Add files using upload-large-folder tool

.gitattributes

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.gitignore

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+push_to_space.py
+temp_space/
+temp_hf_space/
+scrub_rules.txt
+.ipynb_checkpoints/
+*.ipynb
+repo.zip
+data.csv
+*.h5
+*.pkl
+mental_xlmr_final/
+UI/safespace/build/
+UI/safespace/.dart_tool/
+UI/safespace/.pub-cache/
+UI/safespace/.flutter-plugins
+UI/safespace/.flutter-plugins-dependencies
+UI/safespace/node_modules/
+*.log
+.DS_Store

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api.py

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+import os
+from datetime import datetime
+import hashlib
+import logging
+import time
+
+import httpx
+from fastapi import FastAPI, HTTPException, Depends, Request
+from fastapi.responses import HTMLResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from typing import Optional
+
+from core_ai import predict_text, predict_survey, fuse_scores
+from recommendations import get_recommendations
+
+# --- DATABASE SETUP ---
+from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, JSON, Text, Boolean, Index
+from sqlalchemy.orm import declarative_base, sessionmaker, Session
+
+DATABASE_URL = os.environ.get("DATABASE_URL")
+if DATABASE_URL and DATABASE_URL.startswith("postgres://"):
+    DATABASE_URL = DATABASE_URL.replace("postgres://", "postgresql://", 1)
+
+engine = create_engine(DATABASE_URL, connect_args={'connect_timeout': 5}, pool_pre_ping=True) if DATABASE_URL else None
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) if engine else None
+Base = declarative_base()
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("safespace.api")
+
+
+class DBUser(Base):
+    __tablename__ = "users"
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=True)
+    email = Column(String, unique=True, index=True)
+    password = Column(String)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+
+class DBAnalysis(Base):
+    __tablename__ = "analyses"
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(Integer, index=True, nullable=True)
+    primary_condition = Column(String)
+    clinical_scoring = Column(JSON)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    text_input = Column(Text, nullable=True)
+    text_input_hash = Column(Text, nullable=True)
+    text_scores = Column(JSON, nullable=True)
+    survey_scores = Column(JSON, nullable=True)
+    fused_scores = Column(JSON, nullable=True)
+    severity = Column(Text, nullable=True)
+    cause = Column(Text, nullable=True)
+    suicidal_flag = Column(Boolean, default=False)
+    model_version = Column(Text, nullable=True)
+    app_version = Column(Text, nullable=True)
+    locale = Column(Text, nullable=True)
+
+
+Index("ix_analyses_user_id_created_at", DBAnalysis.user_id, DBAnalysis.created_at)
+
+
+class DBCheckin(Base):
+    __tablename__ = "checkins"
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(Integer, index=True, nullable=False)
+    mood = Column(Integer, nullable=False)
+    sleep = Column(Integer, nullable=False)
+    energy = Column(Float, nullable=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+
+Index("ix_checkins_user_id_created_at", DBCheckin.user_id, DBCheckin.created_at)
+
+
+class DBJournalEntry(Base):
+    __tablename__ = "journal_entries"
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(Integer, index=True, nullable=True)
+    content = Column(Text, nullable=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, nullable=True)
+
+# --- APP SETUP ---
+app = FastAPI(title="SafeSpace API", version="1.0.0")
+
+
+@app.middleware("http")
+async def log_requests(request: Request, call_next):
+    start_time = time.time()
+    response = await call_next(request)
+    duration_ms = int((time.time() - start_time) * 1000)
+    logger.info(
+        "%s %s %s %sms",
+        request.method,
+        request.url.path,
+        response.status_code,
+        duration_ms,
+    )
+    return response
+
+
+@app.on_event("startup")
+async def startup_event():
+    import asyncio
+    if engine:
+        try:
+            await asyncio.wait_for(
+                asyncio.to_thread(Base.metadata.create_all, bind=engine),
+                timeout=8.0
+            )
+            logger.info("Database connected and tables verified.")
+        except asyncio.TimeoutError:
+            logger.warning("Database connection timed out during startup - server will start without DB verification.")
+        except Exception as e:
+            logger.exception("Database connection failed during startup: %s", e)
+    logger.info("Application startup complete.")
+
+
+def get_db():
+    if not SessionLocal:
+        yield None
+    else:
+        db = SessionLocal()
+        try:
+            yield db
+        finally:
+            db.close()
+
+# Add CORS so Flutter app can communicate with it
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- Password Hashing ---
+def hash_password(password: str) -> str:
+    return hashlib.sha256(password.encode()).hexdigest()
+
+# --- DASS-42 Clinical Scoring ---
+def calculate_dass_clinical_score(answers: list) -> dict:
+    dep_idx = [2, 4, 9, 12, 15, 16, 20, 23, 25, 30, 33, 36, 37, 41]
+    anx_idx = [1, 3, 6, 8, 14, 18, 19, 22, 24, 27, 29, 35, 39, 40]
+    str_idx = [0, 5, 7, 10, 11, 13, 17, 21, 26, 28, 31, 32, 34, 38]
+
+    dep_score = sum(answers[i] for i in dep_idx)
+    anx_score = sum(answers[i] for i in anx_idx)
+    str_score = sum(answers[i] for i in str_idx)
+
+    def get_severity(score, bounds):
+        if score <= bounds[0]: return "Normal"
+        if score <= bounds[1]: return "Mild"
+        if score <= bounds[2]: return "Moderate"
+        if score <= bounds[3]: return "Severe"
+        return "Extremely Severe"
+
+    return {
+        "depression": {"score": dep_score, "severity": get_severity(dep_score, [9, 13, 20, 27])},
+        "anxiety": {"score": anx_score, "severity": get_severity(anx_score, [7, 9, 14, 19])},
+        "stress": {"score": str_score, "severity": get_severity(str_score, [14, 18, 25, 33])}
+    }
+
+# --- API MODELS ---
+class AnalysisRequest(BaseModel):
+    user_id: str | int = Field(default=None, description="User identifier")
+    text: str = Field(..., min_length=1)
+    survey_answers: list[int] = Field(..., min_items=42, max_items=42)
+    locale: str = Field(default="en")
+    client_ts: str | None = None
+
+
+class AnalyzeRequest(BaseModel):
+    text: str = Field(..., description="The user's response in text (Arabic/English)")
+    survey_answers: list[int] = Field(..., min_items=42, max_items=42, description="List of 42 integers (0-4) representing DASS-42 survey answers")
+    user_id: int | None = Field(default=None, description="Optional user ID to link analysis to a user")
+    locale: str = Field(default="en")
+    client_ts: str | None = None
+    app_version: str | None = None
+    model_version: str | None = None
+
+
+class ChatRequest(BaseModel):
+    message: str
+    session_id: Optional[str] = "default"
+
+
+class ChatResponse(BaseModel):
+    reply: str
+
+
+class SignupRequest(BaseModel):
+    name: str = Field(..., min_length=1)
+    email: str = Field(..., min_length=5)
+    password: str = Field(..., min_length=4)
+
+
+class LoginRequest(BaseModel):
+    email: str = Field(..., min_length=5)
+    password: str = Field(..., min_length=1)
+
+
+class CheckinRequest(BaseModel):
+    mood: int = Field(..., ge=0, le=10)
+    sleep: int = Field(..., ge=0, le=10)
+    energy: float = Field(..., ge=0, le=10)
+    user_id: int | None = Field(default=None, description="Optional user ID to link check-in to a user")
+    client_ts: str | None = None
+
+
+class JournalEntryRequest(BaseModel):
+    content: str = Field(..., min_length=1)
+    user_id: int | None = Field(default=None, description="Optional user ID to link journal entry to a user")
+    client_ts: str | None = None
+
+
+class JournalEntryUpdateRequest(BaseModel):
+    content: str = Field(..., min_length=1)
+
+
+# --- ENDPOINTS ---
+@app.get("/")
+def root():
+    return {"status": "ok", "message": "SafeSpace API"}
+
+
+@app.get("/test", response_class=HTMLResponse)
+def test_page():
+    html_path = os.path.join(os.path.dirname(__file__), "index.html")
+    if not os.path.exists(html_path):
