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Update app.py
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app.py
CHANGED
@@ -7,8 +7,46 @@ from PyPDF2 import PdfReader
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from collections import defaultdict
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# ========== TRANSCRIPT PARSING FUNCTIONS ==========
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def parse_transcript(file):
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if file.name.endswith('.
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text = ''
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reader = PdfReader(file)
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for page in reader.pages:
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@@ -20,16 +58,17 @@ def parse_transcript(file):
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grade_match = re.search(r'(Grade|Year)[\s:]*(\d+|Freshman|Sophomore|Junior|Senior)', text, re.IGNORECASE)
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grade_level = grade_match.group(2) if grade_match else "Unknown"
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# GPA extraction
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gpa_data = {'weighted': "N/A", 'unweighted': "N/A"}
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gpa_patterns = [
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r'Weighted GPA[\s:]*(\d\.\d{1,2})',
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r'GPA \(Weighted\)[\s:]*(\d\.\d{1,2})',
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r'Unweighted GPA[\s:]*(\d\.\d{1,2})',
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r'GPA \(Unweighted\)[\s:]*(\d\.\d{1,2})',
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r'GPA[\s:]*(\d\.\d{1,2})'
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]
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for pattern in gpa_patterns:
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for match in re.finditer(pattern, text, re.IGNORECASE):
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gpa_value = match.group(1)
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@@ -43,6 +82,8 @@ def parse_transcript(file):
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if gpa_data['weighted'] == "N/A":
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gpa_data['weighted'] = gpa_value
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output_text = f"Grade Level: {grade_level}\n\n"
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if gpa_data['weighted'] != "N/A" or gpa_data['unweighted'] != "N/A":
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output_text += "GPA Information:\n"
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else:
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output_text += "No GPA information found\n"
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return output_text, {
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"gpa": gpa_data,
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"grade_level": grade_level
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}
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else:
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return "
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# ========== LEARNING STYLE QUIZ ==========
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learning_style_questions = [
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"When you need directions to a new place, you prefer:",
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"When you learn a new skill, you prefer to:",
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"When you're trying to concentrate, you:",
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"When you meet new people, you remember them by:"
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]
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learning_style_options = [
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["Look at a map (Visual)", "Have someone tell you (Auditory)", "Write down directions (Reading/Writing)", "Try walking/driving there (Kinesthetic)"],
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["Read instructions (Reading/Writing)", "Have someone show you (Visual)", "Listen to explanations (Auditory)", "Try it yourself (Kinesthetic)"],
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["Need quiet (Reading/Writing)", "Need background noise (Auditory)", "Need to move around (Kinesthetic)", "Need visual stimulation (Visual)"],
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["Their face (Visual)", "Their name (Auditory)", "What you talked about (Reading/Writing)", "What you did together (Kinesthetic)"]
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]
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def learning_style_quiz(*answers):
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"Kinesthetic": 0
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}
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for i, answer in enumerate(answers):
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if answer
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scores["Reading/Writing"] += 1
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elif answer
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scores["Auditory"] += 1
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elif answer
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scores["Visual"] += 1
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elif answer
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scores["Kinesthetic"] += 1
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max_score = max(scores.values())
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dominant_styles = [style for style, score in scores.items() if score == max_score]
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if len(dominant_styles) == 1:
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-
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else:
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# ========== PROFILE MANAGEMENT ==========
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def save_profile(name, age, interests, transcript, learning_style,
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movie, movie_reason, show, show_reason,
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book, book_reason, character, character_reason, blog):
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favorites = {
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"movie": movie,
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"movie_reason": movie_reason,
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"show": show,
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"show_reason": show_reason,
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"book": book,
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"book_reason": book_reason,
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"character": character,
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"character_reason": character_reason
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}
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data = {
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"name": name,
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"age": age,
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@@ -127,36 +226,60 @@ def save_profile(name, age, interests, transcript, learning_style,
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"favorites": favorites,
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"blog": blog
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}
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os.makedirs("student_profiles", exist_ok=True)
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json_path = os.path.join("student_profiles", f"{name.replace(' ', '_')}_profile.json")
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with open(json_path, "w") as f:
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json.dump(data, f, indent=2)
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return "Profile saved successfully!"
