Update app.py

app.py

@@ -35,7 +35,7 @@ def extract_transcript_info(df):
         info['Courses'] = list(set([c[0].strip() for c in courses]))
     return info
 
-# Learning style quiz
+# Learning style questions - from educationplanner.org
 learning_style_questions = [
     "When you are learning something new, you prefer to:",
     "When you are at home, you like to:",
@@ -66,6 +66,7 @@ style_count_map = {0: "visual", 1: "auditory", 2: "reading/writing"}
 
 def learning_style_quiz(*answers):
     scores = {'visual': 0, 'auditory': 0, 'reading/writing': 0}
+
     for i, ans in enumerate(answers):
         if i < len(learning_style_answers):
             options = learning_style_answers[i]
@@ -73,10 +74,13 @@ def learning_style_quiz(*answers):
                 index = options.index(ans)
                 style = style_count_map[index]
                 scores[style] += 1
+
     max_score = max(scores.values())
     best_styles = [style.capitalize() for style, score in scores.items() if score == max_score]
+
     return ", ".join(best_styles)
 
+# PanoramaEd categories and multiple choice questions
 get_to_know_categories = {
     "All About Me": [
         ("What’s your favorite way to spend a day off?", []),
@@ -112,103 +116,114 @@ get_to_know_categories = {
     ]
 }
 
-def display_saved_profile():
-    try:
-        with open("student_profile.json", "r") as f:
-            profile = json.load(f)
-    except FileNotFoundError:
-        return "⚠️ No profile data found."
+# Generators for output summaries
+def generate_learning_plan(info):
+    level = info.get("Grade_Level", "unknown")
+    courses = info.get("Courses", [])
+    gpa = info.get("GPA", "N/A")
+    return f"""
+📘 **Personalized Learning Plan**
+- Grade Level: {level}
+- GPA: {gpa}
+- Suggested Focus Areas: {', '.join(courses[:3]) if courses else 'N/A'}
+- Goals: Strengthen key subjects, explore interests, and build study habits.
+"""
 
-    transcript_info = profile.get("transcript_info", {})
-    gpa = transcript_info.get("GPA", "N/A")
-    grade = transcript_info.get("Grade_Level", "N/A")
-    courses = transcript_info.get("Courses", [])
+def generate_learning_style_summary(style):
+    return f"""
+🧠 **Learning Style Summary**
+You are a **{style}** learner. That means you learn best through {"visual aids like charts and images" if "Visual" in style else "listening and verbal instruction" if "Auditory" in style else "reading and writing-based methods"}.
+"""
 
-    past_classes = [{'Course': course, 'Grade': 'A'} for course in courses[:max(len(courses)-2, 0)]]
-    current_classes = [{'Course': course, 'Grade': 'IP'} for course in courses[-2:]]
+def generate_motivation_section(responses):
+    hopes = [ans for q, ans in responses.items() if "hope" in q.lower() or "dream" in q.lower()]
+    return f"""
+💡 **Motivational Summary**
+Your dreams are powerful: {'; '.join(hopes) if hopes else 'You are filled with potential!'}. 
+Believe in yourself and keep moving forward.
+"""
 
-    all_classes_df = pd.DataFrame(past_classes + current_classes)
+# Save all answers into profile
+def save_profile(file, *inputs):
+    if not file:
+        return "⚠️ Please upload your transcript."
 
-    learning_type = profile.get("learning_style", "N/A")
-    responses = profile.get("get_to_know_answers", {})
-    blog = profile.get("blog", "[User skipped this section]")
+    quiz_answers = inputs[:len(learning_style_questions)]
+    if any(ans is None for ans in quiz_answers):
+        return "⚠️ Please answer all the learning style questions."
 
-    comments = [f"⭐ I love how you spend your free time: {responses.get('What’s your favorite way to spend a day off?', 'N/A')}.",
-                f"🍕 {responses.get('If you could only eat one food for the rest of your life, what would it be?', 'N/A')} sounds delicious!",
-                f"🎬 You mentioned {responses.get('What are some of your favorite movies or shows?', 'N/A')}. Great picks!"]
+    blog_checkbox = inputs[len(learning_style_questions)]
+    blog_text = inputs[len(learning_style_questions)+1]
+    category_answers = inputs[len(learning_style_questions)+2:]
 
-    blog_comment = ""
-    if blog and blog != "[User chose to skip this section]":
-        blog_comment = f"📝 Your blog was very thoughtful! You wrote: \"{blog[:150]}...\""
+    if any(ans.strip() == "" for ans in category_answers):
+        return "⚠️ Please complete all 'Get to Know You' sections before saving."
 
