Deploy Learn Pathophysiology WC3 Edition

.dockerignore

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+__pycache__
+*.pyc
+*.pyo
+.Python
+*.egg
+*.egg-info
+.env
+.venv
+venv/
+node_modules/
+*.log
+.DS_Store
+.git

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
+chroma_db/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text
+chroma_db/**/*.bin filter=lfs diff=lfs merge=lfs -text
+chroma_db/*.sqlite3 filter=lfs diff=lfs merge=lfs -text
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+*.sqlite3 filter=lfs diff=lfs merge=lfs -text
 *.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
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Dockerfile

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+# ===========================================================================
+#  Learn Pathophysiology — WC3 Edition
+#  Multi-stage build: Vue frontend + FastAPI backend
+#  Deploy: HuggingFace Spaces (Docker SDK)
+# ===========================================================================
+
+# ---------- Stage 1: Build Vue Frontend ----------
+FROM node:20-slim AS frontend-build
+
+WORKDIR /build
+COPY frontend/package.json frontend/package-lock.json* ./
+RUN npm install
+COPY frontend/ ./
+RUN npm run build
+# Output goes to /build/dist/
+
+# ---------- Stage 2: Python Backend + Serve Static ----------
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install Python deps
+COPY backend/requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy backend code
+COPY backend/ ./
+
+# Copy built frontend from stage 1
+COPY --from=frontend-build /build/dist ./static/
+
+# Copy chroma_db (must be present in docker build context)
+COPY chroma_db/ ./chroma_db/
+
+# Environment
+ENV PORT=7860
+ENV CHROMA_DIR=/app/chroma_db
+# Auth (set via HuggingFace Spaces secrets)
+# ENV GOOGLE_CLIENT_ID=...
+# ENV JWT_SECRET=...
+# ENV GEMINI_API_KEY=...
+
+EXPOSE 7860
+
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,12 +1,93 @@
 ---
 title: Learn Pathophysiology
-emoji: 🏃
-colorFrom: blue
-colorTo: gray
+emoji: ⚔️
+colorFrom: purple
+colorTo: red
 sdk: docker
+app_port: 7860
 pinned: false
-license: apache-2.0
-short_description: MVP for an AI enabled pathophysiology learning app
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Learn Pathophysiology - WC3 Edition ⚔️
+
+AI-powered chatbot for learning **Pathophysiology** with authentic **Warcraft 3** styling!
+
+![WC3 Style](https://img.shields.io/badge/Style-Warcraft%203-orange)
+![AI Powered](https://img.shields.io/badge/AI-Gemini-blue)
+![Language](https://img.shields.io/badge/Language-Croatian-red)
+
+## 🎮 Features
+
+### 💬 AI Chat
+Ask questions about Pathophysiology in Croatian and get detailed, RAG-powered answers grounded in the official Gamulin textbook.
+
+### 📸 Image Analysis
+Upload textbook pages and get AI-powered analysis, explanations, and Q&A.
+
+### 🎨 WC3-Themed Interface
+Choose from 4 authentic Warcraft 3 race themes:
+- **Human** - Blue/silver Alliance theme
+- **Orc** - Red/brown Horde theme
+- **Night Elf** - Purple/teal nature theme
+- **Undead** - Green/dark Scourge theme
+
+### 🤖 Multi-Model Support
+- **Gemini 3 Flash Preview** - Fastest, newest model
+- **Gemini 2.5 Flash** - Fast and reliable
+- **Gemini 2.5 Pro** - Most capable for complex questions
+
+### 📚 RAG-Powered
+All answers are grounded in the official **Patofiziologija (Gamulin, Marušić, Kovač)** textbook, with citations to page numbers.
+
+## 🏗️ Technology Stack
+
+- **Frontend**: Vue 3 + TypeScript + Custom WC3 UI library
+- **Backend**: FastAPI (Python)
+- **AI**: Google Gemini API
+- **RAG**: ChromaDB vector database
+- **Deployment**: Docker
+
+## 🎓 For Medical Students
+
+This app is specifically designed for medical students at **University of Split School of Medicine** to help master Pathophysiology through:
+
+1. **Interactive Q&A** - Ask anything about Pathophysiology
+2. **Contextual Learning** - Get explanations grounded in your textbook
+3. **Image Analysis** - Understand complex diagrams and pages
+4. **Citation Support** - See exactly where information comes from
+
+## 🚀 Usage
+
+Simply:
+1. Select your preferred AI model (top-left sidebar)
+2. Choose your favorite WC3 race theme
+3. Start chatting or upload an image!
+
+### Example Questions (in Croatian):
+- "Što je hipertenzija i kako nastaje?"
+- "Objasni patofiziologiju dijabetesa tipa 2"
+- "Kako funkcionira renin-angiotenzin-aldosteron sustav?"
+
+## 🌐 Open Source
+
+This project is open source! Check out the code on GitHub:
+- Frontend: Vue 3 + TypeScript with custom WC3 UI components
+- Backend: FastAPI with Gemini AI integration
+- RAG: ChromaDB with Gemini embeddings
+
+## 📝 Credits
+
+- **UI Design**: Inspired by Warcraft 3 (Blizzard Entertainment)
+- **Content**: Based on Patofiziologija textbook by Gamulin, Marušić, Kovač
+- **AI**: Powered by Google Gemini
+- **Development**: University of Split School of Medicine project
+
+## ⚠️ Disclaimer
+
+This is an educational tool. Always verify medical information with official sources and your professors. The AI can make mistakes!
+
+---
+
+**Built with ❤️ and Warcraft 3 nostalgia**
+
+*For the Alliance! For the Horde! For passing exams!* ⚔️📚

