Update app.py

app.py

@@ -1,225 +1,749 @@
+# from __future__ import annotations
+# import os
+# import json
+# import time
+# import uuid
+# from dataclasses import dataclass
+# from typing import List, Dict, Any
+
+# # --- Gradio & UI ---
+# import markdown
+# import gradio as gr
+
+# # --- LLM & Vector DB ---
+# from openai import OpenAI
+# import weaviate
+
+# # --- File Processing ---
+# import fitz  # PyMuPDF
+# import docx
+
+# # --- 1. BACKEND LOGIC & CONFIG ---
+
+# # --- LLM Configuration (DeepInfra) ---
+# MODEL_NAME = "openai/gpt-oss-120b"
+# # تأكد من وضع مفتاح API الخاص بك هنا
+# DEEPINFRA_API_KEY = os.getenv("DEEPINFRA_API_KEY", "kPEm10rrnxXrCf0TuB6Xcd7Y7lp3YgKa") 
+# BASE_URL = "https://api.deepinfra.com/v1/openai"
+
+# if not DEEPINFRA_API_KEY:
+#     print("[WARN] DEEPINFRA_API_KEY is not set. The chatbot will likely fail.")
+
+# llm_client = OpenAI(api_key=DEEPINFRA_API_KEY, base_url=BASE_URL)
+
+# # --- Weaviate RAG Configuration ---
+# WEAVIATE_URL = os.getenv("WEAVIATE_URL", "https://org-bgpt4w63nvf1yeuw.c0.us-west3.gcp.weaviate.cloud")
+# # تأكد من وضع مفتاح API الخاص بك هنا
+# WEAVIATE_API_KEY = os.getenv("WEAVIATE_API_KEY", "ZUd6clB5WmYzVGkxeU40cl96NTY5UkViUlVzY05Md3IzQ0JKelBZQmxGZHRPeGpCeGdxS1FUNnlYUkFFPV92MjAw")
+
+# try:
+#     weaviate_client = weaviate.Client(
+#         url=WEAVIATE_URL,
+#         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
+#     )
+#     print("[INFO] Successfully connected to Weaviate.")
+# except Exception as e:
+#     print(f"[ERROR] Failed to connect to Weaviate: {e}")
+#     weaviate_client = None
+
+# # --- Language & Routing Configuration ---
+# ARABIC_RANGE = (
+#     (0x0600, 0x06FF), (0x0750, 0x077F), (0x08A0, 0x08FF),
+#     (0xFB50, 0xFDFF), (0xFE70, 0xFEFF), (0x1EE00, 0x1EEFF)
+# )
+
+# @dataclass
+# class Route:
+#     audience: str
+#     intent: str
+#     language: str
+
+# # --- Knowledge Base (KB) for simple queries ---
+# KB: Dict[str, Dict[str, str]] = {
+#     "student_registration": {
+#         "en": "**How to register (Student)**\n\n1. Go to the EduNatives site and choose Sign Up.\n2. Use your university email and verify it.\n3. Complete your profile.",
+#         "ar": "**طريقة التسجيل (طلاب)**\n\n١. اذهب إلى موقع EduNatives واختر Sign Up.\n٢. استخدم إيميل الجامعة وأكده.\n٣. أكمل ملفك الشخصي.",
+#     },
+# }
+
+# # --- UPDATED: Keywords for intent routing, including RAG intents ---
+# KEYS = {
+#     # RAG Intents
+#     "find_job": ["job", "jobs", "career", "hiring", "وظيفة", "وظائف", "توظيف", "شغل"],
+#     "find_opportunity": ["intern", "internship", "scholarship", "opportunity", "training", "تدريب", "منحة", "فرصة"],
+#     "find_project": ["project", "projects", "research", "مشروع", "مشاريع", "بحث", "ابحاث"],
+#     "join_team": ["team", "join team", "find team", "فريق", "انضم لفريق", "تيم"],
+    
+#     # Standard Intents
+#     "student_registration": ["register", "sign up", "account", "تسجيل", "حساب"],
+#     "student_mentors": ["mentor", "advisor", "professor", "مشرف", "دكتور"],
+#     "university_publish": ["publish", "paper", "conference", "نشر", "مؤتمر"],
+# }
+
+# # --- Mapping intents to audiences ---
+# AUDIENCE_MAP = {
+#     "find_job": "student",
+#     "find_opportunity": "student",
+#     "find_project": "student",
+#     "join_team": "student",
+#     "student_registration": "student",
+#     "student_mentors": "student",
+#     "university_publish": "university",
+# }
+
+# # --- System Prompts ---
+# SYSTEM_PROMPT_BASE = (
+#     "You are **EduNatives Assistant**, a helpful, friendly, and precise academic/career guide for Students, Universities, and Companies. "
+#     "Reply in the user's language (Arabic/English). Be concise and action-oriented."
+# )
+
+# RAG_PROMPT_TEMPLATE = (
+#     "Based on the following information retrieved from our database, please answer the user's question. "
+#     "Format the results clearly (e.g., using a list or table). At the end, ask the user if they need help applying or have more questions.\n\n"
+#     "--- RETRIEVED DATA ---\n{retrieved_data}\n--- END DATA ---\n\n"
+# )
+
+# # --- 2. CORE FUNCTIONS ---
+
+# def log_interaction(data: Dict[str, Any]):
+#     """Appends interaction data to a JSONL file for analytics."""
+#     with open("interaction_log.jsonl", "a", encoding="utf-8") as f:
+#         f.write(json.dumps(data, ensure_ascii=False) + "\n")
+
+# def process_uploaded_file(file_obj: Any) -> str | None:
+#     """Extracts text from an uploaded file object."""
+#     if file_obj is None:
+#         return None
+#     file_path = file_obj.name
+#     file_name = os.path.basename(file_path)
+#     text_content = ""
+#     try:
+#         if file_name.lower().endswith(".pdf"):
+#             with fitz.open(file_path) as doc:
+#                 text_content = "".join(page.get_text() for page in doc)
+#         elif file_name.lower().endswith(".docx"):
+#             doc = docx.Document(file_path)
+#             text_content = "\n".join(para.text for para in doc.paragraphs)
+#         elif file_name.lower().endswith(".txt"):
+#             with open(file_path, "r", encoding="utf-8") as f:
+#                 text_content = f.read()
+#         else:
+#             return f"[Unsupported file type: {file_name}]"
+#         return text_content.strip()
+#     except Exception as e:
+#         print(f"[ERROR] Failed to process file {file_name}: {e}")
+#         return f"[Error processing file: {file_name}]"
+
+# def is_arabic(text: str) -> bool:
+#     """Checks if a string contains Arabic characters."""
+#     return any(any(a <= ord(ch) <= b for a, b in ARABIC_RANGE) for ch in text)
+
+# def route_intent(text: str, has_file: bool, forced_audience: str | None = None) -> Route:
+#     """Determines user intent based on keywords and context."""
+#     lang = "ar" if is_arabic(text) else "en"
+#     text_l = text.lower()
+    
+#     # Special intent for CV analysis
+#     if has_file and any(kw in text_l for kw in ["cv", "resume", "my skills", "سيرة ذاتية", "ملفي"]):
+#         return Route(audience="student", intent="analyze_cv_for_opportunities", language=lang)
+
+#     # Keyword-based routing
+#     match_label = None
+#     for label, kws in KEYS.items():
+#         if any(kw in text_l for kw in kws):
+#             match_label = label
+#             break
+    
+#     if match_label:
+#         audience = AUDIENCE_MAP.get(match_label, "general")
+#         if forced_audience:
+#             audience = forced_audience
+#         return Route(audience=audience, intent=match_label, language=lang)
+
+#     # Fallback to general intent
+#     return Route(audience=forced_audience or "general", intent="general", language=lang)
+
+# def call_llm(user_message: str, history: List[Dict[str, str]], system_prompt: str) -> str:
+#     """Generic function to call the LLM."""
+#     messages: List[Dict[str, str]] = [{"role": "system", "content": system_prompt}]
+#     messages.extend(history[-6:])  # Keep last 3 turns
+#     messages.append({"role": "user", "content": user_message})
+
+#     try:
+#         resp = llm_client.chat.completions.create(
+#             model=MODEL_NAME, messages=messages, temperature=0.6, top_p=0.9, max_tokens=4096,
+#         )
+#         return resp.choices[0].message.content or ""
+#     except Exception as e:
+#         print(f"[ERROR] LLM call failed: {e}")
+#         return "Sorry, I'm having trouble connecting to my brain right now. Please try again later."
+
+# def query_weaviate(class_name: str, query_text: str, properties: List[str], limit: int = 5) -> str:
+#     """Performs vector search on a Weaviate collection and formats the output."""
+#     if not weaviate_client:
+#         return "Database connection is not available."
+#     try:
+#         response = (
+#             weaviate_client.query
+#             .get(class_name, properties)
+#             .with_near_text({"concepts": [query_text]})
+#             .with_limit(limit)
+#             .do()
+#         )
+        
+#         results = response["data"]["Get"][class_name]
+#         if not results:
+#             return f"No matching {class_name.lower()} found."
+
+#         formatted_output = ""
+#         for i, item in enumerate(results):
+#             formatted_output += f"### Result {i+1}\n"
+#             for prop in properties:
+#                 if prop in item and item[prop]:
+#                     formatted_output += f"- **{prop.replace('_', ' ').title()}**: {item[prop]}\n"
+#             formatted_output += "\n"
+#         return formatted_output.strip()
+
+#     except Exception as e:
+#         print(f"[ERROR] Weaviate query failed for class '{class_name}': {e}")
+#         return f"An error occurred while searching for {class_name.lower()}."
+        
+# def analyze_cv_with_llm(cv_text: str) -> str:
+#     """Uses LLM to extract key skills and information from a CV."""
+#     prompt = (
+#         "Analyze the following CV text and extract the key information. "
+#         "Summarize it into a short phrase suitable for a vector search to find matching jobs or internships. "
+#         "Focus on technical skills, programming languages, field of study, and key experiences.\n\n"
+#         f"--- CV TEXT ---\n{cv_text}\n--- END CV TEXT ---"
+#     )
+#     return call_llm(prompt, [], SYSTEM_PROMPT_BASE)
+
+# # --- 3. GRADIO UI & EVENT HANDLERS ---
+
+# with gr.Blocks(css="""
+# .chatbot {height: 500px; overflow: auto;}
+# .user-bubble {background-color: #DCF8C6; padding: 10px; border-radius: 12px; max-width: 75%; float: right; clear: both; margin: 5px; word-wrap: break-word;}
+# .bot-bubble {background-color: #F1F0F0; padding: 10px; border-radius: 12px; max-width: 75%; float: left; clear: both; margin: 5px; word-wrap: break-word;}
+# .chatbox-container {display: flex; gap: 8px; margin-top: 10px;}
+# .bot-bubble table {border-collapse: collapse; width: 100%;}
+# .bot-bubble th, .bot-bubble td {border: 1px solid #ddd; padding: 8px; text-align: left;}
+# .bot-bubble th {background-color: #e9e9e9;}
+# """) as demo:
+#     gr.Markdown("# 🤖 EduNatives Assistant\nYour smart, bilingual guide for academic and career opportunities.")
+
+#     with gr.Row():
+#         audience_dd = gr.Dropdown(
+#             label="Audience",
+#             choices=["Auto", "Student", "University-Research", "Company"],
+#             value="Auto",
+#             interactive=True,
+#             info="Select your role. 'Auto' detects it from your message."
+#         )
+#         clear_btn = gr.Button("🧹 Clear Chat")
+
+#     status = gr.Markdown("Status: Ready.")
+#     chatbot_html = gr.HTML("<div class='chatbot' id='chatbot'></div>")
+#     chat_history_state = gr.State([])
+
+#     with gr.Row(elem_classes="chatbox-container"):
+#         msg = gr.Textbox(
+#             placeholder="اكتب سؤالك هنا... / Ask your question here...",
+#             lines=2, scale=4, autofocus=True,
+#         )
+#         file_uploader = gr.File(
+#             label="Upload Document (.txt, .pdf, .docx)",
+#             file_types=[".txt", ".pdf", ".docx"],
+#             interactive=True,
+#         )
+#         with gr.Column(scale=1, min_width=120):
+#             send_btn = gr.Button("➡️ Send", scale=1, variant="primary")
+
+#     def format_chat_html(history: List[Dict[str, str]]) -> str:
+#         """Converts chat history to styled HTML."""
+#         html = "<div class='chatbot'>"
+#         for message in history:
+#             role, content = message["role"], message["content"]
+#             bubble_class = "user-bubble" if role == "user" else "bot-bubble"
+#             html_content = markdown.markdown(content, extensions=['tables']) if role == "assistant" else content
+#             html += f"<div class='{bubble_class}'>{html_content}</div>"
+#         html += "</div>"
+#         return html
+
+#     def respond(user_text: str, file_obj: Any, history: List[Dict[str, str]], audience_choice: str):
+#         # 1. Process inputs
+#         document_text = process_uploaded_file(file_obj)
+#         if not user_text.strip() and not document_text:
+#             return "", format_chat_html(history), history, "Status: Please type a message or upload a file.", None
+
+#         user_message_for_history = user_text
+#         if document_text:
+#             file_name = os.path.basename(file_obj.name)
+#             user_message_for_history += f"\n\n*📎 [File Attached: {file_name}]*"
+        
+#         # 2. Route intent
+#         forced = {"Student": "student", "University-Research": "university", "Company": "company"}.get(audience_choice)
+#         route = route_intent(user_text, has_file=bool(document_text), forced_audience=forced)
+#         status_text = f"**Audience**: {route.audience} | **Intent**: {route.intent} | **Lang**: {route.language.upper()}"
+        
+#         # 3. Handle different intents
+#         answer = ""
+#         rag_query_text = user_text
+        
+#         # --- RAG Logic Branch ---
+#         if weaviate_client and route.intent in ["find_job", "find_opportunity", "find_project", "analyze_cv_for_opportunities"]:
+#             if route.intent == "analyze_cv_for_opportunities":
+#                 status_text += " | Analyzing CV..."
+#                 # Use LLM to get a search query from the CV
+#                 rag_query_text = analyze_cv_with_llm(document_text)
+                
+#             if "job" in route.intent or "cv" in route.intent:
+#                 retrieved_data = query_weaviate("Job", rag_query_text, ["title", "company", "location", "description"])
+#             elif "opportunity" in route.intent:
+#                 retrieved_data = query_weaviate("Opportunities", rag_query_text, ["title", "type", "organization", "summary"])
+#             elif "project" in route.intent:
+#                 retrieved_data = query_weaviate("Project", rag_query_text, ["title", "field", "university", "abstract"])
+            
+#             # Combine retrieved data with LLM for a natural response
+#             system_prompt = RAG_PROMPT_TEMPLATE.format(retrieved_data=retrieved_data)
+#             answer = call_llm(user_text, history, system_prompt)
+
+#         # --- KB/General LLM Logic Branch ---
+#         else:
+#             if route.intent in KB:
+#                 answer = KB[route.intent].get(route.language, KB[route.intent]["en"])
+#             else:
+#                 answer = call_llm(user_text, history, SYSTEM_PROMPT_BASE)
+
+#         # 4. Update history, log, and format for UI
+#         history.append({"role": "user", "content": user_message_for_history})
+#         history.append({"role": "assistant", "content": answer})
+        
+#         log_interaction({
+#             "timestamp": time.time(),
+#             "user_message": user_text,
+#             "file_uploaded": file_obj.name if file_obj else None,
+#             "audience": route.audience,
+#             "intent": route.intent,
+#             "language": route.language,
+#             "bot_response": answer
+#         })
+        
+#         updated_html = format_chat_html(history)
+#         return "", updated_html, history, status_text, None # Clear text input and file uploader
+
+#     def clear_chat():
+#         """Clears the chat history and UI components."""
+#         return "", [], "Status: Ready.", None
+
+#     # Event Handlers
+#     send_btn.click(respond, inputs=[msg, file_uploader, chat_history_state, audience_dd], outputs=[msg, chatbot_html, chat_history_state, status, file_uploader], queue=True)
+#     msg.submit(respond, inputs=[msg, file_uploader, chat_history_state, audience_dd], outputs=[msg, chatbot_html, chat_history_state, status, file_uploader], queue=True)
+#     clear_btn.click(clear_chat, outputs=[chatbot_html, chat_history_state, status, file_uploader], queue=False)
+
+# if __name__ == "__main__":
+#     demo.launch(debug=True)
+
+
+
+
+# app.py  -- Full EduNatives chatbot with RAG + Application + Team flows
 from __future__ import annotations
 import os
 import json
 import time
+import re
 import uuid
+import datetime
 from dataclasses import dataclass
-from typing import List, Dict, Any
+from typing import List, Dict, Any, Optional
 
