordion("Personal Blog (Optional)", open=False):
blog = gr.Textbox(
label="Share your thoughts",
placeholder="Write something about yourself...",
lines=5
)
save_personal_btn.click(
fn=lambda n, a, i, ts: (
{2: True},
gr.update(elem_classes="completed-tab"),
gr.update(interactive=True),
gr.update(value="Information saved!
", visible=True)
),
inputs=[name, age, interests, tab_completed],
outputs=[tab_completed, step3, step4, save_confirmation]
)
# ===== TAB 4: SAVE & REVIEW =====
with gr.Tab("Save Profile", id=3):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Step 4: Review & Save Your Profile")
with gr.Group():
load_profile_dropdown = gr.Dropdown(
label="Load Existing Profile",
choices=profile_manager.list_profiles(session_token.value),
visible=False
)
with gr.Row():
load_btn = gr.Button("Load", visible=False)
delete_btn = gr.Button("Delete", variant="stop", visible=False)
save_btn = gr.Button("Save Profile", variant="primary")
clear_btn = gr.Button("Clear Form")
with gr.Column(scale=2):
output_summary = gr.Markdown(
"Your profile summary will appear here after saving.",
label="Profile Summary"
)
save_btn.click(
fn=profile_manager.save_profile,
inputs=[
name, age, interests, transcript_data, learning_output,
movie, movie_reason, show, show_reason,
book, book_reason, character, character_reason, blog
],
outputs=output_summary
).then(
fn=lambda: {3: True},
inputs=None,
outputs=tab_completed
).then(
fn=lambda: gr.update(elem_classes="completed-tab"),
outputs=step4
).then(
fn=lambda: gr.update(interactive=True),
outputs=step5
).then(
fn=lambda: profile_manager.list_profiles(session_token.value),
outputs=load_profile_dropdown
).then(
fn=lambda: gr.update(visible=bool(profile_manager.list_profiles(session_token.value))),
outputs=load_btn
).then(
fn=lambda: gr.update(visible=bool(profile_manager.list_profiles(session_token.value))),
outputs=delete_btn
)
# ===== TAB 5: AI ASSISTANT =====
with gr.Tab("AI Assistant", id=4):
gr.Markdown("## Your Personalized Learning Assistant")
gr.Markdown("Ask me anything about studying, your courses, grades, or learning strategies.")
async def chat_wrapper(message: str, history: List[List[str]]):
response = await teaching_assistant.generate_response(
message,
history,
session_token.value
)
return response
chatbot = gr.ChatInterface(
fn=chat_wrapper,
examples=[
"What's my GPA?",
"How should I study for math?",
"What courses am I taking?",
"Study tips for my learning style"
],
title=""
)
# Navigation logic
def navigate_to_tab(tab_index: int, tab_completed_status):
current_tab = tabs.selected
if tab_index <= current_tab:
return gr.Tabs(selected=tab_index), gr.update(visible=False)
# Check all previous tabs are completed
for i in range(tab_index):
if not tab_completed_status.get(i, False):
messages = [
"Please complete the transcript analysis first",
"Please complete the learning style quiz first",
"Please fill out your personal information first",
"Please save your profile first"
]
return (
gr.Tabs(selected=i),
gr.update(
value=f"⛔ {messages[i]}
",
visible=True
)
)
return gr.Tabs(selected=tab_index), gr.update(visible=False)
step1.click(
lambda idx, status: navigate_to_tab(idx, status),
inputs=[gr.State(0), tab_completed],
outputs=[tabs, nav_message]
)
step2.click(
lambda idx, status: navigate_to_tab(idx, status),
inputs=[gr.State(1), tab_completed],
outputs=[tabs, nav_message]
)
step3.click(
lambda idx, status: navigate_to_tab(idx, status),
inputs=[gr.State(2), tab_completed],
outputs=[tabs, nav_message]
)
step4.click(
lambda idx, status: navigate_to_tab(idx, status),
inputs=[gr.State(3), tab_completed],
outputs=[tabs, nav_message]
)
step5.click(
lambda idx, status: navigate_to_tab(idx, status),
inputs=[gr.State(4), tab_completed],
outputs=[tabs, nav_message]
)
# Dark mode toggle
def toggle_dark_mode(dark):
return gr.themes.Soft(primary_hue="blue", secondary_hue="gray") if not dark else gr.themes.Soft(primary_hue="blue", secondary_hue="gray", neutral_hue="slate")
dark_mode.change(
fn=toggle_dark_mode,
inputs=dark_mode,
outputs=None
)
# Load model on startup
app.load(fn=lambda: model_loader.load_model(), outputs=[])
return app
app = create_interface()
if __name__ == "__main__":
app.launch()