+        raise HTTPException(status_code=404, detail="index.html not found")
+    with open(html_path, "r", encoding="utf-8") as f:
+        return f.read()
+
+
+# --- AUTH ENDPOINTS ---
+@app.post("/api/v1/auth/signup")
+async def signup(request: SignupRequest, db: Session = Depends(get_db)):
+    if not db:
+        raise HTTPException(status_code=500, detail="Database not available")
+
+    # Check if email already exists
+    existing = db.query(DBUser).filter(DBUser.email == request.email).first()
+    if existing:
+        raise HTTPException(status_code=400, detail="Email already registered")
+
+    # Create new user
+    try:
+        new_user = DBUser(
+            name=request.name,
+            email=request.email,
+            password=hash_password(request.password),
+        )
+        db.add(new_user)
+        db.commit()
+        db.refresh(new_user)
+
+        return {
+            "user_id": new_user.id,
+            "email": new_user.email,
+            "name": new_user.name,
+            "message": "Account created successfully"
+        }
+    except Exception as e:
+        db.rollback()
+        raise HTTPException(status_code=500, detail=f"Failed to create account: {str(e)}")
+
+
+@app.post("/api/v1/auth/login")
+async def login(request: LoginRequest, db: Session = Depends(get_db)):
+    if not db:
+        raise HTTPException(status_code=500, detail="Database not available")
+
+    user = db.query(DBUser).filter(DBUser.email == request.email).first()
+    if not user:
+        raise HTTPException(status_code=401, detail="Email not found")
+
+    if user.password != hash_password(request.password):
+        # Also try plain-text match for legacy users who signed up before hashing
+        if user.password != request.password:
+            raise HTTPException(status_code=401, detail="Incorrect password")
+
+    return {
+        "user_id": user.id,
+        "email": user.email,
+        "name": user.name or "",
+        "message": "Login successful"
+    }
+
+
+# New-style endpoint (used by index.html test page)
+@app.post("/v1/analysis")
+def analyze(payload: AnalysisRequest, db: Session = Depends(get_db)):
+    # Shift 0-3 UI scale to 1-4 for the AI model (trained on data.csv)
+    shifted_answers = [a + 1 for a in payload.survey_answers]
+    text_scores = predict_text(payload.text)
+    survey_scores = predict_survey(shifted_answers)
+    
+    final_scores = fuse_scores(text_scores, survey_scores)
+    primary = max(final_scores, key=final_scores.get)
+    clinical = calculate_dass_clinical_score(payload.survey_answers)
+    rec = get_recommendations(primary, final_scores[primary], payload.text)
+    created_at_dt = datetime.utcnow()
+    if payload.client_ts:
+        try:
+            created_at_dt = datetime.fromisoformat(payload.client_ts.replace("Z", "+00:00")).replace(tzinfo=None)
+        except ValueError:
+            pass
+    created_at = created_at_dt.isoformat() + "Z"
+
+    # Save to PostgreSQL if DB is connected
+    if db:
+        try:
+            text_input_hash = hashlib.sha256(payload.text.encode()).hexdigest()
+            new_analysis = DBAnalysis(
+                user_id=payload.user_id,
+                primary_condition=primary,
+                clinical_scoring=clinical,
+                created_at=created_at_dt,
+                text_input=payload.text,
+                text_input_hash=text_input_hash,
+                text_scores=text_scores,
+                survey_scores=survey_scores,
+                fused_scores=final_scores,
+                severity=rec.get("severity"),
+                cause=rec.get("cause"),
+                suicidal_flag=rec.get("suicidal_flag", False),
+                model_version=None,
+                app_version=None,
+                locale=payload.locale,
+            )
+            db.add(new_analysis)
+            db.commit()
+        except Exception as e:
+            logger.exception("DB save error: %s", e)
+
+    return {
+        "analysis_id": None,
+        "primary_condition": primary,
+        "fused_scores": final_scores,
+        "text_scores": text_scores,
+        "survey_scores": survey_scores,
+        "clinical_scoring": clinical,
+        "severity": rec.get("severity"),
+        "cause": rec.get("cause"),
+        "recommendations": {
+            "tips_en": rec.get("tips_en", []),
+            "tips_ar": rec.get("tips_ar", []),
+            "resources_en": rec.get("resources_en", []),
+            "resources_ar": rec.get("resources_ar", []),
+            "referral_en": rec.get("referral_en", ""),
+            "referral_ar": rec.get("referral_ar", ""),
+        },
+        "suicidal_flag": rec.get("suicidal_flag", False),
+        "created_at": created_at,
+    }
+
+# Flutter-compatible endpoint (used by api_service.dart)
+@app.post("/api/v1/analyze")
+async def analyze_mental_health(request: AnalyzeRequest, db: Session = Depends(get_db)):
+    try:
+        start_time = time.time()
+        # Shift 0-3 UI scale to 1-4 for the AI model (trained on data.csv)
+        shifted_answers = [a + 1 for a in request.survey_answers]
+        text_scores = predict_text(request.text)
+        survey_scores = predict_survey(shifted_answers)
+        
+        final_scores = fuse_scores(text_scores, survey_scores)
+        primary = max(final_scores, key=final_scores.get)
+        clinical = calculate_dass_clinical_score(request.survey_answers)
+        rec = get_recommendations(primary, final_scores[primary], request.text)
+        created_at_dt = datetime.utcnow()
+        if request.client_ts:
+            try:
+                created_at_dt = datetime.fromisoformat(request.client_ts.replace("Z", "+00:00")).replace(tzinfo=None)
+            except ValueError:
+                pass
+        created_at = created_at_dt.isoformat() + "Z"
+
+        # Save to PostgreSQL if DB is connected
+        if db:
+            try:
+                text_input_hash = hashlib.sha256(request.text.encode()).hexdigest()
+                new_analysis = DBAnalysis(
+                    user_id=request.user_id,
+                    primary_condition=primary,
+                    clinical_scoring=clinical,
+                    created_at=created_at_dt,
+                    text_input=request.text,
+                    text_input_hash=text_input_hash,
+                    text_scores=text_scores,
+                    survey_scores=survey_scores,
+                    fused_scores=final_scores,
+                    severity=rec.get("severity"),
+                    cause=rec.get("cause"),
+                    suicidal_flag=rec.get("suicidal_flag", False),
+                    model_version=request.model_version,
+                    app_version=request.app_version,
+                    locale=request.locale,
+                )
+                db.add(new_analysis)
+                db.commit()
+            except Exception as e:
+                logger.exception("DB save error: %s", e)
+
+        return {
+            "analysis_id": None,
+            "primary_condition": primary,
+            "fused_scores": final_scores,
+            "text_scores": text_scores,
+            "survey_scores": survey_scores,
+            "clinical_scoring": clinical,
+            "severity": rec.get("severity"),
+            "cause": rec.get("cause"),
+            "recommendations": {
+                "tips_en": rec.get("tips_en", []),
+                "tips_ar": rec.get("tips_ar", []),
+                "resources_en": rec.get("resources_en", []),
+                "resources_ar": rec.get("resources_ar", []),
+                "referral_en": rec.get("referral_en", ""),
+                "referral_ar": rec.get("referral_ar", ""),
+            },
+            "suicidal_flag": rec.get("suicidal_flag", False),
+            "created_at": created_at,
+            "duration_ms": int((time.time() - start_time) * 1000),
+        }
+    except Exception as e:
+        logger.exception("Analyze request failed: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to analyze")
+
+# Flutter-compatible history endpoint
+@app.get("/api/v1/analyses/history")
+async def get_analyses_history(user_id: int = None, db: Session = Depends(get_db)):
+    try:
+        if not db:
+            return []
+
+        query = db.query(DBAnalysis)
+
+        # Filter by user_id if provided