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# ========== GRADIO INTERFACE ==========
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with gr.Blocks() as app:
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# Profile tabs
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with gr.Tab("Step 1: Upload Transcript"):
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transcript_file = gr.File(label="Upload your transcript (PDF)")
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transcript_output = gr.Textbox(label="Transcript Output")
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transcript_data = gr.State()
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transcript_file.change(parse_transcript, inputs=transcript_file, outputs=[transcript_output, transcript_data])
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with gr.Tab("Step 2: Learning Style Quiz"):
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quiz_components = []
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for i, (question, options) in enumerate(zip(learning_style_questions, learning_style_options)):
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quiz_components.append(
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learning_output = gr.Textbox(label="Learning Style Result")
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gr.Button("Submit Quiz").click(
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learning_style_quiz,
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inputs=quiz_components,
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character_reason = gr.Textbox(label="Why do you like that character?")
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blog_checkbox = gr.Checkbox(label="Do you want to write a blog?", value=False)
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blog_text = gr.Textbox(label="Write your blog here", visible=False, lines=5)
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blog_checkbox.change(lambda x: gr.update(visible=x), inputs=blog_checkbox, outputs=blog_text)
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with gr.Tab("Step 4: Save
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save_btn = gr.Button("Save Profile")
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app.launch()
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from collections import defaultdict
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# ========== TRANSCRIPT PARSING FUNCTIONS ==========
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def extract_courses_with_grade_levels(text):
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grade_level_pattern = r"(Grade|Year)\s*[:]?\s*(\d+|Freshman|Sophomore|Junior|Senior)"
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grade_match = re.search(grade_level_pattern, text, re.IGNORECASE)
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current_grade_level = grade_match.group(2) if grade_match else "Unknown"
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course_pattern = r"""
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(?:^|\n)
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(?: (Grade|Year)\s*[:]?\s*(\d+|Freshman|Sophomore|Junior|Senior)\s*[\n-]* )?
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(
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(?:[A-Z]{2,}\s?\d{3})
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[A-Z][a-z]+(?:\s[A-Z][a-z]+)*
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)
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\s*
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(?: [:\-]?\s* ([A-F][+-]?|\d{2,3}%)? )?
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"""
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courses_by_grade = defaultdict(list)
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current_grade = current_grade_level
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for match in re.finditer(course_pattern, text, re.VERBOSE | re.MULTILINE):
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grade_context, grade_level, course, grade = match.groups()
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if grade_context:
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current_grade = grade_level
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if course:
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course_info = {"course": course.strip()}
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if grade:
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course_info["grade"] = grade.strip()
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courses_by_grade[current_grade].append(course_info)
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return dict(courses_by_grade)
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def parse_transcript(file):
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if file.name.endswith('.csv'):
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df = pd.read_csv(file)
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elif file.name.endswith('.xlsx'):
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df = pd.read_excel(file)
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elif file.name.endswith('.pdf'):
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text = ''
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reader = PdfReader(file)
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for page in reader.pages:
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grade_match = re.search(r'(Grade|Year)[\s:]*(\d+|Freshman|Sophomore|Junior|Senior)', text, re.IGNORECASE)
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grade_level = grade_match.group(2) if grade_match else "Unknown"
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# Enhanced GPA extraction
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gpa_data = {'weighted': "N/A", 'unweighted': "N/A"}
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gpa_patterns = [
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r'Weighted GPA[\s:]*(\d\.\d{1,2})',
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r'GPA \(Weighted\)[\s:]*(\d\.\d{1,2})',
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r'Cumulative GPA \(Weighted\)[\s:]*(\d\.\d{1,2})',
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r'Unweighted GPA[\s:]*(\d\.\d{1,2})',
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r'GPA \(Unweighted\)[\s:]*(\d\.\d{1,2})',
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r'Cumulative GPA \(Unweighted\)[\s:]*(\d\.\d{1,2})',
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r'GPA[\s:]*(\d\.\d{1,2})'
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]
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for pattern in gpa_patterns:
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for match in re.finditer(pattern, text, re.IGNORECASE):
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gpa_value = match.group(1)
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if gpa_data['weighted'] == "N/A":
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gpa_data['weighted'] = gpa_value
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courses_by_grade = extract_courses_with_grade_levels(text)
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output_text = f"Grade Level: {grade_level}\n\n"
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if gpa_data['weighted'] != "N/A" or gpa_data['unweighted'] != "N/A":
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output_text += "GPA Information:\n"
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else:
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output_text += "No GPA information found\n"
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output_text += "\n(Courses not shown here)"
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return output_text, {
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"gpa": gpa_data,
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"grade_level": grade_level,
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"courses": courses_by_grade
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}
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else:
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return "Unsupported file format", None
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# For CSV/XLSX fallback
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gpa = "N/A"
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for col in ['GPA', 'Grade Point Average', 'Cumulative GPA']:
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if col in df.columns:
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gpa = df[col].iloc[0] if isinstance(df[col].iloc[0], (float, int)) else "N/A"
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break
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grade_level = "N/A"
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for col in ['Grade Level', 'Grade', 'Class', 'Year']:
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if col in df.columns:
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grade_level = df[col].iloc[0]
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break
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courses = []
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for col in ['Course', 'Subject', 'Course Name', 'Class']:
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if col in df.columns:
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courses = df[col].tolist()
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break
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output_text = f"Grade Level: {grade_level}\nGPA: {gpa}\n\nCourses:\n"
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output_text += "\n".join(f"- {course}" for course in courses)
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return output_text, {
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"gpa": {"unweighted": gpa, "weighted": "N/A"},
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"grade_level": grade_level,
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"courses": courses
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}
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# ========== LEARNING STYLE QUIZ ==========
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learning_style_questions = [
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"When you need directions to a new place, you prefer:",
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"When you learn a new skill, you prefer to:",
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"When you're trying to concentrate, you:",
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"When you meet new people, you remember them by:",
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"When you're relaxing, you prefer to:",
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"When you're explaining something to someone, you:",
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"When you're trying to remember something, you:",
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"When you're in a classroom, you learn best when:",
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"When you're trying to solve a problem, you:",
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"When you're taking notes, you:",
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"When you're learning new software, you prefer to:",
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"When you're at a museum, you spend the most time:",
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"When you're assembling furniture, you:",
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"When you're learning new vocabulary, you:",
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"When you're giving a presentation, you prefer:",
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"When you're at a party, you enjoy:",
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"When you're taking a break from studying, you:",
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"When you're learning dance moves, you:",
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"When you're choosing a book, you prefer:"
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]
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learning_style_options = [
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["Look at a map (Visual)", "Have someone tell you (Auditory)", "Write down directions (Reading/Writing)", "Try walking/driving there (Kinesthetic)"],
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["Read instructions (Reading/Writing)", "Have someone show you (Visual)", "Listen to explanations (Auditory)", "Try it yourself (Kinesthetic)"],
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["Need quiet (Reading/Writing)", "Need background noise (Auditory)", "Need to move around (Kinesthetic)", "Need visual stimulation (Visual)"],
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["Their face (Visual)", "Their name (Auditory)", "What you talked about (Reading/Writing)", "What you did together (Kinesthetic)"],
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["Read (Reading/Writing)", "Listen to music (Auditory)", "Watch TV (Visual)", "Do something active (Kinesthetic)"],
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["Write it down (Reading/Writing)", "Tell them verbally (Auditory)", "Show them (Visual)", "Demonstrate physically (Kinesthetic)"],
