Update app.py

app.py

@@ -1,115 +1,126 @@
 import gradio as gr
 import json
 import os
-
-DATA_FILE = "student_profiles.json"
-
-# ---------- Data Storage ----------
-def load_student_data():
-    if not os.path.exists(DATA_FILE):
+import uuid
+
+# -----------------------------
+# File for student profiles
+# -----------------------------
+STUDENTS_FILE = "student_profiles.json"
+
+# -----------------------------
+# Student data handling
+# -----------------------------
+def load_students():
+    """Load all student profiles from JSON file."""
+    if not os.path.exists(STUDENTS_FILE):
         return {}
-    with open(DATA_FILE, "r", encoding="utf-8") as f:
+    with open(STUDENTS_FILE, "r", encoding="utf-8") as f:
         return json.load(f)
 
-def save_student_data(data):
-    with open(DATA_FILE, "w", encoding="utf-8") as f:
-        json.dump(data, f, indent=4, ensure_ascii=False)
-
-def generate_new_student_id():
-    data = load_student_data()
-    if not data:
-        return "student_1"
-    numbers = [int(k.split("_")[1]) for k in data if k.startswith("student_")]
-    return f"student_{max(numbers) + 1}"
-
-def add_student_data(new_data):
-    data = load_student_data()
-    new_id = generate_new_student_id()
-    data[new_id] = new_data
-    save_student_data(data)
-    return new_id, new_data
-
-# ---------- Chat & Roadmap Simulation ----------
-def get_gemini_response(prompt, student_data):
-    # Replace with real LLM call later
-    return f"(Simulated AI Response based on {student_data['personality']} profile: {prompt})"
+def save_students(data):
+    """Save all student profiles to JSON file."""
+    with open(STUDENTS_FILE, "w", encoding="utf-8") as f:
+        json.dump(data, f, indent=2, ensure_ascii=False)
+
+def get_student_data(student_id):
+    """Retrieve a single student profile by ID."""
+    students = load_students()
+    return students.get(student_id)
+
+def add_student_data(data):
+    """Add a new student with unique random ID."""
+    students = load_students()
+    student_id = "student_" + str(uuid.uuid4())[:8]  # unique random ID
+    students[student_id] = data
+    save_students(students)
+    return student_id
+
+# -----------------------------
+# Simulated AI responses (replace with Gemini later)
+# -----------------------------
+def get_gemini_response(prompt, student_data=None):
+    """Simulated LLM response."""
+    if not student_data:
+        return f"(Simulated Response: {prompt})"
+    return f"(Simulated AI Response for {student_data.get('personality','N/A')} personality: {prompt})"
 
 def generate_ai_insights(student_data):
-    return f"(Insights: Your learning style is {student_data['learning_style']}, motivation is {student_data['motivation_level']})"
-
-# ---------- Gradio Interface ----------
-def create_profile(learning_style, academic_progress, personality, interests, goals,
-                   level, preferred_methods, iq_level, eq_level, decision_style,
-                   motivation_level, study_environment, community_groups):
-    new_data = {
-        "learning_style": learning_style,
-        "academic_progress": academic_progress,
-        "personality": personality,
-        "interests": interests,
-        "goals": goals,
-        "level": level,
-        "preferred_methods": [m.strip() for m in preferred_methods.split(",") if m.strip()],
-        "iq_level": iq_level,
-        "eq_level": eq_level,
-        "decision_making_style": decision_style,
-        "motivation_level": motivation_level,
-        "preferred_study_environment": study_environment,
-        "community_groups": [g.strip() for g in community_groups.split(",") if g.strip()]
-    }
-    new_id, saved_data = add_student_data(new_data)
-    return f"✅ New student created with ID: {new_id}", json.dumps(saved_data, indent=2, ensure_ascii=False), list(load_student_data().keys())
-
-def chat(student_id, message):
-    data = load_student_data()
-    student_data = data.get(student_id)
+    """Simulated AI insights."""
+    return f"(Insights: Learning style = {student_data.get('learning_style')}, Motivation = {student_data.get('motivation_level')})"
+
+# -----------------------------
+# FAQ system (Arabic + English)
+# -----------------------------
+faqs = {
+    "إيه هو ThinkPal؟": "ThinkPal منصة بتساعدك تعرف نفسك أكتر وتتعلم بالطريقة اللي تناسبك...",
+    "What is ThinkPal?": "ThinkPal is a platform that helps you know yourself better...",
+    "لو وقفت في نص الطريق؟": "مفيش مشكلة، تقدر ترجع في أي وقت وتكمل من نفس المكان.",
+    "What if I stop halfway?": "No problem! You can always continue later from where you left off."
+}
+
+def find_faq_answer(user_input):
+    """Check if user_input matches any FAQ question."""
+    for q, a in faqs.items():
+        if user_input.strip().lower() in q.lower():
+            return a
+    return None
+
+# -----------------------------
+# Chat interface logic
+# -----------------------------
+def thinkpal_interface(student_id, user_input):
+    """Main chat interface logic."""
+    student_data = get_student_data(student_id)
+    roadmap_text, insights_text, chatbot_response_text = "", "", ""
+
     if not student_data:
-        return "❌ Student not found."
+        return ("❌ Student not found. Please add a profile first.", "", "")
 
