Update app.py

app.py

@@ -1,293 +1,154 @@
-# ========== DEPENDENCY MANAGEMENT ==========
-import sys
-import subprocess
-import importlib
-from datetime import datetime
-import re
-import os
-import json
-import pdfplumber
-from collections import defaultdict
-from typing import List, Dict, Union
 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
 
-required_packages = {
-    'gradio': 'gradio>=3.0',
-    'pandas': 'pandas',
-    'PyPDF2': 'PyPDF2',
-    'transformers': 'transformers',
-    'pdfplumber': '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
 
-def check_and_install_packages():
-    missing_packages = []
-    for import_name, pkg_name in required_packages.items():
-        try:
-            importlib.import_module(import_name)
-        except ImportError:
-            missing_packages.append(pkg_name)
+# ========== IMPROVED TRANSCRIPT PARSING ==========
+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"
 
-    if missing_packages:
-        print(f"Missing packages: {', '.join(missing_packages)}")
-        subprocess.check_call([sys.executable, "-m", "pip", "install", *missing_packages])
-
-check_and_install_packages()
-
-# ========== TRANSCRIPT PARSING ==========
-class UniversalTranscriptParser:
-    def __init__(self):
-        self.patterns = {
-            'miami_dade': self._compile_miami_dade_patterns(),
-            'homeschool': self._compile_homeschool_patterns(),
-            'doral_academy': self._compile_doral_academy_patterns()
-        }
-        
-        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]]]:
-        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:
-            return self._parse_miami_dade(text)
+def extract_courses_from_table(text):
+    # This pattern matches the course table rows in the transcript
+    course_pattern = re.compile(
+        r'(\d{4}-\d{4})\s*'  # School year
+        r'\|?\s*(\d+)\s*'     # Grade level
+        r'\|?\s*([A-Z0-9]+)\s*'  # Course code
+        r'\|?\s*([^\|]+?)\s*'  # Course name (captures until next pipe)
+        r'(?:\|\s*[^\|]*){2}'  # Skip Term and DstNumber
+        r'\|\s*([A-FW]?)\s*'   # Grade (FG column)
+        r'(?:\|\s*[^\|]*)'     # Skip Incl column
+        r'\|\s*([\d\.]+|inProgress)'  # Credits
+    )
     
-    def _identify_transcript_type(self, text: str) -> str:
-        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'
+    courses_by_grade = defaultdict(list)
     
-    def _parse_homeschool(self, text: str) -> Dict[str, Union[Dict, List[Dict]]]:
-        courses = []
-        current_grade = None
-        current_year = None
+    for match in re.finditer(course_pattern, text):
+        year_range, grade_level, course_code, course_name, grade, credits = match.groups()
         
-        student_info = {}
-        name_match = re.search(r'Student Name:\s*(.+)\s*SSN:', text)
-        if name_match:
-            student_info['name'] = name_match.group(1).strip()
+        # Clean up course name
+        course_name = course_name.strip()
+        if 'DE:' in course_name:
+            course_name = course_name.replace('DE:', 'Dual Enrollment:')
+        if 'AP' in course_name:
+            course_name = course_name.replace('AP', 'AP ')
         
-        for line in text.split('\n'):
-            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_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()
-                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'
-                })
-        
-        gpa_data = self._extract_gpa_data(text)
-        return {
-            'student_info': student_info,
-            'courses': {'All': courses},
-            'gpa': gpa_data,
-            'grade_level': current_grade.replace('th Grade', '') if current_grade else "Unknown"
+        course_info = {
+            'name': f"{course_code} {course_name}",
+            'year': year_range,
+            'credits': credits
         }
-    
-    def _parse_doral_academy(self, text: str) -> Dict[str, Union[Dict, List[Dict]]]:
-        courses = []
-        student_info = {}
-        name_match = re.search(r'LEGAL NAME:\s*([^\n]+)', text)
-        if name_match:
-            student_info['name'] = name_match.group(1).strip()
-        
-        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)
-        
-        grade_year_map = {}
-        for match in year_matches:
-            grade_year_map[match.group(2)] = match.group(1)
         
-        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)
+        if grade and grade.strip():
+            course_info['grade'] = grade.strip()
         