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["See it written down (Visual)", "Say it out loud (Auditory)", "Write it down (Reading/Writing)", "Do it physically (Kinesthetic)"],
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["Reading materials (Reading/Writing)", "Listening to lectures (Auditory)", "Seeing diagrams (Visual)", "Doing hands-on activities (Kinesthetic)"],
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["Write down steps (Reading/Writing)", "Talk through it (Auditory)", "Draw diagrams (Visual)", "Try different approaches (Kinesthetic)"],
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["Write detailed notes (Reading/Writing)", "Record lectures (Auditory)", "Draw mind maps (Visual)", "Take minimal notes (Kinesthetic)"],
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["Read the manual (Reading/Writing)", "Have someone explain it (Auditory)", "Watch tutorial videos (Visual)", "Just start using it (Kinesthetic)"],
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["Reading descriptions (Reading/Writing)", "Listening to audio guides (Auditory)", "Looking at exhibits (Visual)", "Interactive displays (Kinesthetic)"],
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["Read instructions first (Reading/Writing)", "Ask someone to help (Auditory)", "Look at diagrams (Visual)", "Start assembling (Kinesthetic)"],
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["Write them repeatedly (Reading/Writing)", "Say them repeatedly (Auditory)", "Use flashcards (Visual)", "Use them in conversation (Kinesthetic)"],
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["Having detailed notes (Reading/Writing)", "Speaking freely (Auditory)", "Using visual aids (Visual)", "Demonstrating something (Kinesthetic)"],
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["Conversations (Auditory)", "People-watching (Visual)", "Dancing/games (Kinesthetic)", "Reading about people (Reading/Writing)"],
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["Read for fun (Reading/Writing)", "Listen to music (Auditory)", "Watch videos (Visual)", "Exercise (Kinesthetic)"],
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["Watch demonstrations (Visual)", "Listen to instructions (Auditory)", "Read choreography (Reading/Writing)", "Try the moves (Kinesthetic)"],
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["Text-heavy books (Reading/Writing)", "Audiobooks (Auditory)", "Books with pictures (Visual)", "Interactive books (Kinesthetic)"]
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]
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def learning_style_quiz(*answers):
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"Kinesthetic": 0
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}
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# Map each answer to a learning style
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for i, answer in enumerate(answers):
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if answer in learning_style_options[i][0]:
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scores["Reading/Writing"] += 1
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elif answer in learning_style_options[i][1]:
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scores["Auditory"] += 1
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elif answer in learning_style_options[i][2]:
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scores["Visual"] += 1
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elif answer in learning_style_options[i][3]:
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scores["Kinesthetic"] += 1
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# Get the highest score(s)
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max_score = max(scores.values())
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dominant_styles = [style for style, score in scores.items() if score == max_score]
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# Generate result
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if len(dominant_styles) == 1:
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result = f"Your primary learning style is: {dominant_styles[0]}"
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else:
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result = f"You have multiple strong learning styles: {', '.join(dominant_styles)}"
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+
# Add detailed breakdown
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+
result += "\n\nDetailed Scores:\n"
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+
for style, score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
|
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+
result += f"{style}: {score}/20\n"
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+
|
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+
return result
|
217 |
+
|
218 |
+
# ========== SAVE STUDENT PROFILE FUNCTION ==========
|
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+
def save_profile(name, age, interests, transcript, learning_style, favorites, blog):
|
220 |
data = {
|
221 |
"name": name,
|
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"age": age,
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|
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"favorites": favorites,
|
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"blog": blog
|
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}
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|
229 |
os.makedirs("student_profiles", exist_ok=True)
|
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json_path = os.path.join("student_profiles", f"{name.replace(' ', '_')}_profile.json")
|
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with open(json_path, "w") as f:
|
232 |
json.dump(data, f, indent=2)
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|
233 |
|
234 |
+
markdown_summary = f"""### Student Profile: {name}
|
235 |
+
**Age:** {age}
|
236 |
+
**Interests:** {interests}
|
237 |
+
**Learning Style:** {learning_style}
|
238 |
+
#### Transcript:
|
239 |
+
{transcript_display(transcript)}
|
240 |
+
#### Favorites:
|
241 |
+
- Movie: {favorites['movie']} ({favorites['movie_reason']})
|
242 |
+
- Show: {favorites['show']} ({favorites['show_reason']})
|
243 |
+
- Book: {favorites['book']} ({favorites['book_reason']})
|
244 |
+
- Character: {favorites['character']} ({favorites['character_reason']})
|
245 |
+
#### Blog:
|
246 |
+
{blog if blog else "_No blog provided_"}
|
247 |
+
"""
|
248 |
+
return markdown_summary
|
249 |
+
|
250 |
+
def transcript_display(transcript_dict):
|
251 |
+
if not transcript_dict:
|
252 |
+
return "No transcript uploaded."