+        if user_id is not None:
+            query = query.filter(DBAnalysis.user_id == user_id)
+
+        # Get the 10 most recent analyses, sorted by created_at ascending (oldest first for graphing)
+        records = query.order_by(DBAnalysis.created_at.desc()).limit(10).all()
+
+        history = []
+        for r in reversed(records):  # Reverse so oldest is first
+            if r.clinical_scoring:
+                history.append({
+                    "id": r.id,
+                    "date": r.created_at.strftime("%b %d"),
+                    "depression": r.clinical_scoring.get("depression", {}).get("score", 0),
+                    "anxiety": r.clinical_scoring.get("anxiety", {}).get("score", 0),
+                    "stress": r.clinical_scoring.get("stress", {}).get("score", 0),
+                    "primary": r.primary_condition
+                })
+        return history
+    except Exception as e:
+        logger.exception("Analyze request failed: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to analyze")
+
+@app.post("/api/v1/chat", response_model=ChatResponse)
+async def chat_with_ai(request: ChatRequest):
+    api_url = os.environ.get("AI_API_URL")
+    api_key = os.environ.get("AI_API_KEY")
+    chatflow_id = os.environ.get("AI_CHATFLOW_ID")
+
+    if not api_url or not api_key or not chatflow_id:
+        raise HTTPException(status_code=500, detail="AI API credentials are not configured in Secrets.")
+
+    endpoint = f"{api_url}/api/v1/prediction/{chatflow_id}"
+    headers = {"Authorization": f"Bearer {api_key}"}
+    payload = {"question": request.message, "overrideConfig": {"sessionId": request.session_id}}
+
+    async with httpx.AsyncClient() as client:
+        try:
+            response = await client.post(endpoint, json=payload, headers=headers, timeout=30.0)
+            response.raise_for_status()
+            data = response.json()
+            return ChatResponse(reply=data.get("text") or data.get("answer") or str(data))
+        except Exception as e:
+            raise HTTPException(status_code=502, detail=f"Failed to communicate with AI API: {str(e)}")
+
+
+@app.post("/api/v1/checkin")
+async def create_checkin(request: CheckinRequest, db: Session = Depends(get_db)):
+    if not db:
+        raise HTTPException(status_code=500, detail="Database not available")
+
+    if request.user_id is None:
+        raise HTTPException(status_code=400, detail="user_id is required")
+
+    created_at = datetime.utcnow()
+    if request.client_ts:
+        try:
+            created_at = datetime.fromisoformat(request.client_ts.replace("Z", "+00:00")).replace(tzinfo=None)
+        except ValueError:
+            pass
+
+    try:
+        new_checkin = DBCheckin(
+            user_id=request.user_id,
+            mood=request.mood,
+            sleep=request.sleep,
+            energy=request.energy,
+            created_at=created_at,
+        )
+        db.add(new_checkin)
+        db.commit()
+        db.refresh(new_checkin)
+
+        return {
+            "id": new_checkin.id,
+            "user_id": new_checkin.user_id,
+            "mood": new_checkin.mood,
+            "sleep": new_checkin.sleep,
+            "energy": new_checkin.energy,
+            "created_at": new_checkin.created_at.isoformat() + "Z",
+        }
+    except Exception as e:
+        db.rollback()
+        logger.exception("Failed to save check-in: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to save check-in")
+
+
+@app.get("/api/v1/checkin/history")
+async def get_checkin_history(user_id: int | None = None, db: Session = Depends(get_db)):
+    try:
+        if not db:
+            return []
+
+        query = db.query(DBCheckin)
+        if user_id is not None:
+            query = query.filter(DBCheckin.user_id == user_id)
+
+        records = query.order_by(DBCheckin.created_at.desc()).limit(30).all()
+        return [
+            {
+                "id": r.id,
+                "user_id": r.user_id,
+                "mood": r.mood,
+                "sleep": r.sleep,
+                "energy": r.energy,
+                "created_at": r.created_at.isoformat() + "Z",
+            }
+            for r in records
+        ]
+    except Exception as e:
+        logger.exception("Failed to fetch check-in history: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to fetch history")
+
+
+@app.post("/api/v1/journal")
+async def create_journal_entry(request: JournalEntryRequest, db: Session = Depends(get_db)):
+    if not db:
+        raise HTTPException(status_code=500, detail="Database not available")
+
+    if request.user_id is None:
+        raise HTTPException(status_code=400, detail="user_id is required")
+
+    created_at = datetime.utcnow()
+    if request.client_ts:
+        try:
+            created_at = datetime.fromisoformat(request.client_ts.replace("Z", "+00:00")).replace(tzinfo=None)
+        except ValueError:
+            pass
+
+    try:
+        entry = DBJournalEntry(
+            user_id=request.user_id,
+            content=request.content,
+            created_at=created_at,
+        )
+        db.add(entry)
+        db.commit()
+        db.refresh(entry)
+
+        return {
+            "id": entry.id,
+            "user_id": entry.user_id,
+            "content": entry.content,
+            "created_at": entry.created_at.isoformat() + "Z",
+        }
+    except Exception as e:
+        db.rollback()
+        logger.exception("Failed to save journal entry: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to save journal entry")
+
+
+@app.get("/api/v1/journal/history")
+async def get_journal_history(user_id: int | None = None, db: Session = Depends(get_db)):
+    try:
+        if not db:
+            return []
+
+        query = db.query(DBJournalEntry)
+        if user_id is not None:
+            query = query.filter(DBJournalEntry.user_id == user_id)
+
+        records = query.order_by(DBJournalEntry.created_at.desc()).limit(50).all()
+        return [
+            {
+                "id": r.id,
+                "user_id": r.user_id,
+                "content": r.content,
+                "created_at": r.created_at.isoformat() + "Z",
+            }
+            for r in records
+        ]
+    except Exception as e:
+        logger.exception("Failed to fetch journal history: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to fetch journal history")
+
+
+@app.put("/api/v1/journal/{entry_id}")
+async def update_journal_entry(entry_id: int, request: JournalEntryUpdateRequest, db: Session = Depends(get_db)):
+    if not db:
+        raise HTTPException(status_code=500, detail="Database not available")
+
+    try:
+        entry = db.query(DBJournalEntry).filter(DBJournalEntry.id == entry_id).first()
+        if not entry:
+            raise HTTPException(status_code=404, detail="Journal entry not found")
+
+        entry.content = request.content
+        entry.updated_at = datetime.utcnow()
+        db.add(entry)
+        db.commit()
+        db.refresh(entry)
+
+        return {
+            "id": entry.id,
+            "user_id": entry.user_id,
+            "content": entry.content,
+            "created_at": entry.created_at.isoformat() + "Z",
+            "updated_at": entry.updated_at.isoformat() + "Z",
+        }
+    except HTTPException:
+        raise
+    except Exception as e:
+        db.rollback()
+        logger.exception("Failed to update journal entry: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to update journal entry")
+
+
+@app.delete("/api/v1/journal/{entry_id}")
+async def delete_journal_entry(entry_id: int, db: Session = Depends(get_db)):
+    if not db:
+        raise HTTPException(status_code=500, detail="Database not available")
+
+    try:
+        entry = db.query(DBJournalEntry).filter(DBJournalEntry.id == entry_id).first()
+        if not entry:
+            raise HTTPException(status_code=404, detail="Journal entry not found")
+
+        db.delete(entry)
+        db.commit()
+        return {"status": "deleted", "id": entry_id}
+    except HTTPException:
+        raise
+    except Exception as e:
+        db.rollback()
+        logger.exception("Failed to delete journal entry: %s", e)
+        raise HTTPException(status_code=500, detail="Failed to delete journal entry")