-# --- Gradio & UI ---
 import markdown
 import gradio as gr
-
-# --- LLM & Vector DB ---
 from openai import OpenAI
-import weaviate
-
-# --- File Processing ---
 import fitz  # PyMuPDF
 import docx
+import weaviate
+from weaviate.classes.init import Auth
+from weaviate.classes.config import Configure, Property, DataType
 
-# --- 1. BACKEND LOGIC & CONFIG ---
+# -------------------- Configuration --------------------
+MODEL_NAME = os.getenv("MODEL_NAME", "openai/gpt-oss-120b")
+DEEPINFRA_API_KEY = os.getenv("DEEPINFRA_API_KEY", "kPEm10rrnxXrCf0TuB6Xcd7Y7lp3YgKa")
+BASE_URL = os.getenv("BASE_URL", "https://api.deepinfra.com/v1/openai")
 
-# --- LLM Configuration (DeepInfra) ---
-MODEL_NAME = "openai/gpt-oss-120b"
-# تأكد من وضع مفتاح API الخاص بك هنا
-DEEPINFRA_API_KEY = os.getenv("DEEPINFRA_API_KEY", "kPEm10rrnxXrCf0TuB6Xcd7Y7lp3YgKa") 
-BASE_URL = "https://api.deepinfra.com/v1/openai"
+WEAVIATE_URL = os.getenv("WEAVIATE_URL", "htorgbgpt4w63nvf1yeuw.c0.us-west3.gcp.weaviate.cloud")
+WEAVIATE_API_KEY = os.getenv("WEAVIATE_API_KEY", "ZUd6clB5WmYzVGkxeU40cl96NTY5UkViUlVzY05Md3IzQ0JKelBZQmxGZHRPeGpCeGdxS1FUNnlYUkFFPV92MjAw")
 
-if not DEEPINFRA_API_KEY:
-    print("[WARN] DEEPINFRA_API_KEY is not set. The chatbot will likely fail.")
+MEMORY_FILE = os.getenv("MEMORY_FILE", "chat_memory.json")
+LOG_FILE = os.getenv("LOG_FILE", "chat_analytics.json")
 
+# -------------------- Clients --------------------
+# LLM client
 llm_client = OpenAI(api_key=DEEPINFRA_API_KEY, base_url=BASE_URL)
 
-# --- Weaviate RAG Configuration ---
-WEAVIATE_URL = os.getenv("WEAVIATE_URL", "https://org-bgpt4w63nvf1yeuw.c0.us-west3.gcp.weaviate.cloud")
-# تأكد من وضع مفتاح API الخاص بك هنا
-WEAVIATE_API_KEY = os.getenv("WEAVIATE_API_KEY", "ZUd6clB5WmYzVGkxeU40cl96NTY5UkViUlVzY05Md3IzQ0JKelBZQmxGZHRPeGpCeGdxS1FUNnlYUkFFPV92MjAw")
-
-try:
-    weaviate_client = weaviate.Client(
-        url=WEAVIATE_URL,
-        auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
-    )
-    print("[INFO] Successfully connected to Weaviate.")
-except Exception as e:
-    print(f"[ERROR] Failed to connect to Weaviate: {e}")
-    weaviate_client = None
-
-# --- Language & Routing Configuration ---
-ARABIC_RANGE = (
-    (0x0600, 0x06FF), (0x0750, 0x077F), (0x08A0, 0x08FF),
-    (0xFB50, 0xFDFF), (0xFE70, 0xFEFF), (0x1EE00, 0x1EEFF)
+# Weaviate client
+weaviate_client = weaviate.connect_to_weaviate_cloud(
+    cluster_url=WEAVIATE_URL,
+    auth_credentials=Auth.api_key(WEAVIATE_API_KEY),
 )
 
-@dataclass
-class Route:
-    audience: str
-    intent: str
-    language: str
-
-# --- Knowledge Base (KB) for simple queries ---
+# -------------------- KB, Keys, prompts --------------------
 KB: Dict[str, Dict[str, str]] = {
     "student_registration": {
-        "en": "**How to register (Student)**\n\n1. Go to the EduNatives site and choose Sign Up.\n2. Use your university email and verify it.\n3. Complete your profile.",
-        "ar": "**طريقة التسجيل (طلاب)**\n\n١. اذهب إلى موقع EduNatives واختر Sign Up.\n٢. استخدم إيميل الجامعة وأكده.\n٣. أكمل ملفك الشخصي.",
+        "en": (
+            "**How to register / create an account (Student)**\n\n"
+            "1. Go to the EduNatives site and choose Sign Up.\n"
+            "2. Use your university email if possible and verify it.\n"
+            "3. Complete your profile (major, skills, interests).\n"
+            "4. Enable notifications for internships/scholarships."
+        ),
+        "ar": (
+            "**طريقة التسجيل وإنشاء حساب (طلاب)**\n\n"
+            "١. اذهب إلى موقع EduNatives واختر Sign Up.\n"
+            "٢. يفضل استخدام إيميل الجامعة وتأكيده.\n"
+            "٣. أكمل ملفك الشخصي (التخصص، المهارات، الاهتمامات).\n"
+            "٤. فعّل التنبيهات لفرص التدريب والمنح."
+        ),
+    },
+    "student_internships": {
+        "en": (
+            "**Finding internships & scholarships**\n\n"
+            "- Use the search filters: field, location, duration, paid/unpaid.\n"
+            "- Follow companies and set up alerts for new opportunities.\n"
+            "- Keep your profile and resume updated."
+        ),
+        "ar": (
+            "**كيفية العثور على تدريب أو منحة**\n\n"
+            "- استخدم فلاتر البحث: التخصص، المكان، المدة، مدفوع/غير مدفوع.\n"
+            "- تابع الشركات وفعّل التنبيهات للفرص الجديدة.\n"
+            "- حافظ على تحديث ملفك الشخصي وسيرتك الذاتية."
+        ),
     },
 }
 
