Configure for 100% self-contained CPU deployment

Dockerfile

@@ -0,0 +1,43 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Set Hugging Face cache directory to a build-in writeable directory
+ENV HF_HOME=/app/.cache/huggingface
+# Tell NLTK where to find downloaded data
+ENV NLTK_DATA=/app/nltk_data
+
+# Install system deps for numpy/umap
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential gcc && \
+    rm -rf /var/lib/apt/lists/*
+
+# Copy and install Python deps
+COPY pyproject.toml .
+RUN pip install --no-cache-dir .
+
+# Pre-download NLTK data at build time to the app-owned directory
+RUN mkdir -p /app/nltk_data && \
+    python -c "import nltk; nltk.download('punkt', download_dir='/app/nltk_data'); nltk.download('punkt_tab', download_dir='/app/nltk_data'); nltk.download('stopwords', download_dir='/app/nltk_data')"
+
+# Pre-download HF embedding models at build time
+RUN python -c "from sentence_transformers import SentenceTransformer; \
+    SentenceTransformer('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True); \
+    SentenceTransformer('google/gemma-3n-embedding-exp', trust_remote_code=True); \
+    SentenceTransformer('Qwen/Qwen3-Embedding-0.6B', trust_remote_code=True)"
+
+# Pre-download HF Qwen LLM model at build time
+RUN python -c "from transformers import AutoTokenizer, AutoModelForCausalLM; \
+    AutoTokenizer.from_pretrained('Qwen/Qwen2.5-1.5B-Instruct'); \
+    AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-1.5B-Instruct')"
+
+# Copy app code
+COPY . .
+
+# Grant full read/write/execute permissions on /app for non-root containers in Hugging Face
+RUN chmod -R 777 /app
+
+# HF Spaces expects port 7860
+EXPOSE 7860
+
+CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "7860"]

backend/constants.py

@@ -9,24 +9,24 @@ The chunks are labelled "Chunk A" and "Chunk B". You must ignore any model names
 Score each dimension independently. Do not let one dimension influence another.
 
 1. Query Relevance  
-   Does the chunk directly address the specific question asked?  
-   10 = precisely on-topic, 1 = entirely off-topic.  
-   Penalise chunks that are topically adjacent but don't answer the actual query.
+    Does the chunk directly address the specific question asked?  
+    10 = precisely on-topic, 1 = entirely off-topic.  
+    Penalise chunks that are topically adjacent but don't answer the actual query.
 
 2. Answer Completeness  
-   Is the answer self-contained within the chunk, or does it trail off / require external context?  
-   10 = standalone complete answer, 1 = fragment with no usable answer.  
-   Do NOT reward length. A concise, complete answer scores higher than a verbose partial one.
+    Is the answer self-contained within the chunk, or does it trail off / require external context?  
+    10 = standalone complete answer, 1 = fragment with no usable answer.  
+    Do NOT reward length. A concise, complete answer scores higher than a verbose partial one.
 
 3. Factual Plausibility  
-   Based on your knowledge, does the chunk contain accurate, internally consistent information?  
-   10 = no detectable errors, 1 = clearly wrong or contradictory.  
-   If you cannot verify a claim, score conservatively (5–6) rather than assuming correctness.  
-   Do not penalise a chunk for information you simply don't recognise.
+    Based on your knowledge, does the chunk contain accurate, internally consistent information?  
+    10 = no detectable errors, 1 = clearly wrong or contradictory.  
+    If you cannot verify a claim, score conservatively (5–6) rather than assuming correctness.  
+    Do not penalise a chunk for information you simply don't recognise.
 
 4. Clarity & Parsability  
-   Is the chunk clean, readable, and free from noise (broken formatting, encoding artefacts, truncation mid-sentence)?  
-   10 = polished and easy to parse, 1 = heavily noisy or unreadable.
+    Is the chunk clean, readable, and free from noise (broken formatting, encoding artefacts, truncation mid-sentence)?  
+    10 = polished and easy to parse, 1 = heavily noisy or unreadable.
 