app.py

@@ -0,0 +1,508 @@
+"""
+app.py
+======
+Mental Health AI — Full Pipeline
+Files needed:
+  mental_xlmr_final/   ← XLM-R model folder
+  mental_model.h5      ← Survey Keras model
+  scaler.pkl           ← Survey scaler
+  recommendations.py   ← same directory
+
+Install:
+  pip install streamlit transformers torch tensorflow scikit-learn deep-translator
+"""
+import sys, os
+sys.path.append(os.path.dirname(__file__))
+import re, pickle, warnings
+import numpy as np
+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from deep_translator import GoogleTranslator
+sys.path.insert(0, os.path.dirname(__file__))
+from recommendations import get_recommendations
+
+warnings.filterwarnings("ignore")
+
+# ── PAGE CONFIG ───────────────────────────────────────────────────────────────
+st.set_page_config(page_title="Mental Health AI", page_icon="🧠", layout="wide")
+
+st.markdown("""
+<style>
+.stApp { background: linear-gradient(135deg, #0d1117, #161b22, #0d1117); color: #e6edf3; }
+h1 { text-align: center; color: #58a6ff; font-size: 36px; margin-bottom: 4px; }
+h2, h3 { color: #c9d1d9; }
+.section-card {
+    background: rgba(22,27,34,0.9);
+    border: 1px solid #30363d;
+    border-radius: 14px;
+    padding: 22px 26px;
+    margin-bottom: 18px;
+}
+.result-card {
+    background: #161b22;
+    border: 1px solid #30363d;
+    border-radius: 12px;
+    padding: 20px;
+    text-align: center;
+    margin-bottom: 8px;
+}
+.result-card.primary { border: 2px solid #58a6ff; }
+.result-label { font-size: 15px; color: #8b949e; margin-bottom: 6px; }
+.result-value { font-size: 44px; font-weight: 700; }
+.severity-badge {
+    display: inline-block;
+    padding: 3px 12px;
+    border-radius: 20px;
+    font-size: 12px;
+    font-weight: 600;
+    margin-top: 6px;
+}
+.rec-block {
+    background: #161b22;
+    border: 1px solid #30363d;
+    border-radius: 12px;
+    padding: 18px 22px;
+    margin-bottom: 14px;
+}
+.rec-title { font-size: 15px; font-weight: 700; margin-bottom: 10px; }
+.rec-item { font-size: 14px; color: #c9d1d9; padding: 4px 0; border-bottom: 1px solid #21262d; }
+.rec-item:last-child { border-bottom: none; }
+.ar-text { font-size: 13px; color: #8b949e; margin-top: 3px; direction: rtl; }
+.referral-box {
+    background: rgba(248,81,73,0.1);
+    border: 1px solid rgba(248,81,73,0.4);
+    border-radius: 10px;
+    padding: 14px 18px;
+    margin-top: 12px;
+}
+.crisis-box {
+    background: rgba(248,81,73,0.2);
+    border: 2px solid #f85149;
+    border-radius: 12px;
+    padding: 20px 24px;
+    margin: 16px 0;
+}
+div.stButton > button {
+    background: linear-gradient(90deg, #1f6feb, #58a6ff);
+    color: white; font-size: 17px; font-weight: 700;
+    border-radius: 10px; height: 52px; width: 100%; border: none;
+}
+div.stSlider > label { color: #c9d1d9 !important; font-size: 13px; }
+.stTextArea textarea {
+    background: #0d1117 !important;
+    color: #e6edf3 !important;
+    border: 1px solid #30363d !important;
+    border-radius: 8px !important;
+}
+</style>
+""", unsafe_allow_html=True)
+
+# ── CONSTANTS ─────────────────────────────────────────────────────────────────
+CLASSES = ["anxiety", "depression", "stress"]
+ARABIC_LABELS = {"anxiety": "القلق", "depression": "الاكتئاب", "stress": "الضغط النفسي"}
+COLORS = {"anxiety": "#ffa657", "depression": "#79c0ff", "stress": "#56d364"}
+SEVERITY_AR = {
+    "normal": "طبيعي", "mild": "خفيف", "moderate": "متوسط",
+    "severe": "شديد", "extremely_severe": "شديد جداً", "crisis": "أزمة",
+}
+SEVERITY_COLORS = {
+    "normal": "#56d364", "mild": "#e3b341", "moderate": "#ffa657",
+    "severe": "#f85149", "extremely_severe": "#ff0000", "crisis": "#ff0000",
+}
+
+CAUSE_AR = {
+    "work": "ضغط العمل", "relationships": "العلاقات", "financial": "الضغط المالي",
+    "academic": "الضغط الأكاديمي", "health": "المخاوف الصحية", "social": "القلق الاجتماعي",
+    "self_worth": "الثقة بالنفس", "trauma": "الصدمة النفسية", "general": "عام",
+}
+
+# ── LOAD MODELS ───────────────────────────────────────────────────────────────
+@st.cache_resource
+def load_xlmr():
+    token = st.secrets["HF_TOKEN"]
+    xlmr_tokenizer = AutoTokenizer.from_pretrained(
+        "tasneem33355/mental-xlmr", token=token
+    )
+    model = AutoModelForSequenceClassification.from_pretrained(
+        "tasneem33355/mental-xlmr", token=token
+    )
+    model.eval()
+    # Hardcoded — LabelEncoder على ['anxiety','depression','stress'] دايماً بترتّب alphabetically
+    # 0=anxiety, 1=depression, 2=stress
+    classes = ["anxiety", "depression", "stress"]
+    return xlmr_tokenizer, model, classes
+
+@st.cache_resource
+def load_survey():
+    scaler  = pickle.load(open(os.path.join(os.path.dirname(__file__), "scaler.pkl"), "rb"))
+    weights = pickle.load(open(os.path.join(os.path.dirname(__file__), "model_weights.pkl"), "rb"))
+
+    def predict(x):
+        for w in weights:
+            if len(w) == 2:
+                x = np.dot(x, w[0]) + w[1]
+                x = np.maximum(0, x)  # ReLU
+        x = np.exp(x) / np.sum(np.exp(x))  # Softmax
+        return x
+
+    return scaler, predict
+
+xlmr_tokenizer, xlmr_model, le = load_xlmr()
+scaler, survey_predict = load_survey()
+
+# ── HELPERS ───────────────────────────────────────────────────────────────────
+def clean_text(text):
+    text = re.sub(r'(.)\1{2,}', r'\1\1', text)
+    text = re.sub(r'[^\w\s\u0600-\u06FF\[\]]', ' ', text)