-# --- UPDATED: Keywords for intent routing, including RAG intents ---
 KEYS = {
-    # RAG Intents
-    "find_job": ["job", "jobs", "career", "hiring", "وظيفة", "وظائف", "توظيف", "شغل"],
-    "find_opportunity": ["intern", "internship", "scholarship", "opportunity", "training", "تدريب", "منحة", "فرصة"],
-    "find_project": ["project", "projects", "research", "مشروع", "مشاريع", "بحث", "ابحاث"],
-    "join_team": ["team", "join team", "find team", "فريق", "انضم لفريق", "تيم"],
-    
-    # Standard Intents
-    "student_registration": ["register", "sign up", "account", "تسجيل", "حساب"],
-    "student_mentors": ["mentor", "advisor", "professor", "مشرف", "دكتور"],
-    "university_publish": ["publish", "paper", "conference", "نشر", "مؤتمر"],
+    "student_registration": ["register", "sign up", "signup", "create account", "account", "تسجيل", "انشاء", "إنشاء", "حساب", "اعمل حساب", "سجل"],
+    "student_internships": ["intern", "internship", "training", "scholar", "scholarship", "grant", "opportunity", "تدريب", "تدريبي", "منحة", "منح", "فرصة", "فرص", "انترنشيب"],
+    "student_mentors": ["mentor", "advisor", "professor", "supervisor", "faculty", "connect", "منتور", "مشرف", "دكتور", "أستاذ", "استاذ", "التواصل", "اكلم"],
+    "university_publish": ["publish", "paper", "research", "preprint", "conference", "event", "seminar", "webinar", "نشر", "أبحاث", "ابحاث", "بحث", "مؤتمر", "فعالية", "فعاليات", "ندوة", "ورشة"],
+    "university_connect": ["students", "connect with students", "reach students", "collaborate", "طلاب", "تواصل مع الطلاب", "التواصل مع الطلاب", "تعاون"],
+    "company_post_jobs": ["job", "jobs", "post job", "hiring", "hire", "internships", "graduate", "وظيفة", "وظائف", "اعلان", "إعلان", "نشر وظيفة", "توظيف", "فرص تدريب", "خريجين"],
+    "company_find_talent": ["talent", "candidate", "recruit", "search", "find", "pipeline", "موهبة", "مواهب", "مرشحين", "تعيين", "تجنيد", "ابحث", "دور على"],
+    "project_query": ["project", "projects", "مشروع", "مشاريع", "هدف", "أهداف"],
+    "apply_job_opportunity": ["apply", "application", "تقديم", "طلب", "عايز اقدم", "اريد التقديم", "اريد اتقدم"],
+    "join_team": ["team", "join team", "فريق", "انضمام لفريق", "انضمام", "انضم"],
 }
 
-# --- Mapping intents to audiences ---
 AUDIENCE_MAP = {
-    "find_job": "student",
-    "find_opportunity": "student",
-    "find_project": "student",
-    "join_team": "student",
     "student_registration": "student",
+    "student_internships": "student",
     "student_mentors": "student",
     "university_publish": "university",
+    "university_connect": "university",
+    "company_post_jobs": "company",
+    "company_find_talent": "company",
+    "project_query": "student",
+    "apply_job_opportunity": "student",
+    "join_team": "student",
 }
 
-# --- System Prompts ---
 SYSTEM_PROMPT_BASE = (
-    "You are **EduNatives Assistant**, a helpful, friendly, and precise academic/career guide for Students, Universities, and Companies. "
-    "Reply in the user's language (Arabic/English). Be concise and action-oriented."
+    "You are **EduNatives Assistant**, a helpful, friendly, and precise academic/career guide. "
+    "You serve three primary audiences: Students, Universities/Researchers, and Companies.\n\n"
+    "Goals by audience:\n"
+    "- Students: registration/account help; finding internships/scholarships; connecting with mentors or professors; querying projects; applying for jobs/opportunities; joining project teams.\n"
+    "- Universities/Researchers: publish research or announce events; connect/collaborate with students.\n"
+    "- Companies: post jobs/internships/graduate roles; discover student talent.\n\n"
+    "General rules:\n"
+    "- Reply in the user's language (Arabic if the user writes Arabic; otherwise English).\n"
+    "- Use data from Weaviate collections (Job, Opportunities, Project) when relevant.\n"
+    "- Be concise, step-by-step, and action-oriented (lists, bullets, checklists).\n"
+    "- If information is unavailable, state that clearly and suggest the next best step.\n"
+    "- For CV analysis, extract skills/experience and recommend matching opportunities.\n"
+    "- Assist with applications and team matching.\n"
+    "- Ensure that all generated prompts are phrased using positive reinforcement."
+)
+
+CONTEXT_INJECT_TEMPLATE = (
+    "Context to guide your answer (do not repeat verbatim):\n"
+    "- Audience: {audience}\n- Intent: {intent}\n- Extra hints: Keep it practical for this audience."
 )
 
-RAG_PROMPT_TEMPLATE = (
-    "Based on the following information retrieved from our database, please answer the user's question. "
-    "Format the results clearly (e.g., using a list or table). At the end, ask the user if they need help applying or have more questions.\n\n"
-    "--- RETRIEVED DATA ---\n{retrieved_data}\n--- END DATA ---\n\n"
+# -------------------- Utility helpers --------------------
+ARABIC_RANGE = (
+    (0x0600, 0x06FF), (0x0750, 0x077F), (0x08A0, 0x08FF),
+    (0xFB50, 0xFDFF), (0xFE70, 0xFEFF), (0x1EE00, 0x1EEFF)
 )
 
-# --- 2. CORE FUNCTIONS ---
+def is_arabic(text: str) -> bool:
+    for ch in text:
+        code = ord(ch)
+        for a, b in ARABIC_RANGE:
+            if a <= code <= b:
+                return True
+    return False
+
+def format_chat_html(history: List[Dict[str, str]]) -> str:
+    html = "<div class='chatbot'>"
+    for message in history:
+        role = message["role"]
+        content = message["content"]
+        if role == "user":
+            html += f"<div class='user-bubble'>{content}</div>"
+        elif role == "assistant":
+            html_content = markdown.markdown(content, extensions=['tables'])
+            html += f"<div class='bot-bubble'>{html_content}</div>"
+    html += "</div>"
+    return html
+
+# Simple keyword-based CV skills/experience extractor (improvable)
+_SKILL_REGEX = re.compile(r"\b(python|java|c\+\+|c#|javascript|nlp|machine learning|deep learning|data science|sql|aws|azure|docker|kubernetes|react|node\.js)\b", re.IGNORECASE)
+_EXP_REGEX = re.compile(r"(\d+)\s*(?:years|year|months|month)\s*(?:of)?\s*(?:experience|exp|worked)", re.IGNORECASE)
+
+def extract_skills_experience(text: str) -> Dict[str, List[str]]:
+    skills = list({m.group(0).lower() for m in _SKILL_REGEX.finditer(text)})
+    experiences = [m.group(0) for m in _EXP_REGEX.finditer(text)]
+    return {"skills": skills, "experience": experiences}
+
+# -------------------- Ensure auxiliary collections --------------------
+def ensure_aux_collections():
+    # Team
+    if not weaviate_client.collections.exists("Team"):
+        weaviate_client.collections.create(
+            name="Team",
+            properties=[
+                Property(name="teamId", data_type=DataType.TEXT),
+                Property(name="name", data_type=DataType.TEXT),
+                Property(name="projectId", data_type=DataType.TEXT),
+                Property(name="members", data_type=DataType.TEXT_ARRAY),
+                Property(name="createdAt", data_type=DataType.DATE),
+                Property(name="creatorId", data_type=DataType.TEXT),
+            ],
+            vectorizer_config=Configure.Vectorizer.none()
+        )
+
+    # Application
+    if not weaviate_client.collections.exists("Application"):
+        weaviate_client.collections.create(
+            name="Application",
+            properties=[
+                Property(name="applicationId", data_type=DataType.TEXT),
+                Property(name="jobId", data_type=DataType.TEXT),
+                Property(name="opportunityId", data_type=DataType.TEXT),
+                Property(name="applicantName", data_type=DataType.TEXT),
+                Property(name="applicantEmail", data_type=DataType.TEXT),
+                Property(name="coverLetter", data_type=DataType.TEXT),
+                Property(name="cvText", data_type=DataType.TEXT),
+                Property(name="createdAt", data_type=DataType.DATE),
+            ],
+            vectorizer_config=Configure.Vectorizer.none()
+        )
 