-        gpa_data = self._extract_gpa_data(text)
-        grade_level = "12" if re.search(r'GRADE LEVEL:\s*12', text) else "Unknown"
-        
-        return {
-            'student_info': student_info,
-            'courses': dict(courses_by_grade),
-            'gpa': gpa_data,
-            'grade_level': grade_level
-        }
+        courses_by_grade[grade_level].append(course_info)
     
-    def _parse_miami_dade(self, text: str) -> Dict[str, Union[Dict, List[Dict]]]:
-        courses = []
-        courses_by_grade = defaultdict(list)
-        
-        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 = 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)
-        
-        gpa_data = self._extract_gpa_data(text)
-        grade_level = re.search(r'Current Grade:\s*(\d+)', text).group(1) if re.search(r'Current Grade:\s*(\d+)', text) else "Unknown"
-        
-        return {
-            'student_info': student_info,
-            'courses': dict(courses_by_grade),
-            'gpa': gpa_data,
-            'grade_level': grade_level
-        }
+    return courses_by_grade
 
-    def _extract_gpa_data(self, text: str) -> Dict[str, str]:
-        """Improved GPA extraction with multiple pattern matching"""
-        gpa_data = {}
-        
-        # Weighted GPA patterns
-        weighted_patterns = [
-            r'Weighted GPA\s*:\s*([\d\.]+)',
-            r'Weighted GPA\s*([\d\.]+)',
-            r'GPA WTD\s*:\s*([\d\.]+)',
-            r'Weighted\s*:\s*([\d\.]+)'
-        ]
-        
-        # Unweighted GPA patterns
-        unweighted_patterns = [
-            r'Un-weighted GPA\s*:\s*([\d\.]+)',
-            r'Unweighted GPA\s*([\d\.]+)',
-            r'GPA UNWTD\s*:\s*([\d\.]+)',
-            r'Unweighted\s*:\s*([\d\.]+)'
-        ]
-        
-        # Try all weighted patterns
-        for pattern in weighted_patterns:
-            match = re.search(pattern, text, re.IGNORECASE)
-            if match:
-                gpa_data['weighted'] = match.group(1)
-                break
-        
-        # Try all unweighted patterns
-        for pattern in unweighted_patterns:
-            match = re.search(pattern, text, re.IGNORECASE)
-            if match:
-                gpa_data['unweighted'] = match.group(1)
-                break
-        
-        # Fallback to cumulative GPA if not found
-        if not gpa_data:
-            cumulative_match = re.search(r'Cumulative GPA\s*:\s*([\d\.]+)', text, re.IGNORECASE)
-            if cumulative_match:
-                gpa_data['weighted'] = cumulative_match.group(1)
-                gpa_data['unweighted'] = cumulative_match.group(1)
-        
-        return gpa_data
-
-# ========== TRANSCRIPT PROCESSING ==========
 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() or '' + '\n'
+        reader = PdfReader(file)
+        for page in reader.pages:
+            text += page.extract_text() + '\n'
         
-        parsed_data = parser.parse_transcript(text)
+        # 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"
         
-        # Enhanced GPA display
-        gpa_data = parsed_data.get('gpa', {})
-        weighted_gpa = gpa_data.get('weighted', 'Not Found (Please check transcript)')
-        unweighted_gpa = gpa_data.get('unweighted', 'Not Found (Please check transcript)')
+        # Extract all courses with grades and year taken
+        courses_by_grade = extract_courses_from_table(text)
         
-        output_text = "=== TRANSCRIPT ANALYSIS RESULTS ===\n\n"
-        output_text += "GPA INFORMATION:\n"
-        output_text += f"🔹 Weighted GPA: {weighted_gpa}\n"
-        output_text += f"🔹 Unweighted GPA: {unweighted_gpa}\n\n"
+        # Prepare detailed output
+        output_text = f"Student Transcript Summary\n{'='*40}\n"
+        output_text += f"Current Grade Level: {grade_level}\n"
+        output_text += f"Weighted GPA: {gpa_data['weighted']}\n"
+        output_text += f"Unweighted GPA: {gpa_data['unweighted']}\n\n"
+        output_text += "Course History:\n{'='*40}\n"
         