+    return re.sub(r'\s+', ' ', text).strip()
+
+def translate_to_en(text):
+    try:
+        return GoogleTranslator(source="auto", target="en").translate(text)
+    except Exception:
+        return ""
+
+# ── KEYWORD OVERRIDE ─────────────────────────────────────────────────────────
+DEPRESSION_KEYWORDS = [
+    # عربي فصيح
+    "اكتئاب", "مكتئب", "مكتئبة", "حزن", "حزين", "حزينة", "يأس", "يائس", "يائسة",
+    "فراغ", "إحساس بالفراغ", "بلا معنى", "لا معنى", "مالهاش معنى", "بلا هدف",
+    "لا أمل", "مفيش أمل", "تعبت من الحياة", "زهقت من الحياة",
+    "مش لاقي معنى", "مش لاقية معنى", "حاسس بالفراغ", "حاسة بالفراغ",
+    "مفيش طاقة", "مفيش رغبة", "بكاء", "عايز أبكي", "عايزة أبكي",
+    "وحيد", "وحيدة", "عزلة", "منعزل", "منعزلة",
+    "إرهاق نفسي", "إرهاق عاطفي", "مش حاسس بحاجة", "مش حاسة بحاجة",
+    # عامية مصرية وشامية
+    "زهقت", "تعبت", "مش طايق", "مش طايقة", "نفسيتي وحشة", "نفسيتي في الأرض",
+    "مش قادر أكمل", "مش قادرة أكمل", "مش عايش", "مش قادر أعيش",
+    "مش عايز أصحى", "مش عايزة أصحى", "دموع", "بدمع", "قلبي تقيل",
+    "مش حاسس بنفسي", "مش حاسة بنفسي", "ما بحس بشي", "ما في فايدة",
+    "مافي امل", "ما في امل", "حياتي خربت", "خسرت كل حاجة",
+    # إنجليزي
+    "depressed", "depression", "hopeless", "hopelessness", "empty", "emptiness",
+    "worthless", "meaningless", "no meaning", "no purpose", "cannot go on",
+    "cant go on", "no energy", "no motivation", "crying", "feel nothing",
+    "numb", "isolated", "lonely", "loneliness", "sad", "sadness",
+    "despair", "grief", "miserable", "broken", "lost all hope",
+]
+
+ANXIETY_KEYWORDS = [
+    # عربي
+    "قلق", "قلقان", "قلقانة", "خوف", "خايف", "خايفة", "توتر", "متوتر", "متوترة",
+    "هلع", "مش مرتاح", "مش مرتاحة", "ذعر", "رهاب", "وسواس",
+    # إنجليزي
+    "panic", "anxious", "anxiety", "worried", "worry", "fear",
+    "scared", "nervous", "restless", "tense", "phobia", "ocd",
+]
+
+STRESS_KEYWORDS = [
+    # عربي
+    "ضغط", "ضغوط", "مضغوط", "مضغوطة", "إجهاد", "مجهد", "مجهدة",
+    # إنجليزي
+    "overwhelmed", "stressed", "stress", "burnout", "exhausted", "overloaded",
+]
+
+def keyword_boost(text: str, scores: dict) -> dict:
+    """
+    يعوّض الـ stress bias في الموديل عن طريق override قوي
+    لما تكون كلمات depression أو anxiety أو stress واضحة في النص.
+    """
+    text_lower = text.lower()
+
+    dep_hits = sum(1 for kw in DEPRESSION_KEYWORDS if kw.lower() in text_lower)
+    anx_hits = sum(1 for kw in ANXIETY_KEYWORDS   if kw.lower() in text_lower)
+    str_hits = sum(1 for kw in STRESS_KEYWORDS     if kw.lower() in text_lower)
+
+    if dep_hits == 0 and anx_hits == 0 and str_hits == 0:
+        return scores
+
+    s = dict(scores)
+
+    if dep_hits > 0 and dep_hits >= anx_hits and dep_hits >= str_hits:
+        # depression كلمات واضحة — override قوي
+        boost = min(0.55 + dep_hits * 0.10, 0.85)
+        s["depression"] = boost
+        remaining = 1.0 - boost
+        total_rest = s["anxiety"] + s["stress"]
+        if total_rest > 0:
+            s["anxiety"] = round(remaining * s["anxiety"] / total_rest, 4)
+            s["stress"]  = round(remaining * s["stress"]  / total_rest, 4)
+        s["depression"] = round(boost, 4)
+
+    elif anx_hits > 0 and anx_hits >= dep_hits and anx_hits >= str_hits:
+        # anxiety كلمات واضحة — override قوي
+        boost = min(0.55 + anx_hits * 0.10, 0.85)
+        s["anxiety"] = boost
+        remaining = 1.0 - boost
+        total_rest = s["depression"] + s["stress"]
+        if total_rest > 0:
+            s["depression"] = round(remaining * s["depression"] / total_rest, 4)
+            s["stress"]     = round(remaining * s["stress"]     / total_rest, 4)
+        s["anxiety"] = round(boost, 4)
+
+    elif str_hits > 0 and str_hits >= dep_hits and str_hits >= anx_hits:
+        # stress كلمات واضحة — override قوي
+        boost = min(0.55 + str_hits * 0.10, 0.85)
+        s["stress"] = boost
+        remaining = 1.0 - boost
+        total_rest = s["depression"] + s["anxiety"]
+        if total_rest > 0:
+            s["depression"] = round(remaining * s["depression"] / total_rest, 4)
+            s["anxiety"]    = round(remaining * s["anxiety"]    / total_rest, 4)
+        s["stress"] = round(boost, 4)
+
+    # normalize
+    total = sum(s.values())
+    if total > 0:
+        s = {k: round(v / total, 4) for k, v in s.items()}
+
+    return s
+
+
+def predict_text(text: str) -> dict:
+    cleaned  = clean_text(text)
+    text_en  = translate_to_en(cleaned)
+    combined = (text_en + " [SEP] " + cleaned) if text_en else cleaned
+    inputs   = xlmr_tokenizer(combined, return_tensors="pt",
+                               truncation=True, max_length=192, padding=True)
+    with torch.no_grad():
+        probs = torch.softmax(xlmr_model(**inputs).logits, dim=-1).squeeze().numpy()
+    raw_scores = {c: round(float(p), 4) for c, p in zip(le, probs)}
+    # طبّق الـ keyword boost على النص الأصلي + الترجمة
+    boosted = keyword_boost(text + " " + text_en, raw_scores)
+    return boosted
+
+def predict_survey(answers: list) -> dict:
+    data = scaler.transform(np.array(answers).reshape(1, -1))
+    pred = survey_predict(data)[0]
+    return {
+        "depression": round(float(pred[0]), 4),
+        "anxiety":    round(float(pred[1]), 4),
+        "stress":     round(float(pred[2]), 4),
+    }
+
+def fuse_scores(text_s, survey_s, w_text=0.4, w_survey=0.6):
+    return {c: round(w_text * text_s[c] + w_survey * survey_s[c], 4) for c in CLASSES}