-def log_interaction(data: Dict[str, Any]):
-    """Appends interaction data to a JSONL file for analytics."""
-    with open("interaction_log.jsonl", "a", encoding="utf-8") as f:
-        f.write(json.dumps(data, ensure_ascii=False) + "\n")
+    # Memory
+    if not weaviate_client.collections.exists("Memory"):
+        weaviate_client.collections.create(
+            name="Memory",
+            properties=[
+                Property(name="memoryId", data_type=DataType.TEXT),
+                Property(name="sessionId", data_type=DataType.TEXT),
+                Property(name="text", data_type=DataType.TEXT),
+                Property(name="createdAt", data_type=DataType.DATE),
+            ],
+            vectorizer_config=Configure.Vectorizer.none()
+        )
+
+ensure_aux_collections()
+
+# -------------------- Weaviate query helpers (RAG) --------------------
+def query_weaviate_collection(class_name: str, query_text: str, limit: int = 5) -> List[dict]:
+    """
+    Query using v4 weaviate client (hybrid search).
+    """
+    try:
+        collection = weaviate_client.collections.get(class_name)
 
-def process_uploaded_file(file_obj: Any) -> str | None:
-    """Extracts text from an uploaded file object."""
-    if file_obj is None:
+        # Hybrid search
+        res = collection.query.hybrid(query=query_text, limit=limit)
+
+        items = [o.properties for o in res.objects]
+
+        # --- fallback لو مفيش نتيجة
+        if not items:
+            print(f"[Hybrid returned 0 → fallback filter on {class_name}]")
+            res2 = collection.query.fetch_objects(limit=limit)  # مجرد fallback بسيط
+            items = [o.properties for o in res2.objects]
+
+        return items
+    except Exception as e:
+        print(f"[Weaviate Query Error] class={class_name} error={e}")
+        return []
+
+
+def build_rag_prompt(user_question: str, retrieved_items: List[dict], class_name: str) -> str:
+    intro = f"Use the following {len(retrieved_items)} records from {class_name} to answer the question succinctly.\n\n"
+    parts = []
+    for i, item in enumerate(retrieved_items, 1):
+        if class_name == "Job":
+            parts.append(f"{i}. Title: {item.get('title','N/A')} | Company: {item.get('companyName','N/A')} | Skills: {', '.join(item.get('skills',[]))} | Desc: {item.get('description','')[:200]}")
+        elif class_name == "Opportunities":
+            parts.append(f"{i}. Title: {item.get('title','N/A')} | Topic: {item.get('topic','N/A')} | Skills: {', '.join(item.get('skills',[]))} | Overview: {item.get('overview','')[:200]}")
+        elif class_name == "Project":
+            parts.append(f"{i}. Title: {item.get('title','N/A')} | ShortDesc: {item.get('shortDescription','')[:200]} | Fields: {', '.join(item.get('fields',[]))}")
+        else:
+            parts.append(f"{i}. {str(item)[:200]}")
+    context_block = "\n".join(parts)
+    closing = f"\n\nQuestion: {user_question}\nAnswer concisely and, if applicable, include next steps (how to apply / contact / form a team)."
+    return intro + context_block + closing
+
+def rag_answer(user_question: str, class_name: str, top_k: int = 5) -> (str, List[dict]):
+    retrieved = query_weaviate_collection(class_name, user_question, limit=top_k)
+    if not retrieved:
+        return "", []
+    prompt = build_rag_prompt(user_question, retrieved, class_name)
+    try:
+        resp = llm_client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT_BASE},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.2,
+            max_tokens=512
+        )
+        answer = resp.choices[0].message.content or ""
+    except Exception as e:
+        print(f"[RAG LLM Error] {e}")
+        answer = ""
+    return answer, retrieved
+
+# -------------------- Save helpers --------------------
+def save_application_to_weaviate(application: dict) -> bool:
+    try:
+        collection = weaviate_client.collections.get("Application")
+        # ensure createdAt exists
+        application.setdefault("createdAt", datetime.datetime.utcnow().isoformat() + "Z")
+        # add stable uuid
+        uid = str(uuid.uuid5(uuid.NAMESPACE_DNS, application.get("applicationId", str(uuid.uuid4()))))
+        collection.data.insert(properties=application, uuid=uid)
+        return True
+    except Exception as e:
+        print(f"[Save Application Error] {e}")
+        return False
+
+def save_team_to_weaviate(team_props: dict) -> Optional[dict]:
+    try:
+        collection = weaviate_client.collections.get("Team")
+        team_props.setdefault("createdAt", datetime.datetime.utcnow().isoformat() + "Z")
+        uid = str(uuid.uuid5(uuid.NAMESPACE_DNS, team_props.get("teamId", str(uuid.uuid4()))))
+        collection.data.insert(properties=team_props, uuid=uid)
+        return team_props
+    except Exception as e:
+        print(f"[Save Team Error] {e}")
+        return None
+
+def save_memory_to_weaviate(session_id: str, text: str) -> bool:
+    try:
+        collection = weaviate_client.collections.get("Memory")
+        mem = {"memoryId": str(uuid.uuid4()), "sessionId": session_id, "text": text, "createdAt": datetime.datetime.utcnow().isoformat() + "Z"}
+        collection.data.insert(properties=mem, uuid=str(uuid.uuid5(uuid.NAMESPACE_DNS, mem["memoryId"])))
+        return True
+    except Exception as e:
+        print(f"[Save Memory Error] {e}")
+        return False
+
+# -------------------- File processing --------------------
+def process_uploaded_file(file_obj: Any) -> dict | None:
+    """
+    file_obj is a Gradio file (file_obj.name present)
+    returns dict with 'content' and 'profile' keys or None
+    """
+    if not file_obj:
         return None
     file_path = file_obj.name
-    file_name = os.path.basename(file_path)
+    filename = os.path.basename(file_path)
     text_content = ""
     try:
-        if file_name.lower().endswith(".pdf"):
+        if filename.lower().endswith(".pdf"):
             with fitz.open(file_path) as doc:
-                text_content = "".join(page.get_text() for page in doc)
-        elif file_name.lower().endswith(".docx"):
+                for page in doc:
+                    text_content += page.get_text()
+        elif filename.lower().endswith(".docx"):
             doc = docx.Document(file_path)
-            text_content = "\n".join(para.text for para in doc.paragraphs)
-        elif file_name.lower().endswith(".txt"):
+            for p in doc.paragraphs:
+                text_content += p.text + "\n"
+        elif filename.lower().endswith(".txt"):
             with open(file_path, "r", encoding="utf-8") as f:
                 text_content = f.read()
         else:
-            return f"[Unsupported file type: {file_name}]"
-        return text_content.strip()
+            return {"error": f"Unsupported file type: {filename}"}
+        profile = extract_skills_experience(text_content)
+        return {"content": text_content.strip(), "profile": profile, "filename": filename}
     except Exception as e:
-        print(f"[ERROR] Failed to process file {file_name}: {e}")
-        return f"[Error processing file: {file_name}]"
+        print(f"[File process error] {e}")
+        return {"error": f"Error processing file {filename}: {e}"}
 