-    if message.lower() == "roadmap":
-        return get_gemini_response("roadmap", student_data)
-    elif message.lower() == "insights":
-        return generate_ai_insights(student_data)
+    if user_input.lower() == "roadmap":
+        roadmap_text = get_gemini_response("roadmap", student_data) or "Could not generate roadmap."
+    elif user_input.lower() == "insights":
+        insights_text = generate_ai_insights(student_data) or "Could not generate insights."
     else:
-        return get_gemini_response(message, student_data)
+        faq_answer = find_faq_answer(user_input)
+        chatbot_response_text = faq_answer if faq_answer else get_gemini_response(user_input, student_data) or "Sorry, could not process request."
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 ThinkPal – Personalized Learning Assistant")
+    return roadmap_text, insights_text, chatbot_response_text
 
-    with gr.Tab("➕ Add Student"):
-        with gr.Row():
-            learning_style = gr.Textbox(label="Learning Style")
-            academic_progress = gr.Textbox(label="Academic Progress")
-        personality = gr.Textbox(label="Personality")
-        interests = gr.Textbox(label="Interests")
-        goals = gr.Textbox(label="Goals")
-        level = gr.Textbox(label="Level")
-        preferred_methods = gr.Textbox(label="Preferred Methods (comma-separated)")
-        iq_level = gr.Textbox(label="IQ Level")
-        eq_level = gr.Textbox(label="EQ Level")
-        decision_style = gr.Textbox(label="Decision Style")
-        motivation_level = gr.Textbox(label="Motivation Level")
-        study_environment = gr.Textbox(label="Study Environment")
-        community_groups = gr.Textbox(label="Community Groups (comma-separated)")
-
-        create_btn = gr.Button("Save Profile")
-        status = gr.Textbox(label="Status")
-        preview = gr.Textbox(label="Profile Preview", lines=8)
-        student_list = gr.Dropdown(label="Student IDs", choices=list(load_student_data().keys()), value=None)
-
-        create_btn.click(
-            fn=create_profile,
-            inputs=[learning_style, academic_progress, personality, interests, goals,
-                    level, preferred_methods, iq_level, eq_level, decision_style,
-                    motivation_level, study_environment, community_groups],
-            outputs=[status, preview, student_list]
-        )
+def add_new_student(data_json):
+    """Add student from JSON string."""
+    try:
+        data = json.loads(data_json)
+    except json.JSONDecodeError:
+        return "❌ Invalid JSON. Please check your input."
 
-    with gr.Tab("💬 Chat"):
-        student_select = gr.Dropdown(label="Choose Student", choices=list(load_student_data().keys()))
-        chat_in = gr.Textbox(label="Message (type 'roadmap' or 'insights')")
-        chat_out = gr.Textbox(label="Chatbot Response", lines=6)
-        chat_btn = gr.Button("Send")
+    student_id = add_student_data(data)
+    return f"✅ Student added with ID: {student_id}"
 
-        chat_btn.click(fn=chat, inputs=[student_select, chat_in], outputs=chat_out)
+# -----------------------------
+# Gradio UI
+# -----------------------------
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 🎓 ThinkPal - Personalized Learning Assistant")
 
-demo.launch()
+    with gr.Tab("💬 Chat"):
+        with gr.Row():
+            student_id_input = gr.Textbox(label="Enter Student ID", placeholder="student_1")
+            user_input = gr.Textbox(label="Your Question", placeholder="Type roadmap, insights, or FAQ...")
+        submit_btn = gr.Button("Ask")
+        roadmap_output = gr.Textbox(label="Roadmap", lines=8)
+        insights_output = gr.Textbox(label="Insights", lines=6)
+        chatbot_output = gr.Textbox(label="Chatbot Response", lines=4)
+        submit_btn.click(thinkpal_interface, [student_id_input, user_input], [roadmap_output, insights_output, chatbot_output])
+
+    with gr.Tab("➕ Add Student"):
+        gr.Markdown("Paste student profile data as JSON to add a new student.")
+        new_student_data = gr.Textbox(label="Student Data (JSON)", lines=10,
+                                      placeholder='{"learning_style": "visual", "goals": "improve math"}')
+        add_btn = gr.Button("Add Student")
+        add_output = gr.Textbox(label="Result")
+        add_btn.click(add_new_student, inputs=[new_student_data], outputs=[add_output])
+
+# For Hugging Face Spaces
+if __name__ == "__main__":
+    demo.launch()