 # OVERALL SCORE
 overall = (relevance + completeness + plausibility + clarity) / 4
@@ -58,28 +58,28 @@ Search Query: {search_query}
 # OUTPUT RULE
 OUTPUT: respond with this exact JSON structure and nothing else:
 {{
-  "winner": "chunk_a" | "chunk_b" | "tie",
-  "confidence": float,
-  "chunk_a_score": {{
+    "winner": "chunk_a" | "chunk_b" | "tie",
+    "confidence": float,
+    "chunk_a_score": {{
     "query_relevance": int,
     "answer_completeness": int,
     "factual_plausibility": int,
     "clarity": int,
     "overall": float
-  }},
-  "chunk_b_score": {{
+    }},
+    "chunk_b_score": {{
     "query_relevance": int,
     "answer_completeness": int,
     "factual_plausibility": int,
     "clarity": int,
     "overall": float
-  }},
-  "winner_reason": "2-3 sentences",
-  "deciding_dimension": "e.g. query_relevance",
-  "chunk_a_strengths": ["..."],
-  "chunk_a_weaknesses": ["..."],
-  "chunk_b_strengths": ["..."],
-  "chunk_b_weaknesses": ["..."]
+    }},
+    "winner_reason": "2-3 sentences",
+    "deciding_dimension": "e.g. query_relevance",
+    "chunk_a_strengths": ["..."],
+    "chunk_a_weaknesses": ["..."],
+    "chunk_b_strengths": ["..."],
+    "chunk_b_weaknesses": ["..."]
 }}
 """
 
@@ -91,3 +91,10 @@ Match the tone and vocabulary a subject-matter expert would use when writing abo
 Query: {search_text}
 
 Passage:"""
+
+
+MODEL_MAP = {
+    "nomic-embed-text": "nomic-ai/nomic-embed-text-v1.5",
+    "EmbeddingGemma": "google/gemma-3n-embedding-exp",
+    "qwen3-embedding:0.6b": "Qwen/Qwen3-Embedding-0.6B",
+}

backend/engines/embedding.py

@@ -1,29 +1,44 @@
 from typing import Sequence
-import httpx
 
-from backend.models.schemas import ChunkNode, EmbeddingModel
+# import httpx
+import os
+from backend.constants import MODEL_MAP
+from backend.models.schemas import ChunkNode
+from sentence_transformers import SentenceTransformer
+
+
+_model_cache = {}
 
 
 class EmbeddingEngine:
     def __init__(self, embedding_model):
         self.embedding_model = embedding_model
-        self.client = httpx.AsyncClient(timeout=60)
+        # self.client = httpx.AsyncClient(timeout=60)
+        hf_name = MODEL_MAP.get(embedding_model, embedding_model)
+
+        if hf_name not in _model_cache:
+            _model_cache[hf_name] = SentenceTransformer(hf_name, trust_remote_code=True)
+        self.model = _model_cache[hf_name]
 
     async def generate_embeddings(self, chunks: Sequence[ChunkNode | str]):
         texts = [c if isinstance(c, str) else c.text for c in chunks]
-        res = await self.client.post(
-            "http://localhost:11434/api/embed",
-            json={
-                "model": self.embedding_model,
-                "input": texts,
-            },
-        )
-
-        res_json = res.json()
-
-        if "embeddings" not in res_json or not res_json["embeddings"]:
-            raise ValueError(
-                f"Ollama failed to generate embeddings for {self.embedding_model}. Response: {res_json}"
-            )
-
-        return res_json["embeddings"]
+        # commented for using sentence transformer.
+        # res = await self.client.post(
+        #     "http://localhost:11434/api/embed",
+        #     json={
+        #         "model": self.embedding_model,
+        #         "input": texts,
+        #     },
+        # )
+
+        # res_json = res.json()
+
+        # if "embeddings" not in res_json or not res_json["embeddings"]:
+        #     raise ValueError(
+        #         f"Ollama failed to generate embeddings for {self.embedding_model}. Response: {res_json}"
+        #     )
+
+        # return res_json["embeddings"]
+        # sentence-transformers is sync, but it's fast on CPU for small batches
+        embeddings = self.model.encode(texts, normalize_embeddings=True)
+        return embeddings.tolist()

backend/engines/llm_client.py

@@ -1,17 +1,56 @@
 from typing import Optional
-import ollama
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+_llm_pipeline = None
 
 
 class OllamaClient:
     def __init__(self):
-        self.model = "gemma4:e2b"
-        self.client = ollama.AsyncClient()
+        self.model_name = "Qwen/Qwen2.5-1.5B-Instruct"
+
+    def _get_pipeline(self):
+        global _llm_pipeline
+        if _llm_pipeline is None:
+            tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+            model = AutoModelForCausalLM.from_pretrained(
+                self.model_name,
+                torch_dtype=torch.float32,
+                device_map="cpu"
+            )
+            _llm_pipeline = pipeline(
+                "text-generation",
+                model=model,
+                tokenizer=tokenizer
+            )
+        return _llm_pipeline
 