-def is_arabic(text: str) -> bool:
-    """Checks if a string contains Arabic characters."""
-    return any(any(a <= ord(ch) <= b for a, b in ARABIC_RANGE) for ch in text)
+# -------------------- Logging --------------------
+def log_interaction(user_message: str, route: 'Route', response: str):
+    entry = {"timestamp": time.time(), "user_message": user_message, "audience": route.audience, "intent": route.intent, "language": route.language, "response": response}
+    try:
+        if os.path.exists(LOG_FILE):
+            with open(LOG_FILE, "r", encoding="utf-8") as f:
+                logs = json.load(f)
+        else:
+            logs = []
+        logs.append(entry)
+        with open(LOG_FILE, "w", encoding="utf-8") as f:
+            json.dump(logs, f, ensure_ascii=False, indent=2)
+    except Exception as e:
+        print(f"[Log error] {e}")
 
-def route_intent(text: str, has_file: bool, forced_audience: str | None = None) -> Route:
-    """Determines user intent based on keywords and context."""
-    lang = "ar" if is_arabic(text) else "en"
-    text_l = text.lower()
-    
-    # Special intent for CV analysis
-    if has_file and any(kw in text_l for kw in ["cv", "resume", "my skills", "سيرة ذاتية", "ملفي"]):
-        return Route(audience="student", intent="analyze_cv_for_opportunities", language=lang)
+# -------------------- Intent routing --------------------
+@dataclass
+class Route:
+    audience: str
+    intent: str
+    language: str
 
-    # Keyword-based routing
+def route_intent(text: str, forced_audience: str | None = None) -> Route:
+    lang = "ar" if is_arabic(text) else "en"
+    text_l = text.lower() if text else ""
     match_label = None
     for label, kws in KEYS.items():
-        if any(kw in text_l for kw in kws):
-            match_label = label
+        for kw in kws:
+            if kw in text_l:
+                match_label = label
+                break
+        if match_label:
             break
-    
-    if match_label:
-        audience = AUDIENCE_MAP.get(match_label, "general")
-        if forced_audience:
-            audience = forced_audience
-        return Route(audience=audience, intent=match_label, language=lang)
-
-    # Fallback to general intent
-    return Route(audience=forced_audience or "general", intent="general", language=lang)
-
-def call_llm(user_message: str, history: List[Dict[str, str]], system_prompt: str) -> str:
-    """Generic function to call the LLM."""
-    messages: List[Dict[str, str]] = [{"role": "system", "content": system_prompt}]
-    messages.extend(history[-6:])  # Keep last 3 turns
+    audience = AUDIENCE_MAP.get(match_label, "general")
+    if forced_audience and forced_audience in {"student", "university", "company"}:
+        audience = forced_audience
+    return Route(audience=audience, intent=match_label or "general", language=lang)
+
+# -------------------- call_llm --------------------
+def call_llm(user_message: str, history: List[Dict[str, str]], route: Route, temperature: float = 0.6, max_tokens: int = 512) -> str:
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT_BASE},
+        {"role": "system", "content": CONTEXT_INJECT_TEMPLATE.format(audience=route.audience, intent=route.intent)}
+    ]
+    # include last few turns
+    MAX_TURNS = 3
+    trimmed = history[-MAX_TURNS*2:] if history else []
+    messages.extend(trimmed)
     messages.append({"role": "user", "content": user_message})
-
     try:
         resp = llm_client.chat.completions.create(
-            model=MODEL_NAME, messages=messages, temperature=0.6, top_p=0.9, max_tokens=4096,
+            model=MODEL_NAME,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens
         )
         return resp.choices[0].message.content or ""
     except Exception as e:
-        print(f"[ERROR] LLM call failed: {e}")
-        return "Sorry, I'm having trouble connecting to my brain right now. Please try again later."
-
-def query_weaviate(class_name: str, query_text: str, properties: List[str], limit: int = 5) -> str:
-    """Performs vector search on a Weaviate collection and formats the output."""
-    if not weaviate_client:
-        return "Database connection is not available."
-    try:
-        response = (
-            weaviate_client.query
-            .get(class_name, properties)
-            .with_near_text({"concepts": [query_text]})
-            .with_limit(limit)
-            .do()
-        )
-        
-        results = response["data"]["Get"][class_name]
-        if not results:
-            return f"No matching {class_name.lower()} found."
-
-        formatted_output = ""
-        for i, item in enumerate(results):
-            formatted_output += f"### Result {i+1}\n"
-            for prop in properties:
-                if prop in item and item[prop]:
-                    formatted_output += f"- **{prop.replace('_', ' ').title()}**: {item[prop]}\n"
-            formatted_output += "\n"
-        return formatted_output.strip()
-
-    except Exception as e:
-        print(f"[ERROR] Weaviate query failed for class '{class_name}': {e}")
-        return f"An error occurred while searching for {class_name.lower()}."
-        
-def analyze_cv_with_llm(cv_text: str) -> str:
-    """Uses LLM to extract key skills and information from a CV."""
-    prompt = (
-        "Analyze the following CV text and extract the key information. "
-        "Summarize it into a short phrase suitable for a vector search to find matching jobs or internships. "
-        "Focus on technical skills, programming languages, field of study, and key experiences.\n\n"
-        f"--- CV TEXT ---\n{cv_text}\n--- END CV TEXT ---"
-    )
-    return call_llm(prompt, [], SYSTEM_PROMPT_BASE)
-
-# --- 3. GRADIO UI & EVENT HANDLERS ---
+        print(f"[LLM Error] {e}")
+        return ""
 