+
+# ── SURVEY QUESTIONS ─────────────────────────────────────────────────────────
+SURVEY_Q = [
+    ("I found it hard to wind down", "وجدت صعوبة في الاسترخاء"),
+    ("I was aware of dryness of my mouth", "لاحظت جفافاً في فمي"),
+    ("I couldn't seem to experience any positive feeling at all", "لم أستطع الشعور بأي مشاعر إيجابية"),
+    ("I experienced breathing difficulty", "أحسست بصعوبة في التنفس"),
+    ("I found it difficult to work up the initiative to do things", "وجدت صعوبة في اتخاذ المبادرة للقيام بالأشياء"),
+    ("I tended to over-react to situations", "كنت أبالغ في ردود أفعالي تجاه المواقف"),
+    ("I experienced trembling", "شعرت بالرعشة"),
+    ("I felt that I was using a lot of nervous energy", "شعرت أنني أستهلك الكثير من الطاقة العصبية"),
+    ("I was worried about situations in which I might panic", "كنت قلقاً من مواقف قد أصاب فيها بالذعر"),
+    ("I felt that I had nothing to look forward to", "شعرت أنه لا يوجد شيء أتطلع إليه"),
+    ("I found myself getting agitated", "وجدت نفسي أشعر بالانفعال"),
+    ("I found it difficult to relax", "وجدت صعوبة في الاسترخاء"),
+    ("I felt down-hearted and blue", "شعرت بالإحباط والكآبة"),
+    ("I was intolerant of anything that kept me from getting on", "كنت غير متسامح مع أي شيء يعيقني"),
+    ("I felt I was close to panic", "شعرت أنني على وشك الذعر"),
+    ("I was unable to become enthusiastic", "لم أستطع أن أتحمس لأي شيء"),
+    ("I felt I wasn't worth much as a person", "شعرت أنني لست شخصاً ذا قيمة"),
+    ("I felt that I was rather touchy", "شعرت أنني متقلب المزاج"),
+    ("I was aware of the action of my heart", "كنت واعياً لنبضات قلبي"),
+    ("I felt scared without any good reason", "شعرت بالخوف دون سبب واضح"),
+    ("I felt that life was meaningless", "شعرت أن الحياة بلا معنى"),
+    ("I found it hard to calm down", "وجدت صعوبة في التهدئة"),
+    ("I felt nervous", "شعرت بالتوتر"),
+    ("I felt sad and depressed", "شعرت بالحزن والاكتئاب"),
+    ("I found myself getting impatient", "وجدت نفسي أشعر بنفاد الصبر"),
+    ("I felt that I was rather emotional", "شعرت أنني عاطفي بشكل مفرط"),
+    ("I felt restless", "شعرت بعدم الهدوء"),
+    ("I had difficulty concentrating", "وجدت صعوبة في التركيز"),
+    ("I felt lonely", "شعرت بالوحدة"),
+    ("I found it difficult to relax", "وجدت صعوبة في الاسترخاء"),
+    ("I felt hopeless", "شعرت باليأس"),
+    ("I felt worried about many things", "كنت قلقاً بشأن أشياء كثيرة"),
+    ("I felt that I had no energy", "شعرت بعدم وجود طاقة"),
+    ("I felt tense", "شعرت بالتوتر والضيق"),
+    ("I felt tired for no reason", "شعرت بالتعب دون سبب"),
+    ("I felt uneasy", "شعرت بعدم الارتياح"),
+    ("I felt worthless", "شعرت بأنني لا قيمة لي"),
+    ("I felt anxious", "شعرت بالقلق"),
+    ("I felt discouraged", "شعرت بالإحباط"),
+    ("I felt stressed", "شعرت بالضغط"),
+    ("I felt overwhelmed", "شعرت بالإرهاق"),
+    ("I felt emotionally exhausted", "شعرت بالإنهاك العاطفي"),
+]
+
+# ── UI ────────────────────────────────────────────────────────────────────────
+st.title("🧠 Mental Health AI")
+st.markdown(
+    "<p style='text-align:center;color:#8b949e;font-size:15px;'>"
+    "Write how you feel and answer the survey for a complete assessment"
+    "<br><span dir='rtl'>اكتب ما تشعر به وأجب على الأسئلة للحصول على تقييم شامل</span></p>",
+    unsafe_allow_html=True,
+)
+st.markdown("---")
+
+# ── PART 1: TEXT ─────────────────────────────────────────────────────────────
+st.markdown("<div class='section-card'>", unsafe_allow_html=True)
+st.markdown("### 💬 How are you feeling? / كيف تشعر؟")
+st.markdown(
+    "<p style='color:#8b949e;font-size:13px;'>"
+    "Write in any language — Arabic (any dialect), English, or both<br>"
+    "<span dir='rtl'>اكتب بأي لغة — عربي (أي لهجة)، إنجليزي، أو الاتنين</span></p>",
+    unsafe_allow_html=True,
+)
+user_text = st.text_area(
+    label="",
+    placeholder="e.g. I've been feeling very overwhelmed at work and can't sleep...\nمثال: أنا تعبان جداً من الشغل ومش قادر أنام...",
+    height=120,
+    label_visibility="collapsed",
+)
+st.markdown("</div>", unsafe_allow_html=True)
+
+# ── PART 2: SURVEY ────────────────────────────────────────────────────────────
+st.markdown("<div class='section-card'>", unsafe_allow_html=True)
+st.markdown("### 📋 DASS-42 Survey / استبيان DASS-42")
+st.markdown(
+    "<p style='color:#8b949e;font-size:13px;'>"
+    "0 = Never &nbsp;|&nbsp; 1 = Sometimes &nbsp;|&nbsp; 2 = Often &nbsp;|&nbsp; "
+    "3 = Most of the time &nbsp;|&nbsp; 4 = Always<br>"
+    "<span dir='rtl'>0 = لم يحدث أبداً | 1 = أحياناً | 2 = كثيراً | 3 = معظم الوقت | 4 = دائماً</span></p>",
+    unsafe_allow_html=True,
+)
+
+survey_answers = []
+for i in range(0, len(SURVEY_Q), 2):
+    cols = st.columns(2)
+    for j, (en, ar) in enumerate(SURVEY_Q[i:i+2]):
+        with cols[j]:
+            val = st.slider(
+                f"{i+j+1}. {en}\n{ar}",
+                min_value=0, max_value=3, value=0,
+                key=f"q_{i+j}",
+            )
+            survey_answers.append(val)
+
+st.markdown("</div>", unsafe_allow_html=True)
+
+# ── PREDICT BUTTON ────────────────────────────────────────────────────────────
+_, col_btn, _ = st.columns([1, 2, 1])
+with col_btn:
+    predict_btn = st.button("🔍 Analyze / تحليل")
+
+# ── RESULTS ──────────────────────────────────────────────────────────────────
+if predict_btn:
+    if not user_text.strip():