     async def generate(self, prompt: str, response_format: Optional[str] = "json"):
-        args = {"model": self.model, "prompt": prompt}
+        pipe = self._get_pipeline()
+        
+        messages = [
+            {"role": "system", "content": "You are a helpful evaluation referee. Output raw JSON only, matching the exact format requested."},
+            {"role": "user", "content": prompt}
+        ]
+        
+        generation_kwargs = {
+            "max_new_tokens": 512,
+            "temperature": 0.1,
+            "do_sample": False
+        }
+        
+        outputs = pipe(messages, **generation_kwargs)
+        result_text = outputs[0]["generated_text"][-1]["content"]
+        
+        cleaned_text = result_text.strip()
+        if cleaned_text.startswith("```"):
+            lines = cleaned_text.splitlines()
+            if lines[0].startswith("```"):
+                lines = lines[1:]
+            if lines and lines[-1].strip() == "```":
+                lines = lines[:-1]
+            cleaned_text = "\n".join(lines).strip()
+            
+        return cleaned_text
 
-        if response_format:
-            args["format"] = response_format
 
-        response = await self.client.generate(**args)  # type: ignore
-        return response["response"]

backend/main.py

@@ -8,12 +8,13 @@ from fastapi.responses import FileResponse
 
 
 def initialize_nltk():
-    nltk_data_dir = os.path.expanduser("~/nltk_data")
+    nltk_data_dir = os.environ.get("NLTK_DATA", os.path.expanduser("~/nltk_data"))
     os.makedirs(nltk_data_dir, exist_ok=True)
 
     if nltk_data_dir not in nltk.data.path:
         nltk.data.path.append(nltk_data_dir)
 
+
     resources = {
         "tokenizers/punkt": "punkt",
         "tokenizers/punkt_tab": "punkt_tab",

backend/rate_limiter.py

@@ -0,0 +1,48 @@
+from collections import defaultdict, deque
+from datetime import datetime, timedelta
+
+
+class RateLimiter:
+    def __init__(
+        self, requests_per_minute=2, violation_threshold=10, ban_duration_minutes=60
+    ):
+        self.requests_per_minute = requests_per_minute
+        self.violation_threshold = violation_threshold
+        self.ban_duration = timedelta(minutes=ban_duration_minutes)
+
+        self.requests = defaultdict(deque)
+        self.violations = defaultdict(int)
+        self.banned_until = {}
+
+    def allow(self, ip: str) -> bool:
+        now = datetime.utcnow()
+
+        # Check ban
+        if ip in self.banned_until:
+            if now < self.banned_until[ip]:
+                return False
+            del self.banned_until[ip]
+
+        # Remove requests older than 1 minute
+        window = now - timedelta(minutes=1)
+        reqs = self.requests[ip]
+
+        while reqs and reqs[0] < window:
+            reqs.popleft()
+
+        # Rate limit hit
+        if len(reqs) >= self.requests_per_minute:
+            self.violations[ip] += 1
+
+            if self.violations[ip] >= self.violation_threshold:
+                self.banned_until[ip] = now + self.ban_duration
+
+            return False
+
+        reqs.append(now)
+        return True
+
+
+limiter = RateLimiter(
+    requests_per_minute=5, violation_threshold=10, ban_duration_minutes=60
+)

backend/routers/retrieval_router.py

@@ -4,10 +4,12 @@ from backend.engines.llm_client import OllamaClient
 from backend.constants import system_instructions
 import asyncio
 from typing import Any, Dict, List, Optional
-from fastapi import APIRouter
+from fastapi import APIRouter, Request, HTTPException
 import json
 from backend.engines.embedding import EmbeddingEngine
 from backend.engines.reducer import ReducerEngine
+from backend.rate_limiter import limiter
+
 from backend.models.schemas import (
     CompareRequest,
     CompareResponse,
@@ -25,13 +27,19 @@ router = APIRouter(prefix="/api", tags=["retrieval"])
 
 
 @router.post("/retrieve", response_model=QueryResponse)
-async def retrieve(request: QueryRequest):
+async def retrieve(request: QueryRequest, http_request: Request):
+
+    if request.use_hyde:
+        client_ip = http_request.client.host if http_request.client else "127.0.0.1"
+        if not limiter.allow(client_ip):
+            raise HTTPException(
+                status_code=429,
+                detail="Rate limit exceeded. You can only make 2 LLM requests per minute."
+            )
+        search_text = await get_hyde_text(request.search_text)
+    else:
+        search_text = request.search_text
 
-    search_text = (
-        await get_hyde_text(request.search_text)
-        if request.use_hyde
-        else request.search_text
-    )
 
     limit = request.top_k * 4 if request.use_reranking else request.top_k
 
@@ -69,12 +77,18 @@ async def retrieve(request: QueryRequest):
 