|
253 |
+
if isinstance(transcript_dict, dict) and all(isinstance(v, list) for v in transcript_dict.values()):
|
254 |
+
display = ""
|
255 |
+
for grade_level, courses in transcript_dict.items():
|
256 |
+
display += f"\n**Grade {grade_level}**\n"
|
257 |
+
for course in courses:
|
258 |
+
display += f"- {course['course']}"
|
259 |
+
if 'grade' in course:
|
260 |
+
display += f" (Grade: {course['grade']})"
|
261 |
+
display += "\n"
|
262 |
+
return display
|
263 |
+
return "\n".join([f"- {course}" for course in transcript_dict["courses"]] +
|
264 |
+
[f"Grade Level: {transcript_dict['grade_level']}", f"GPA: {transcript_dict['gpa']}"])
|
265 |
|
266 |
# ========== GRADIO INTERFACE ==========
|
267 |
with gr.Blocks() as app:
|
|
|
268 |
with gr.Tab("Step 1: Upload Transcript"):
|
269 |
+
transcript_file = gr.File(label="Upload your transcript (CSV, Excel, or PDF)")
|
270 |
transcript_output = gr.Textbox(label="Transcript Output")
|
271 |
transcript_data = gr.State()
|
272 |
+
transcript_file.change(fn=parse_transcript, inputs=transcript_file, outputs=[transcript_output, transcript_data])
|
273 |
|
274 |
with gr.Tab("Step 2: Learning Style Quiz"):
|
275 |
+
gr.Markdown("### Complete this 20-question quiz to determine your learning style")
|
276 |
quiz_components = []
|
277 |
for i, (question, options) in enumerate(zip(learning_style_questions, learning_style_options)):
|
278 |
+
quiz_components.append(
|
279 |
+
gr.Radio(choices=options, label=f"{i+1}. {question}")
|
280 |
+
)
|
281 |
|
282 |
+
learning_output = gr.Textbox(label="Learning Style Result", lines=5)
|
283 |
gr.Button("Submit Quiz").click(
|
284 |
learning_style_quiz,
|
285 |
inputs=quiz_components,
|
|
|
300 |
character_reason = gr.Textbox(label="Why do you like that character?")
|
301 |
blog_checkbox = gr.Checkbox(label="Do you want to write a blog?", value=False)
|
302 |
blog_text = gr.Textbox(label="Write your blog here", visible=False, lines=5)
|
303 |
+
blog_checkbox.change(fn=lambda x: gr.update(visible=x), inputs=blog_checkbox, outputs=blog_text)
|
304 |
|
305 |
+
with gr.Tab("Step 4: Save & Review"):
|
306 |
+
output_summary = gr.Markdown()
|
307 |
save_btn = gr.Button("Save Profile")
|
308 |
+
|
309 |
+
def gather_and_save(name, age, interests, movie, movie_reason, show, show_reason,
|
310 |
+
book, book_reason, character, character_reason, blog, transcript, learning_style):
|
311 |
+
favorites = {
|
312 |
+
"movie": movie,
|
313 |
+
"movie_reason": movie_reason,
|
314 |
+
"show": show,
|
315 |
+
"show_reason": show_reason,
|
316 |
+
"book": book,
|
317 |
+
"book_reason": book_reason,
|
318 |
+
"character": character,
|
319 |
+
"character_reason": character_reason,
|
320 |
+
}
|
321 |
+
return save_profile(name, age, interests, transcript, learning_style, favorites, blog)
|
322 |
+
|
323 |
+
save_btn.click(fn=gather_and_save,
|
324 |
+
inputs=[name, age, interests, movie, movie_reason, show, show_reason,
|
325 |
+
book, book_reason, character, character_reason, blog_text,
|
326 |
+
transcript_data, learning_output],
|
327 |
+
outputs=output_summary)
|
328 |
|
329 |
app.launch()
|