+        st.warning("Please write how you feel first. / من فضلك اكتب ما تشعر به أولاً.")
+        st.stop()
+
+    with st.spinner("Analyzing... / جاري التحليل..."):
+        text_scores   = predict_text(user_text)
+        survey_scores = predict_survey(survey_answers)
+        final_scores  = fuse_scores(text_scores, survey_scores)
+        primary       = max(final_scores, key=final_scores.get)
+        rec           = get_recommendations(primary, final_scores[primary], user_text)
+
+    st.markdown("---")
+    st.markdown("## 📊 Results / النتائج")
+
+    # ── SCORE CARDS ───────────────────────────────────────────────────────────
+    cols = st.columns(3)
+    for col, cls in zip(cols, CLASSES):
+        pct        = int(final_scores[cls] * 100)
+        is_primary = cls == primary
+        card_class = "result-card primary" if is_primary else "result-card"
+        sev        = rec["severity"] if is_primary else ""
+        badge      = ""
+        if is_primary and sev:
+            sev_color = SEVERITY_COLORS.get(sev, "#8b949e")
+            badge = (f"<div class='severity-badge' style='background:{sev_color}20;"
+                     f"color:{sev_color};border:1px solid {sev_color};'>"
+                     f"{sev.replace('_',' ').title()} / {SEVERITY_AR.get(sev,'')}</div>")
+        col.markdown(f"""
+        <div class='{card_class}'>
+            <div class='result-label'>{cls.title()} / {ARABIC_LABELS[cls]}</div>
+            <div class='result-value' style='color:{COLORS[cls]}'>{pct}%</div>
+            {badge}
+        </div>""", unsafe_allow_html=True)
+
+    # ── PRIMARY LABEL ─────────────────────────────────────────────────────────
+    if not rec["suicidal_flag"]:
+        cause_label = CAUSE_AR.get(rec["cause"], rec["cause"])
+        st.markdown(
+            f"<p style='text-align:center;margin-top:10px;font-size:17px;color:#8b949e;'>"
+            f"Primary: <strong style='color:{COLORS[primary]}'>{primary.title()} / {ARABIC_LABELS[primary]}</strong>"
+            f" &nbsp;|&nbsp; Cause detected / السبب المكتشف: "
+            f"<strong style='color:#e3b341'>{rec['cause'].replace('_',' ').title()} / {cause_label}</strong></p>",
+            unsafe_allow_html=True,
+        )
+
+    # ── CRISIS BOX ────────────────────────────────────────────────────────────
+    if rec["suicidal_flag"]:
+        st.markdown("""
+        <div class='crisis-box'>
+        <h3 style='color:#f85149;margin-top:0;'>🚨 Crisis Support Needed / مطلوب دعم أزمة</h3>
+        </div>""", unsafe_allow_html=True)
+
+    # ── SCORE DETAILS ─────────────────────────────────────────────────────────
+    with st.expander("Show score breakdown / عرض تفاصيل النتائج"):
+        c1, c2 = st.columns(2)
+        c1.markdown("**Text model / موديل النص:**")
+        for cls in CLASSES:
+            c1.markdown(f"- {cls} / {ARABIC_LABELS[cls]}: **{int(text_scores[cls]*100)}%**")
+        c2.markdown("**Survey model / موديل السيرفاي:**")
+        for cls in CLASSES:
+            c2.markdown(f"- {cls} / {ARABIC_LABELS[cls]}: **{int(survey_scores[cls]*100)}%**")
+
+    # ── RECOMMENDATIONS ───────────────────────────────────────────────────────
+    st.markdown("---")
+    st.markdown("## 💡 Recommendations / التوصيات")
+
+    col_tips, col_res = st.columns(2)
+
+    with col_tips:
+        st.markdown("<div class='rec-block'>", unsafe_allow_html=True)
+        st.markdown("<div class='rec-title'>✅ Practical Tips / نصائح عملية</div>",
+                    unsafe_allow_html=True)
+        tips_en = rec.get("tips_en", [])
+        tips_ar = rec.get("tips_ar", [])
+        for en, ar in zip(tips_en, tips_ar):
+            st.markdown(
+                f"<div class='rec-item'>{en}"
+                f"<div class='ar-text' dir='rtl'>• {ar}</div></div>",
+                unsafe_allow_html=True,
+            )
+        st.markdown("</div>", unsafe_allow_html=True)
+
+    with col_res:
+        st.markdown("<div class='rec-block'>", unsafe_allow_html=True)
+        st.markdown("<div class='rec-title'>📚 Resources / موارد مفيدة</div>",
+                    unsafe_allow_html=True)
+        res_en = rec.get("resources_en", [])
+        res_ar = rec.get("resources_ar", [])
+        for en, ar in zip(res_en, res_ar):
+            st.markdown(
+                f"<div class='rec-item'>{en}"
+                f"<div class='ar-text' dir='rtl'>• {ar}</div></div>",
+                unsafe_allow_html=True,
+            )
+        st.markdown("</div>", unsafe_allow_html=True)
+
+    # ── REFERRAL ───────────────────────────────────────────────────────────���──
+    ref_en = rec.get("referral_en", "")
+    ref_ar = rec.get("referral_ar", "")
+    if ref_en:
+        box_class = "crisis-box" if rec["suicidal_flag"] else "referral-box"
+        st.markdown(
+            f"<div class='{box_class}'>"
+            f"<strong>🏥 When to seek help / متى تطلب المساعدة:</strong><br>"
+            f"{ref_en}<br>"
+            f"<span dir='rtl' style='color:#f0a0a0;font-size:13px;'>{ref_ar}</span>"
+            f"</div>",
+            unsafe_allow_html=True,
+        )
+
+    st.markdown(
+        "<p style='text-align:center;color:#484f58;font-size:12px;margin-top:20px;'>"
+        "⚠️ This system is for awareness only and is not a substitute for professional medical diagnosis.<br>"
+        "هذا النظام للتوعية فقط وليس بديلاً عن التشخيص الطبي المتخصص.</p>",
+        unsafe_allow_html=True,
+    )