 
 @router.post("/compare", response_model=CompareResponse)
-async def compare(request: CompareRequest):
-    retrieval_text = (
-        await get_hyde_text(request.search_text)
-        if request.use_hyde
-        else request.search_text
-    )
+async def compare(request: CompareRequest, http_request: Request):
+    if request.use_hyde:
+        client_ip = http_request.client.host if http_request.client else "127.0.0.1"
+        if not limiter.allow(client_ip):
+            raise HTTPException(
+                status_code=429,
+                detail="Rate limit exceeded. You can only make 2 LLM requests per minute."
+            )
+        retrieval_text = await get_hyde_text(request.search_text)
+    else:
+        retrieval_text = request.search_text
+
     limit = request.top_k * 4 if request.use_reranking else request.top_k
     (
         (retrieved_chunks_a, _),
@@ -125,8 +139,15 @@ async def compare(request: CompareRequest):
 
 
 @router.post("/judge", response_model=JudgeResponse)
-async def judge(request: JudgeRequest):
+async def judge(request: JudgeRequest, http_request: Request):
+    client_ip = http_request.client.host if http_request.client else "127.0.0.1"
+    if not limiter.allow(client_ip):
+        raise HTTPException(
+            status_code=429,
+            detail="Rate limit exceeded. You can only make 2 LLM requests per minute."
+        )
     prompt = system_instructions.format(
+
         search_query=request.search_query,
         chunk_a=request.chunk_a,
         chunk_b=request.chunk_b,

frontend/app.js

@@ -1296,11 +1296,19 @@ async function runQuerySimulator() {
     });
 
     if (!res.ok) {
-      throw new Error(`HTTP error! status: ${res.status}`);
+      let errMsg = `HTTP error! status: ${res.status}`;
+      try {
+        const errData = await res.json();
+        if (errData && errData.detail) {
+          errMsg = errData.detail;
+        }
+      } catch (_) {}
+      throw new Error(errMsg);
     }
 
     const data = await res.json();
 
+
     // Parse the query coordinates returned by UMAP
     const targetX = data.query_coords[0];
     const targetY = data.query_coords[1];
@@ -1418,7 +1426,7 @@ async function runQuerySimulator() {
     // ----------------------------------------------
   } catch (err) {
     console.error("Query failed:", err);
-    dom.queryResultsList.innerHTML = `<div style="color: #ef4444; padding: 1rem;">Failed to fetch results. Check console.</div>`;
+    dom.queryResultsList.innerHTML = `<div style="color: #ef4444; padding: 1rem;">${escapeHtml(err.message)}</div>`;
   } finally {
     dom.btnQuery.disabled = false;
     dom.btnQuery.innerHTML = "🔍 Query";
@@ -1442,6 +1450,26 @@ if (dom.closeDrawer) {
   });
 }
 
+// Bind HyDE change warning toasts
+const queryUseHyde = document.getElementById("query-use-hyde");
+if (queryUseHyde) {
+  queryUseHyde.addEventListener("change", (e) => {
+    if (e.target.checked) {
+      showToast("HyDE enabled! Query generation via LLM will take a bit longer (around 2-3 seconds).", "info");
+    }
+  });
+}
+
+const arenaUseHyde = document.getElementById("arena-use-hyde");
+if (arenaUseHyde) {
+  arenaUseHyde.addEventListener("change", (e) => {
+    if (e.target.checked) {
+      showToast("HyDE enabled! Comparison query generation via LLM will take a bit longer (around 2-3 seconds).", "info");
+    }
+  });
+}
+
+
 // ============================================================
 // TASK 3.5: ARENA COMPARISON LOGIC
 // ============================================================
@@ -1531,13 +1559,20 @@ if (domArena.btnFight) {
         }),
       });
 
-      if (!res.ok)
-        throw new Error(
-          "Comparison failed. Check if server is running and /api/compare exists.",
-        );
+      if (!res.ok) {
+        let errMsg = "Comparison failed. Check if server is running.";
+        try {
+          const errData = await res.json();
+          if (errData && errData.detail) {
+            errMsg = errData.detail;
+          }
+        } catch (_) {}
+        throw new Error(errMsg);
+      }
 
       const data = await res.json();
 
+
       // Render HyDE box if present
       const hydeText = document.getElementById("arena-hyde-text");
       if (hydeContainer && hydeText) {
@@ -1646,7 +1681,7 @@ if (domReferee.btnCall) {
     domReferee.verdictBox.style.display = "none";
 
     const statuses = [
-      "⚖️ Summoning Gemma evaluation referee...",
+      "⚖️ Summoning AI evaluation referee...",
       "🔍 Blinding model names and metadata...",
       "🧠 Auditing Corner A relevance and clarity...",
       "🧠 Auditing Corner B completeness and truthfulness...",
@@ -1671,12 +1706,19 @@ if (domReferee.btnCall) {
         }),
       });
 
