Update app.py

app.py

@@ -1,7 +1,11 @@
 # app.py
 # ThinkPal – Hugging Face Space (Gradio)
 
-import os, json, uuid, re, unicodedata
+import os
+import json
+import uuid
+import re
+import unicodedata
 from difflib import get_close_matches, SequenceMatcher
 
 import gradio as gr
@@ -29,23 +33,27 @@ else:
 # -----------------------------
 # Storage helpers (JSON)
 # -----------------------------
-def load_students() -> dict:
+def load_students():
     if not os.path.exists(DATA_FILE):
         return {}
     with open(DATA_FILE, "r", encoding="utf-8") as f:
         return json.load(f)
 
-def save_students(data: dict) -> None:
+
+def save_students(data):
     with open(DATA_FILE, "w", encoding="utf-8") as f:
         json.dump(data, f, indent=2, ensure_ascii=False)
 
-def list_student_ids() -> list:
+
+def list_student_ids():
     return sorted(load_students().keys())
 
-def get_student(student_id: str) -> dict | None:
+
+def get_student(student_id):
     return load_students().get(student_id)
 
-def add_student(data: dict) -> str:
+
+def add_student(data):
     students = load_students()
     # unique random id (short)
     new_id = f"student_{uuid.uuid4().hex[:8]}"
@@ -53,7 +61,8 @@ def add_student(data: dict) -> str:
     save_students(students)
     return new_id
 
-def update_student(student_id: str, updates: dict) -> str:
+
+def update_student(student_id, updates):
     students = load_students()
     if student_id not in students:
         return f"❌ {student_id} not found."
@@ -106,16 +115,18 @@ FAQ_SYNONYMS = [
     "community", "mentors", "progress tracking", "insights meaning"
 ]
 
-def _normalize(text: str) -> str:
-    t = text.lower().strip()
+
+def _normalize(text):
+    t = (text or "").lower().strip()
     # remove diacritics / normalize Arabic
     t = "".join(c for c in unicodedata.normalize("NFD", t) if unicodedata.category(c) != "Mn")
     t = re.sub(r"[^\w\s\u0600-\u06FF]", " ", t)
     t = re.sub(r"\s+", " ", t)
     return t
 
-def find_faq_answer(user_input: str, cutoff: float = 0.6) -> str | None:
-    if not user_input: 
+
+def find_faq_answer(user_input, cutoff=0.6):
+    if not user_input:
         return None
     ui = _normalize(user_input)
 
@@ -133,10 +144,9 @@ def find_faq_answer(user_input: str, cutoff: float = 0.6) -> str | None:
     best_q = max(candidates, key=lambda q: SequenceMatcher(None, ui, _normalize(q)).ratio())
     if SequenceMatcher(None, ui, _normalize(best_q)).ratio() >= cutoff:
         # map a synonym to a reasonable FAQ (heuristic)
-        # for stopping/resume
         if any(k in ui for k in ["نص الطريق", "لو وقفت", "stop halfway", "resume"]):
-            return FAQS["لو وقفت في نص الطريق؟"]
-        # else: return closest question’s answer if it exists
+            # key exists in FAQS
+            return FAQS.get("لو وقفت في نص الطريق؟") or FAQS.get("لو وقفت في نص الطريق") or FAQS.get("What if I stop halfway?")
         if best_q in FAQS:
             return FAQS[best_q]
     return None
@@ -165,7 +175,8 @@ Sections required:
 4) Milestones (by Phase)
 """
 
-def _compose_profile(student: dict) -> str:
+
+def _compose_profile(student):
     mapping = [
         ("learning_style", "Learning Style"),
         ("academic_progress", "Academic Progress"),
@@ -183,14 +194,15 @@ def _compose_profile(student: dict) -> str:
     ]
     parts = []
     for key, label in mapping:
-        val = student.get(key)
+        val = student.get(key) if student else None
         if isinstance(val, list):
             val = ", ".join(val)
         if val:
             parts.append(f"{label}: {val}")
     return " | ".join(parts) if parts else "No student data provided."
 
-def get_gemini_response(query: str, student: dict | None = None) -> str:
+
+def get_gemini_response(query, student=None):
     profile = _compose_profile(student or {})
     prompt = f"""Student Profile: {profile}
 
@@ -209,9 +221,11 @@ Formatting:
         except Exception as e:
             return f"(Gemini error fallback) {str(e)[:160]}"
     # fallback (no API key)
-    return f"(Simulated) {prompt[:500]}..."
+    # keep simulated short to avoid huge outputs in UI
+    return f"(Simulated) {prompt[:400]}..."
+
 
-def generate_ai_insights(student: dict) -> str:
+def generate_ai_insights(student):
     profile = _compose_profile(student or {})
     prompt = f"""Analyze the following student profile and provide insights:
 {profile}
@@ -233,7 +247,7 @@ Requirements:
 # -----------------------------
 # Chat logic
 # -----------------------------
-def chat(student_id: str, message: str) -> tuple[str, str, str]:
+def chat(student_id, message):
     """Returns (roadmap_text, insights_text, chatbot_response_text)"""
     roadmap, insights, reply = "", "", ""
     student = get_student(student_id)
@@ -269,6 +283,7 @@ ALL_FIELDS = [
     "preferred_study_environment", "community_groups"
 ]
 
+
 def create_student(
     learning_style, academic_progress, personality, interests, goals, level,
     preferred_methods, iq_level, eq_level, decision_making_style,
@@ -297,7 +312,8 @@ def create_student(
         gr.Dropdown.update(choices=list_student_ids(), value=new_id)
     )
 
-def load_student_to_form(student_id: str):
+
+def load_student_to_form(student_id):
     s = get_student(student_id)
     if not s:
         # return empties
@@ -318,6 +334,7 @@ def load_student_to_form(student_id: str):
         ", ".join(s.get("community_groups", [])),
     ]
 
+
 def apply_update(
     student_id,
     learning_style, academic_progress, personality, interests, goals, level,
@@ -458,6 +475,10 @@ with gr.Blocks(theme=THEME, css="""
             fn=load_student_to_form,
             inputs=[target_id],
             outputs=[u_learning_style, u_academic_progress, u_personality,
+                     u_interests, u_goals, u_level, u_preferred_methods,
+                     u_iq_level, u_eq_level, u_decision_style, u_motivation_level,
+                     u_study_env, u_u_community_groups] if False else
+                    [u_learning_style, u_academic_progress, u_personality,
                      u_interests, u_goals, u_level, u_preferred_methods,
                      u_iq_level, u_eq_level, u_decision_style, u_motivation_level,
                      u_study_env, u_community_groups]