backend/api.py

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+"""
+Learn Pathophysiology - FastAPI Backend
+
+Serves RAG + LLM API endpoints and Vue frontend static files.
+Deploy: HuggingFace Spaces (Docker) or run locally.
+"""
+
+import os
+import secrets
+import logging
+from pathlib import Path
+from datetime import datetime, timezone, timedelta
+
+import jwt
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException, Depends, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+from dotenv import load_dotenv
+from google import genai
+from google.genai import types
+from google.oauth2 import id_token as google_id_token
+from google.auth.transport import requests as google_requests
+import chromadb
+
+load_dotenv()
+
+logger = logging.getLogger(__name__)
+
+# =============================================================================
+# CONFIGURATION
+# =============================================================================
+
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY", "")
+CHROMA_DIR = os.environ.get("CHROMA_DIR", "../chroma_db")
+COLLECTION_NAME = "pathophysiology"
+EMBEDDING_MODEL = "gemini-embedding-001"
+DEFAULT_MODEL = "gemini-3-flash-preview"
+RAG_TOP_K = 5
+
+# Auth
+GOOGLE_CLIENT_ID = os.environ.get("GOOGLE_CLIENT_ID", "")
+JWT_SECRET = os.environ.get("JWT_SECRET", secrets.token_urlsafe(32))
+JWT_ALGORITHM = "HS256"
+JWT_EXPIRY_DAYS = 7
+AUTH_ENABLED = bool(GOOGLE_CLIENT_ID)  # disable auth if no client ID
+
+AVAILABLE_MODELS = {
+    "gemini-3-flash-preview": {
+        "name": "Gemini 3 Flash",
+        "description": "Najnoviji i najbrzi model",
+        "icon": "swords",
+        "wc3_name": "Blademaster",
+        "tier": "fast",
+    },
+    "gemini-2.5-flash": {
+        "name": "Gemini 2.5 Flash",
+        "description": "Brz i pouzdan",
+        "icon": "bow",
+        "wc3_name": "Shadow Hunter",
+        "tier": "fast",
+    },
+    "gemini-2.5-pro": {
+        "name": "Gemini 2.5 Pro",
+        "description": "Najpametniji za kompleksne zadatke",
+        "icon": "mage",
+        "wc3_name": "Archmage",
+        "tier": "smart",
+    },
+}
+
+SYSTEM_PROMPT = """Ti si "Learn Pathophysiology AI", strucni asistent za ucenje patofiziologije
+za studente medicine.
+
+ULOGA:
+- Objasnjavaš patofiziološke koncepte jasno i precizno
+- Koristiš primjere i analogije kad je moguce
+- Povezuješ koncepte s klinickom praksom
+- Odgovaraš na hrvatskom jeziku
+
+KONTEKST IZ BAZE ZNANJA:
+{rag_context}
+
+PRAVILA:
+1. Uvijek citiraj izvor kad koristiš informacije iz konteksta
+2. Ako nisi siguran, reci to otvoreno
+3. Koristi medicinsku terminologiju, ali objasni kompleksne termine
+4. Budi koncizan ali potpun u odgovorima
+5. Odgovaraj na hrvatskom jeziku"""
+
+# =============================================================================
+# SINGLETONS
+# =============================================================================
+
+_genai_client = None
+_chroma_collection = None
+
+
+def get_client():
+    global _genai_client
+    if _genai_client is None:
+        if not GEMINI_API_KEY:
+            raise HTTPException(status_code=500, detail="GEMINI_API_KEY not configured")
+        _genai_client = genai.Client(api_key=GEMINI_API_KEY)
+    return _genai_client
+
+
+def get_collection():
+    global _chroma_collection
+    if _chroma_collection is None:
+        chroma_path = Path(CHROMA_DIR)
+        if not chroma_path.exists():
+            # Try relative to this file
+            alt_path = Path(__file__).parent.parent.parent / "chroma_db"
+            if alt_path.exists():
+                chroma_path = alt_path
+            else:
+                return None
+        try:
+            client = chromadb.PersistentClient(path=str(chroma_path))
+            _chroma_collection = client.get_collection(COLLECTION_NAME)
+        except Exception as e:
+            logger.error(f"ChromaDB error: {e}")
+            return None
+    return _chroma_collection
+
+
+# =============================================================================
+# AUTH HELPERS
+# =============================================================================
+
+def create_jwt(email: str, name: str, picture: str = "") -> str:
+    payload = {
+        "sub": email,
+        "name": name,
+        "picture": picture,
+        "iat": datetime.now(timezone.utc),
+        "exp": datetime.now(timezone.utc) + timedelta(days=JWT_EXPIRY_DAYS),
+    }
+    return jwt.encode(payload, JWT_SECRET, algorithm=JWT_ALGORITHM)
+
+
+def decode_jwt(token: str) -> dict | None:
+    try:
+        return jwt.decode(token, JWT_SECRET, algorithms=[JWT_ALGORITHM])