-    summary_text = f"""
-👤 **Student Overview**
-- Name: Extracted from transcript (if available)
-- GPA: {gpa}
-- Grade Level: {grade}
-📚 **Courses**
-{all_classes_df.to_markdown(index=False)}
-🧠 **Learning Type**
-Based on your answers, your learning style is: **{learning_type}**
-💬 **Personal Reflections**
-{chr(10).join(comments)}
-{blog_comment}
-❓ Is all this information correct?
-"""
-    return summary_text
+    if blog_checkbox and blog_text.strip() == "":
+        return "⚠️ You checked the blog option but didn’t write anything. Please write your mini blog or uncheck the option."
+
+    df = parse_transcript(file)
+    transcript_info = extract_transcript_info(df)
+    learning_type = learning_style_quiz(*quiz_answers)
+
+    question_texts = [q for cat in get_to_know_categories.values() for q, _ in cat]
+    responses = dict(zip(question_texts, category_answers))
 
+    profile = {
+        "transcript": df.to_dict(orient='records'),
+        "transcript_info": transcript_info,
+        "learning_style": learning_type,
+        "get_to_know_answers": responses,
+        "blog": blog_text if blog_checkbox else "[User chose to skip this section]"
+    }
 
-# Interface
+    summary = {
+        "Learning_Plan": generate_learning_plan(transcript_info),
+        "Style_Summary": generate_learning_style_summary(learning_type),
+        "Motivation": generate_motivation_section(responses)
+    }
+
+    with open("student_profile.json", "w") as f:
+        json.dump(profile, f, indent=4)
+
+    with open("student_summary.md", "w") as f:
+        f.write(summary["Learning_Plan"] + '\n' + summary["Style_Summary"] + '\n' + summary["Motivation"])
+
+    return f"✅ Profile saved! Your learning style is: {learning_type}"
+
+# Build Gradio UI
 with gr.Blocks() as demo:
-    profile_interface = gr.Column(visible=True)
-    confirmation_interface = gr.Column(visible=False)
-
-    with profile_interface:
-        gr.Markdown("## ✍️ Create Your Profile")
-        transcript_input = gr.File(label="Upload Transcript (.csv, .xlsx, or .pdf)")
-        ls_dropdowns = [gr.Dropdown(choices=learning_style_answers[i], label=learning_style_questions[i]) for i in range(10)]
-        get_to_know_inputs = {}
-        for section, questions in get_to_know_categories.items():
-            gr.Markdown(f"### {section}")
-            for q, _ in questions:
-                get_to_know_inputs[q] = gr.Textbox(label=q)
-        blog_input = gr.Textbox(label="Write a short blog about yourself (optional)", lines=5)
-        save_btn = gr.Button("Save Profile")
-
-    with confirmation_interface:
-        gr.Markdown("## ✅ Profile Review & Confirmation")
-        summary_output = gr.Markdown()
-        confirm_btn = gr.Button("Yes, everything is correct")
-        correct_btn = gr.Button("No, I need to make changes")
-        final_status = gr.Textbox(label="Final Status")
-
-    def save_profile(transcript_file, *answers):
-        df = parse_transcript(transcript_file)
-        transcript_info = extract_transcript_info(df)
-        learning_result = learning_style_quiz(*answers[:10])
-        personal_responses = {q: ans for q, ans in zip(get_to_know_inputs.keys(), answers[10:-1])}
-        blog_text = answers[-1]
-
-        student_data = {
-            "transcript_info": transcript_info,
-            "learning_style": learning_result,
-            "get_to_know_answers": personal_responses,
-            "blog": blog_text or "[User chose to skip this section]"
-        }
-
-        with open("student_profile.json", "w") as f:
-            json.dump(student_data, f, indent=2)
-
-        return gr.update(visible=False), gr.update(visible=True), display_saved_profile()
-
-    save_btn.click(
-        fn=save_profile,
-        inputs=[transcript_input] + ls_dropdowns + list(get_to_know_inputs.values()) + [blog_input],
-        outputs=[profile_interface, confirmation_interface, summary_output]
-    )
-
-    confirm_btn.click(fn=lambda: "🎉 All set! You're ready to move forward.", outputs=[final_status])
-    correct_btn.click(fn=lambda: "🔁 Okay! Please update the necessary information and save again.", outputs=[final_status])
+    gr.Markdown("## 🎓 Personalized AI Student Assistant")
+
+    with gr.Row():
+        file = gr.File(label="📄 Upload Your Transcript (.csv, .xlsx, .pdf)")
+
+    with gr.Column():
+        gr.Markdown("### 🧠 Learning Style Discovery")
+        quiz_components = []
+        for i, (question, options) in enumerate(zip(learning_style_questions, learning_style_answers)):
+            quiz_components.append(gr.Radio(
+                choices=options,
+                label=f"{i+1}. {question}"
+            ))
+
+    category_inputs = []
+    for category, questions in get_to_know_categories.items():
+        gr.Markdown(f"### 📘 {category}")
+        for q_text, _ in questions:
+            category_inputs.append(gr.Textbox(label=q_text))
+
+    blog_checkbox = gr.Checkbox(label="📝 I'd like to write a mini blog about myself")
+    blog_text = gr.Textbox(lines=5, label="✍️ Mini Blog", visible=False)
+
+    blog_checkbox.change(fn=lambda x: gr.update(visible=x), inputs=blog_checkbox, outputs=blog_text)
+
+    submit = gr.Button("🗕️ Save My Profile")
+    output = gr.Textbox(label="Status")
+
+    submit.click(fn=save_profile,
+                 inputs=[file, *quiz_components, blog_checkbox, blog_text, *category_inputs],
+                 outputs=[output])
 
 if __name__ == '__main__':
     demo.launch()