+# -------------------- Main respond flow (Gradio) --------------------
 with gr.Blocks(css="""
 .chatbot {height: 500px; overflow: auto;}
 .user-bubble {background-color: #DCF8C6; padding: 10px; border-radius: 12px; max-width: 75%; float: right; clear: both; margin: 5px; word-wrap: break-word;}
@@ -229,116 +753,222 @@ with gr.Blocks(css="""
 .bot-bubble th, .bot-bubble td {border: 1px solid #ddd; padding: 8px; text-align: left;}
 .bot-bubble th {background-color: #e9e9e9;}
 """) as demo:
-    gr.Markdown("# 🤖 EduNatives Assistant\nYour smart, bilingual guide for academic and career opportunities.")
+
+    gr.Markdown("# 🤖 EduNatives Assistant")
 
     with gr.Row():
-        audience_dd = gr.Dropdown(
-            label="Audience",
-            choices=["Auto", "Student", "University-Research", "Company"],
-            value="Auto",
-            interactive=True,
-            info="Select your role. 'Auto' detects it from your message."
-        )
+        audience_dd = gr.Dropdown(label="Audience", choices=["Auto", "Student", "University-Research", "Company"], value="Auto", interactive=True)
         clear_btn = gr.Button("🧹 Clear Chat")
 
     status = gr.Markdown("Status: Ready.")
     chatbot_html = gr.HTML("<div class='chatbot' id='chatbot'></div>")
     chat_history_state = gr.State([])
+    user_id_state = gr.State("default_user")
 
     with gr.Row(elem_classes="chatbox-container"):
-        msg = gr.Textbox(
-            placeholder="اكتب سؤالك هنا... / Ask your question here...",
-            lines=2, scale=4, autofocus=True,
-        )
-        file_uploader = gr.File(
-            label="Upload Document (.txt, .pdf, .docx)",
-            file_types=[".txt", ".pdf", ".docx"],
-            interactive=True,
-        )
+        msg = gr.Textbox(placeholder="اكتب سؤالك هنا... / Ask your question here...", lines=2, scale=4, autofocus=True)
+        file_uploader = gr.File(label="Upload Document (.txt, .pdf, .docx)", file_types=[".txt", ".pdf", ".docx"], file_count="single", interactive=True)
         with gr.Column(scale=1, min_width=120):
             send_btn = gr.Button("➡️ Send", scale=1, variant="primary")
 
-    def format_chat_html(history: List[Dict[str, str]]) -> str:
-        """Converts chat history to styled HTML."""
-        html = "<div class='chatbot'>"
-        for message in history:
-            role, content = message["role"], message["content"]
-            bubble_class = "user-bubble" if role == "user" else "bot-bubble"
-            html_content = markdown.markdown(content, extensions=['tables']) if role == "assistant" else content
-            html += f"<div class='{bubble_class}'>{html_content}</div>"
-        html += "</div>"
-        return html
-
-    def respond(user_text: str, file_obj: Any, history: List[Dict[str, str]], audience_choice: str):
-        # 1. Process inputs
-        document_text = process_uploaded_file(file_obj)
-        if not user_text.strip() and not document_text:
-            return "", format_chat_html(history), history, "Status: Please type a message or upload a file.", None
+    def respond(user_text: str, file_obj: Any, history: List[Dict[str, str]], audience_choice: str, user_id: str):
+        user_text = (user_text or "").strip()
+        # process file if exists
+        doc_info = process_uploaded_file(file_obj) if file_obj else None
+        if not user_text and not doc_info:
+            return "", format_chat_html(history), history, "Status: Please type a message or upload a file.", None, user_id
+
+        # build combined input
+        llm_input = user_text
+        if doc_info and "content" in doc_info:
+            llm_input = f"Based on the document content below, answer the question.\n\n---DOCUMENT---\n{doc_info['content'][:6000]}\n---END DOCUMENT---\n\nQuestion: {user_text}"
 
-        user_message_for_history = user_text
-        if document_text:
-            file_name = os.path.basename(file_obj.name)
-            user_message_for_history += f"\n\n*📎 [File Attached: {file_name}]*"
-        
-        # 2. Route intent
         forced = {"Student": "student", "University-Research": "university", "Company": "company"}.get(audience_choice)
-        route = route_intent(user_text, has_file=bool(document_text), forced_audience=forced)
+        route = route_intent(llm_input, forced_audience=forced)
         status_text = f"**Audience**: {route.audience} | **Intent**: {route.intent} | **Lang**: {route.language.upper()}"
-        
-        # 3. Handle different intents
-        answer = ""
-        rag_query_text = user_text
-        
-        # --- RAG Logic Branch ---
-        if weaviate_client and route.intent in ["find_job", "find_opportunity", "find_project", "analyze_cv_for_opportunities"]:
-            if route.intent == "analyze_cv_for_opportunities":
-                status_text += " | Analyzing CV..."
-                # Use LLM to get a search query from the CV
-                rag_query_text = analyze_cv_with_llm(document_text)
-                
-            if "job" in route.intent or "cv" in route.intent:
-                retrieved_data = query_weaviate("Job", rag_query_text, ["title", "company", "location", "description"])
-            elif "opportunity" in route.intent:
-                retrieved_data = query_weaviate("Opportunities", rag_query_text, ["title", "type", "organization", "summary"])
-            elif "project" in route.intent:
-                retrieved_data = query_weaviate("Project", rag_query_text, ["title", "field", "university", "abstract"])
-            
-            # Combine retrieved data with LLM for a natural response
-            system_prompt = RAG_PROMPT_TEMPLATE.format(retrieved_data=retrieved_data)
-            answer = call_llm(user_text, history, system_prompt)
 