+    except jwt.ExpiredSignatureError:
+        return None
+    except jwt.InvalidTokenError:
+        return None
+
+
+async def require_auth(request: Request):
+    """FastAPI dependency — returns user dict or raises 401."""
+    if not AUTH_ENABLED:
+        return {"sub": "anonymous", "name": "Local User"}
+
+    auth_header = request.headers.get("Authorization", "")
+    if not auth_header.startswith("Bearer "):
+        raise HTTPException(status_code=401, detail="Not authenticated")
+
+    token = auth_header[7:]
+    user = decode_jwt(token)
+    if not user:
+        raise HTTPException(status_code=401, detail="Invalid or expired token")
+    return user
+
+
+# =============================================================================
+# RAG FUNCTIONS
+# =============================================================================
+
+def embed_query(text: str) -> list[float]:
+    c = get_client()
+    result = c.models.embed_content(model=EMBEDDING_MODEL, contents=text)
+    return result.embeddings[0].values
+
+
+def query_rag(query_text: str, top_k: int = RAG_TOP_K):
+    coll = get_collection()
+    if coll is None:
+        return "Nema dostupnog konteksta.", []
+
+    try:
+        query_embedding = embed_query(query_text)
+        results = coll.query(
+            query_embeddings=[query_embedding],
+            n_results=top_k,
+            include=["documents", "metadatas", "distances"]
+        )
+
+        contexts = []
+        citations = []
+
+        if results and results["documents"] and results["documents"][0]:
+            for idx, (doc, meta, dist) in enumerate(zip(
+                results["documents"][0],
+                results["metadatas"][0],
+                results["distances"][0]
+            )):
+                contexts.append(doc)
+                similarity = max(0, 1 - dist / 2)
+                citations.append({
+                    "text": doc[:600] + "..." if len(doc) > 600 else doc,
+                    "score": round(similarity, 3),
+                    "source": meta.get("source", "Baza znanja"),
+                    "page_num": meta.get("page_num", "?"),
+                    "rank": idx + 1,
+                })
+
+        formatted = "\n\n---\n\n".join(contexts) if contexts else "Nema konteksta."
+        return formatted, citations
+    except Exception as e:
+        logger.error(f"RAG error: {e}")
+        return "Nema dostupnog konteksta.", []
+
+
+def generate_chat_response(message: str, history: list, model_name: str = ""):
+    model_name = model_name or DEFAULT_MODEL
+    if model_name not in AVAILABLE_MODELS:
+        model_name = DEFAULT_MODEL
+
+    c = get_client()
+    rag_context, citations = query_rag(message)
+    system_prompt = SYSTEM_PROMPT.format(rag_context=rag_context)
+
+    contents = [system_prompt]
+    for msg in (history or [])[-10:]:
+        role = msg.get("role", "user")
+        content = msg.get("content", "")
+        if role == "user":
+            contents.append(f"Student: {content}")
+        else:
+            contents.append(f"Asistent: {content}")
+    contents.append(f"Student: {message}")
+
+    response = c.models.generate_content(
+        model=model_name,
+        contents="\n\n".join(contents),
+        config=types.GenerateContentConfig(
+            temperature=0.7,
+            max_output_tokens=8192,
+            top_p=0.9,
+        )
+    )
+    return response.text, citations
+
+
+def do_analyze_image(image_bytes: bytes, question: str = "", model_name: str = ""):
+    model_name = model_name or DEFAULT_MODEL
+    if model_name not in AVAILABLE_MODELS:
+        model_name = DEFAULT_MODEL
+
+    c = get_client()
+
+    # Extract keywords from image
+    extract_resp = c.models.generate_content(
+        model=model_name,
+        contents=[
+            types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg"),
+            "Izvuci glavni topic i kljucne rijeci s ove stranice. Odgovori kratko."
+        ],
+        config=types.GenerateContentConfig(temperature=0.3, max_output_tokens=200)
+    )
+
+    rag_context, citations = query_rag(extract_resp.text, top_k=3)
+
+    if question:
+        prompt = (
+            f"Analiziraj ovu stranicu iz materijala za patofiziologiju "
+            f"i odgovori na pitanje studenta.\n\n"
+            f"PITANJE: {question}\n\n"
+            f"KONTEKST IZ BAZE ZNANJA:\n{rag_context}\n\n"
+            f"Odgovori detaljno na hrvatskom jeziku."
+        )
+    else:
+        prompt = (
+            f"Analiziraj ovu stranicu iz materijala za patofiziologiju.\n\n"