check_db.py

@@ -0,0 +1,23 @@
+import os
+from sqlalchemy import create_engine, Column, Integer, String, DateTime
+from sqlalchemy.orm import declarative_base, sessionmaker
+
+DATABASE_URL = "postgresql://safespace:AdminAdmin@postgresql-208383-0.cloudclusters.net:19712/safespace"
+
+engine = create_engine(DATABASE_URL)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+Base = declarative_base()
+
+class DBUser(Base):
+    __tablename__ = "users"
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=True)
+    email = Column(String, unique=True, index=True)
+    password = Column(String)
+
+db = SessionLocal()
+users = db.query(DBUser).all()
+print(f"Total users: {len(users)}")
+for u in users:
+    print(f"ID: {u.id}, Email: {u.email}, Password Hash: {u.password}")
+db.close()

clean_git_repo/UI/safespace/.dart_tool/chrome-device/Default/History

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18bb7fac167630752b1cfa38fdbd37883c97701d67639db61141b814498b3388
+size 163840

clean_git_repo/UI/safespace/.dart_tool/chrome-device/Default/Web Data

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3743dd646541935eebc92ec44dc065a71a5beefd8165facae2a5a73eeb6ae656
+size 157696

clean_git_repo/UI/safespace/.dart_tool/chrome-device/Default/shared_proto_db/000003.log

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