-        if 'Not Found' in weighted_gpa or 'Not Found' in unweighted_gpa:
-            output_text += "NOTE: Could not automatically locate GPA information.\n"
-            output_text += "Please check your transcript for GPA details and enter them manually if needed.\n"
+        # Sort grades numerically (09, 10, 11, 12)
+        for grade in sorted(courses_by_grade.keys(), key=int):
+            output_text += f"\nGrade {grade}:\n{'-'*30}\n"
+            for course in courses_by_grade[grade]:
+                output_text += f"- {course['name']}"
+                if 'grade' in course and course['grade']:
+                    output_text += f" (Grade: {course['grade']})"
+                if 'credits' in course:
+                    output_text += f" | Credits: {course['credits']}"
+                output_text += f" | Year: {course['year']}\n"
         
-        return output_text, parsed_data
+        return output_text, {
+            "gpa": gpa_data,
+            "grade_level": grade_level,
+            "courses": dict(courses_by_grade)
+        }
     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:",
-    # ... [rest of your questions] ...
+    "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)"],
-    # ... [rest of your options] ...
+    ["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):
@@ -311,19 +172,49 @@ def learning_style_quiz(*answers):
     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"
-        # ... [rest of your learning style tips] ...
+        # 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"
@@ -334,6 +225,7 @@ def learning_style_quiz(*answers):
 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 = {
@@ -362,27 +254,49 @@ def save_profile(name, age, interests, transcript, learning_style,
     with open(json_path, "w") as f:
         json.dump(data, f, indent=2)
 
-    gpa = transcript.get('gpa', {})
     markdown_summary = f"""### Student Profile: {name}
 **Age:** {age}  
 **Interests:** {interests}  
 **Learning Style:** {learning_style}  
-
-#### GPA Information:
-- Weighted GPA: {gpa.get('weighted', 'Not Available')}
-- Unweighted GPA: {gpa.get('unweighted', 'Not Available')}
-
+#### 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
+        for grade in sorted(courses_by_grade.keys(), key=int):
+            display += f"\n**Grade {grade}**\n"
+            for course in courses_by_grade[grade]:
+                display += f"- {course['name']}"
+                if 'grade' in course and course['grade']:
+                    display += f" (Grade: {course['grade']})"
+                if 'credits' in course:
+                    display += f" | Credits: {course['credits']}"
+                display += f" | Year: {course['year']}\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"):
@@ -398,36 +312,97 @@ def generate_response(message, history):
     if not profile:
         return "Please complete and save your profile first using the previous tabs."
     
-    transcript = profile.get("transcript", {})
-    gpa = transcript.get("gpa", {})
+    # 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", {})
     
-    # When user asks about GPA
-    if any(word in message.lower() for word in ["gpa", "grade", "weighted", "unweighted"]):
-        response = "Your GPA Information:\n"
-        response += f"- Weighted GPA: {gpa.get('weighted', 'Not Available')}\n"
-        response += f"- Unweighted GPA: {gpa.get('unweighted', 'Not Available')}\n"
-        
-        # Add interpretation if available
-        weighted = gpa.get('weighted')
-        if weighted and weighted.replace('.', '').isdigit():
-            weighted_num = float(weighted)
-            if weighted_num >= 3.5:
-                response += "\nExcellent GPA! You're doing great!"
-            elif weighted_num >= 3.0:
-                response += "\nGood GPA! Keep up the good work!"
-            else:
-                response += "\nConsider focusing on improving your grades."
+    # 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
     
-    # ... [rest of your AI assistant logic] ...
+    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"
+        for grade in sorted(courses.keys(), key=int):
+            response += f"\nGrade {grade}:\n"
+            for course in courses[grade]:
+                response += f"- {course['name']}"
+                if 'grade' in course:
+                    response += f" (Grade: {course['grade']})"
+                response += "\n"
+        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)")
+        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=5)
+        transcript_output = gr.Textbox(label="Transcript Results", lines=20)
         transcript_data = gr.State()
         transcript_file.change(
             fn=parse_transcript,
@@ -480,9 +455,10 @@ with gr.Blocks() as app:
         chatbot = gr.ChatInterface(
             fn=generate_response,
             examples=[
-                "What's my GPA?",
-                "How should I study for my classes?",
-                "What subjects am I taking?"
+                "How should I study for my next test?",
+                "What's my GPA information?",
+                "Show me my course history",
+                "How can I improve my grades?"
             ]
         )