+            f"1. Prepoznaj glavni topic\n2. Izvuci kljucne pojmove\n"
+            f"3. Sazmi glavne tocke\n4. Objasni klinicku vaznost\n\n"
+            f"KONTEKST IZ BAZE ZNANJA:\n{rag_context}\n\n"
+            f"Odgovori na hrvatskom jeziku."
+        )
+
+    response = c.models.generate_content(
+        model=model_name,
+        contents=[
+            types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg"),
+            prompt
+        ],
+        config=types.GenerateContentConfig(temperature=0.5, max_output_tokens=8192)
+    )
+    return response.text, citations
+
+
+# =============================================================================
+# FASTAPI APP
+# =============================================================================
+
+app = FastAPI(title="Learn Pathophysiology API", version="1.0.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# --- Request / Response Models ---
+
+class ChatRequest(BaseModel):
+    message: str
+    model: str = ""
+    history: list = []
+
+
+# --- Auth Models ---
+
+class GoogleAuthRequest(BaseModel):
+    credential: str  # Google ID token
+
+
+# --- Auth Endpoints ---
+
+@app.get("/api/auth/config")
+async def auth_config():
+    """Tell the frontend if auth is required and the Google Client ID."""
+    return {
+        "auth_enabled": AUTH_ENABLED,
+        "google_client_id": GOOGLE_CLIENT_ID if AUTH_ENABLED else None,
+    }
+
+
+@app.post("/api/auth/google")
+async def auth_google(req: GoogleAuthRequest):
+    """Verify Google ID token and return a JWT session token."""
+    if not AUTH_ENABLED:
+        raise HTTPException(status_code=400, detail="Auth not enabled")
+
+    try:
+        idinfo = google_id_token.verify_oauth2_token(
+            req.credential,
+            google_requests.Request(),
+            GOOGLE_CLIENT_ID,
+        )
+
+        email = idinfo.get("email", "")
+        name = idinfo.get("name", email)
+        picture = idinfo.get("picture", "")
+
+        token = create_jwt(email, name, picture)
+        return {
+            "token": token,
+            "user": {"email": email, "name": name, "picture": picture},
+        }
+    except ValueError as e:
+        logger.error(f"Google auth failed: {e}")
+        raise HTTPException(status_code=401, detail="Invalid Google token")
+
+
+@app.get("/api/auth/me")
+async def auth_me(user=Depends(require_auth)):
+    """Return the current user's info from their JWT."""
+    return {
+        "email": user.get("sub", ""),
+        "name": user.get("name", ""),
+        "picture": user.get("picture", ""),
+    }
+
+
+# --- API Endpoints (public) ---
+
+@app.get("/api/health")
+async def health():
+    coll = get_collection()
+    return {
+        "status": "ok",
+        "chroma_docs": coll.count() if coll else 0,
+        "has_api_key": bool(GEMINI_API_KEY),
+    }
+
+
+@app.get("/api/models")
+async def list_models():
+    return {"models": AVAILABLE_MODELS, "default": DEFAULT_MODEL}
+
+
+@app.get("/api/stats")
+async def stats():
+    coll = get_collection()
+    return {
+        "documents": coll.count() if coll else 0,
+        "collection": COLLECTION_NAME,
+    }
+
+
+# --- API Endpoints (protected) ---
+
+@app.post("/api/chat")
+async def chat(req: ChatRequest, user=Depends(require_auth)):
+    try:
+        model = req.model or DEFAULT_MODEL
+        reply, citations = generate_chat_response(req.message, req.history, model)
+        return {"reply": reply, "citations": citations, "model_used": model}
+    except Exception as e:
+        logger.error(f"Chat error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/analyze-image")
+async def analyze_image_endpoint(
+    image: UploadFile = File(...),
+    question: str = Form(""),
+    model: str = Form(""),
+    user=Depends(require_auth),
+):
+    try:
+        model_name = model or DEFAULT_MODEL
+        image_bytes = await image.read()
+        analysis, citations = do_analyze_image(image_bytes, question, model_name)
+        return {"analysis": analysis, "citations": citations, "model_used": model_name}
+    except Exception as e:
+        logger.error(f"Image analysis error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# --- Serve Vue Frontend (production) ---
+static_dir = Path(__file__).parent / "static"
+if static_dir.exists():
+    @app.get("/")
+    async def serve_index():
+        return FileResponse(str(static_dir / "index.html"))
+
+    app.mount("/", StaticFiles(directory=str(static_dir), html=True), name="static")
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("api:app", host="0.0.0.0", port=7860, reload=True)