-        # --- KB/General LLM Logic Branch ---
-        else:
-            if route.intent in KB:
-                answer = KB[route.intent].get(route.language, KB[route.intent]["en"])
+        # quick CV skills if provided
+        cv_profile = doc_info.get("profile") if doc_info and "profile" in doc_info else {"skills": [], "experience": []}
+
+        # Decide RAG target based on intent / keywords
+        text_lower = (user_text or "").lower()
+        wants_project = any(k in text_lower for k in KEYS.get("project_query", []))
+        wants_job = any(k in text_lower for k in KEYS.get("company_post_jobs", [])) or any(k in text_lower for k in KEYS.get("apply_job_opportunity", []))
+        wants_opp = any(k in text_lower for k in KEYS.get("student_internships", []))
+
+        final_answer = ""
+        retrieved = []
+
+        try:
+            if wants_project or route.intent == "project_query":
+                final_answer, retrieved = rag_answer(user_text or (doc_info or {}).get("content",""), "Project", top_k=5)
+                if not final_answer:
+                    final_answer = KB.get("student_internships", {}).get(route.language, "") if route.intent == "student_internships" else ""
+            elif wants_job or route.intent == "apply_job_opportunity" or route.intent == "company_post_jobs":
+                # try jobs first
+                final_answer, retrieved = rag_answer(user_text or (doc_info or {}).get("content",""), "Job", top_k=5)
+                if not final_answer:
+                    # fallback to ops
+                    final_answer, retrieved = rag_answer(user_text or (doc_info or {}).get("content",""), "Opportunities", top_k=5)
+            elif wants_opp or route.intent == "student_internships":
+                final_answer, retrieved = rag_answer(user_text or (doc_info or {}).get("content",""), "Opportunities", top_k=5)
             else:
-                answer = call_llm(user_text, history, SYSTEM_PROMPT_BASE)
+                # default LLM response with context
+                final_answer = call_llm(llm_input, history, route) or KB.get("student_registration", {}).get(route.language, "Sorry, I don't have info.")
+        except Exception as e:
+            print(f"[Respond Error] {e}")
+            final_answer = call_llm(llm_input, history, route) or KB.get("student_registration", {}).get(route.language, "")
+
+        # --- Application flow: if user indicated they want to apply (keywords) ---
+        wants_apply = any(kw in text_lower for kw in KEYS.get("apply_job_opportunity", []))
+        app_summary = ""
+        generated_cover = ""
+        if wants_apply:
+            target = retrieved[0] if retrieved else None
+            cover_text = ""
+            if target:
+                # build cover letter prompt using target and CV text if present
+                cover_prompt = f"Write a concise 3-paragraph cover letter applying for this role:\nRole details: {json.dumps(target, ensure_ascii=False)[:1500]}\n"
+                if doc_info and "content" in doc_info:
+                    cover_prompt += f"\nApplicant CV summary: {doc_info['content'][:2000]}\n"
+                cover_prompt += "\nWrite the letter in the same language as the user."
+                try:
+                    resp = llm_client.chat.completions.create(
+                        model=MODEL_NAME,
+                        messages=[{"role":"system","content":SYSTEM_PROMPT_BASE},{"role":"user","content":cover_prompt}],
+                        temperature=0.3,
+                        max_tokens=512
+                    )
+                    cover_text = resp.choices[0].message.content or ""
+                except Exception as e:
+                    print(f"[Cover generation error] {e}")
+                    cover_text = "I can help craft a cover letter, but an error occurred while generating it."
+
+                application = {
+                    "applicationId": str(uuid.uuid4()),
+                    "jobId": target.get("id") or target.get("jobId"),
+                    "opportunityId": target.get("id") or target.get("opportunityId"),
+                    "applicantName": "Unknown",
+                    "applicantEmail": "Unknown",
+                    "coverLetter": cover_text,
+                    "cvText": doc_info.get("content","")[:4000] if doc_info else "",
+                    "createdAt": datetime.datetime.now().isoformat()
+                }
+                saved = save_application_to_weaviate(application)
+                app_summary = "✅ Application prepared and saved." if saved else "⚠️ Application could not be saved."
+                generated_cover = cover_text
+            else:
+                app_summary = "لم أجد وظيفة/فرصة مناسبة تلقائياً من النتائج. أرسل عنوان الوظيفة أو اختر من النتيجة."
+
+        # --- Team creation flow (join_team) ---
+        team_created_msg = ""
+        wants_team = any(k in text_lower for k in KEYS.get("join_team", []))
+        if wants_team and (wants_project or route.intent == "join_team"):
+            # attempt to suggest members using cv skills or create empty team entry
+            suggested_members = []
+            if cv_profile and cv_profile.get("skills"):
+                # use skills to search opportunities maybe get studentName fields
+                matches = query_weaviate_collection("Opportunities", " ".join(cv_profile.get("skills", [])), limit=5)
+                for m in matches:
+                    name = m.get("studentName") or m.get("student") or m.get("name")
+                    if name:
+                        suggested_members.append(name)
+            if not suggested_members:
+                team_props = {
+                    "teamId": str(uuid.uuid4()),
+                    "name": f"Team for project - {uuid.uuid4().hex[:6]}",
+                    "projectId": retrieved[0].get("globalId") if retrieved and retrieved[0].get("globalId") else None,
+                    "members": [],
+                    "createdAt": datetime.datetime.utcnow().isoformat()+"Z",
+                    "creatorId": user_id
+                }
+                saved_team = save_team_to_weaviate(team_props)
+                if saved_team:
+                    team_created_msg = f"✅ Team created with id {team_props['teamId']}. يمكنك إضافة أعضاء لاحقًا."
+                else:
+                    team_created_msg = "⚠️ لم أتمكن من إنشاء الفريق الآن."
+            else:
+                team_props = {
+                    "teamId": str(uuid.uuid4()),
+                    "name": f"Team for project - {uuid.uuid4().hex[:6]}",
+                    "projectId": retrieved[0].get("globalId") if retrieved and retrieved[0].get("globalId") else None,
+                    "members": suggested_members,
+                    "createdAt": datetime.datetime.utcnow().isoformat()+"Z",
+                    "creatorId": user_id
+                }
+                saved_team = save_team_to_weaviate(team_props)
+                if saved_team:
+                    team_created_msg = f"✅ Team created with members: {', '.join(suggested_members)}"
+                else:
+                    team_created_msg = "⚠️ لم أتمكن من إنشاء الفريق الآن."
+
+        # Save a short memory entry
+        try:
+            sess = str(uuid.uuid5(uuid.NAMESPACE_DNS, (user_id or "anon") + (user_text or "")[:50]))
+            mem_text = f"User: {user_text[:300]} | Action: RAG on { 'Project' if wants_project else 'Job' if wants_job else 'Opportunities' if wants_opp else 'LLM' }"
+            save_memory_to_weaviate(sess, mem_text)
+        except Exception as e:
+            print(f"[Memory Save Error] {e}")
+
+        # Prepare final message (answer + top results + app/team status)
+        message_parts = []
+        if final_answer:
+            message_parts.append(final_answer)
+        if retrieved:
+            list_lines = []
+            for item in retrieved[:5]:
+                title = item.get("title") or item.get("jobTitle") or item.get("globalId") or "No Title"
+                meta = item.get("companyName") or item.get("topic") or item.get("shortDescription","")
+                list_lines.append(f"- **{title}** | {meta}")
+            if list_lines:
+                message_parts.append("\n\n**Top results:**\n" + "\n".join(list_lines))
+        if wants_apply:
+            message_parts.append("\n\n**Application status:** " + app_summary)
+            if generated_cover:
+                message_parts.append("\n\n**Generated Cover Letter:**\n" + generated_cover)
+        if team_created_msg:
+            message_parts.append("\n\n" + team_created_msg)
+
+        final_message_to_user = "\n\n".join([p for p in message_parts if p])
+
+        # Update history
+        user_message_for_history = user_text
+        if doc_info and doc_info.get("filename"):
+            user_message_for_history += f"\n\n*📎 [File Attached: {doc_info.get('filename')}] *"
 
-        # 4. Update history, log, and format for UI
         history.append({"role": "user", "content": user_message_for_history})
-        history.append({"role": "assistant", "content": answer})
-        
-        log_interaction({
-            "timestamp": time.time(),
-            "user_message": user_text,
-            "file_uploaded": file_obj.name if file_obj else None,
-            "audience": route.audience,
-            "intent": route.intent,
-            "language": route.language,
-            "bot_response": answer
-        })
-        
-        updated_html = format_chat_html(history)
-        return "", updated_html, history, status_text, None # Clear text input and file uploader
+        history.append({"role": "assistant", "content": final_message_to_user or "عذراً، لم أجد إجابة مناسبة الآن."})
 
-    def clear_chat():
-        """Clears the chat history and UI components."""
-        return "", [], "Status: Ready.", None
+        # log interaction
+        try:
+            log_interaction(user_text, route, final_message_to_user)
+        except Exception:
+            pass
 
-    # Event Handlers
-    send_btn.click(respond, inputs=[msg, file_uploader, chat_history_state, audience_dd], outputs=[msg, chatbot_html, chat_history_state, status, file_uploader], queue=True)
-    msg.submit(respond, inputs=[msg, file_uploader, chat_history_state, audience_dd], outputs=[msg, chatbot_html, chat_history_state, status, file_uploader], queue=True)
-    clear_btn.click(clear_chat, outputs=[chatbot_html, chat_history_state, status, file_uploader], queue=False)
+        # Return values: clear input field, updated html, updated history, status, clear file uploader, keep user_id
+        return "", format_chat_html(history), history, status_text, None, user_id
+
+    def clear_chat():
+        return "", [], "Status: Ready.", None, "default_user"
+
+    # Bind events
+    send_btn.click(
+        respond,
+        inputs=[msg, file_uploader, chat_history_state, audience_dd, user_id_state],
+        outputs=[msg, chatbot_html, chat_history_state, status, file_uploader, user_id_state],
+        queue=True
+    )
+    msg.submit(
+        respond,
+        inputs=[msg, file_uploader, chat_history_state, audience_dd, user_id_state],
+        outputs=[msg, chatbot_html, chat_history_state, status, file_uploader, user_id_state],
+        queue=True
+    )
+    clear_btn.click(
+        clear_chat,
+        outputs=[msg, chatbot_html, chat_history_state, status, file_uploader, user_id_state],
+        queue=False
+    )
 
 if __name__ == "__main__":
     demo.launch(debug=True)