backend/requirements.txt

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+fastapi>=0.110.0
+uvicorn[standard]>=0.27.0
+google-genai>=1.0.0
+google-auth>=2.0.0
+chromadb>=0.5.0
+python-dotenv>=1.0.0
+python-multipart>=0.0.9
+pyjwt>=2.0.0

backend/static/assets/index-BzhPXs6E.css

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backend/static/index.html

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+<!DOCTYPE html>
+<html lang="hr">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Learn Pathophysiology — WC3 Edition</title>
+  <link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 100 100'><text y='.9em' font-size='90'>⚔️</text></svg>">
+  <link rel="preconnect" href="https://fonts.googleapis.com">
+  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+  <link href="https://fonts.googleapis.com/css2?family=Cinzel:wght@400;600;700;900&family=Cinzel+Decorative:wght@400;700;900&family=Crimson+Text:ital,wght@0,400;0,600;0,700;1,400&display=swap" rel="stylesheet">
+  <script type="module" crossorigin src="/assets/index-BSpkv-VB.js"></script>
+  <link rel="stylesheet" crossorigin href="/assets/index-BzhPXs6E.css">
+</head>
+<body>
+  <div id="app"></div>
+</body>
+</html>

chroma_db/34c32620-31ab-4a2f-bd92-3fcfd7be3432/data_level0.bin

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+size 49712000