-      if (!res.ok)
-        throw new Error(
-          "Gemma evaluation failed. Check if local Ollama server is running.",
-        );
+      if (!res.ok) {
+        let errMsg = "Evaluation referee failed to respond.";
+        try {
+          const errData = await res.json();
+          if (errData && errData.detail) {
+            errMsg = errData.detail;
+          }
+        } catch (_) {}
+        throw new Error(errMsg);
+      }
 
       const data = await res.json();
+
       clearInterval(statusInterval);
 
       renderRefereeScorecard(data, labelA, labelB);
@@ -1816,3 +1858,41 @@ function renderScoreRow(label, valA, valB) {
     </div>
   `;
 }
+
+// --- TOAST NOTIFICATIONS SYSTEM ---
+function showToast(message, type = "info") {
+  let container = document.querySelector(".toast-container");
+  if (!container) {
+    container = document.createElement("div");
+    container.className = "toast-container";
+    document.body.appendChild(container);
+  }
+
+  const toast = document.createElement("div");
+  toast.className = `toast toast-${type}`;
+  
+  let icon = "ℹ️";
+  if (type === "warning") icon = "⚠️";
+  else if (type === "success") icon = "✅";
+  else if (type === "error") icon = "❌";
+  else if (type === "info") icon = "🔮"; // Default to crystal ball for HyDE/LLM notice
+
+  toast.innerHTML = `
+    <span>${icon}</span>
+    <span style="flex: 1;">${message}</span>
+  `;
+
+  container.appendChild(toast);
+
+  // Auto-remove after 4 seconds
+  setTimeout(() => {
+    toast.classList.add("toast-fade-out");
+    setTimeout(() => {
+      toast.remove();
+      if (container.children.length === 0) {
+        container.remove();
+      }
+    }, 300);
+  }, 4000);
+}
+

frontend/index.html

@@ -429,16 +429,17 @@
               </div>
               
               <div class="referee-trigger-box" id="referee-trigger-box">
-                <p class="referee-prompt-text">Call upon the local Gemma model to analyze, score, and judge both contexts!</p>
+                <p class="referee-prompt-text">Call upon the AI evaluation referee to analyze, score, and judge both contexts! (Takes 2-3 seconds)</p>
                 <button class="btn-run" id="btn-call-referee" style="margin: 0 auto; display: block; max-width: 250px;">⚖️ CALL THE REFEREE</button>
               </div>
 
               <!-- Loading scanner status -->
               <div class="referee-loading-box" id="referee-loading-box" style="display: none;">
                 <div class="skeleton-loader" style="max-width: 400px; margin: 0 auto 1rem; border-radius: 6px;"></div>
-                <div class="referee-status-text" id="referee-status-text">Summoning Gemma to review the case...</div>
+                <div class="referee-status-text" id="referee-status-text">Summoning AI referee to review the case...</div>
               </div>
 
+
               <!-- Scorecard Verdict Display -->
               <div class="referee-verdict-box" id="referee-verdict-box" style="display: none;"></div>
             </div>

frontend/styles.css

@@ -698,6 +698,71 @@ input[type="range"]::-moz-range-thumb {
 /* ============================================================
    RETRIEVAL X-RAY HIGHLIGHTS (TASK 3.4)
    ============================================================ */
+
+/* --- Premium Toast Notification System --- */
+.toast-container {
+  position: fixed;
+  bottom: 24px;
+  right: 24px;
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+  z-index: 9999;
+  pointer-events: none;
+}
+
+.toast {
+  pointer-events: auto;
+  background-color: var(--bg-surface-raised, #1f2937);
+  border: 1px solid var(--border-default, #374151);
+  color: var(--text-primary, #f3f4f6);
+  padding: 12px 18px;
+  border-radius: var(--radius-md, 8px);
+  box-shadow: var(--shadow-lg, 0 10px 15px -3px rgba(0, 0, 0, 0.3));
+  font-family: var(--font-sans, system-ui);
+  font-size: 0.85rem;
+  display: flex;
+  align-items: center;
+  gap: var(--space-sm, 8px);
+  min-width: 250px;
+  max-width: 380px;
+  animation: toast-slide-in 0.3s cubic-bezier(0.16, 1, 0.3, 1) forwards;
+  transition: all 0.3s ease;
+  border-left: 4px solid var(--superman-blue, #3b82f6);
+}
+
+.toast.toast-info {
+  border-left-color: var(--superman-blue, #3b82f6);
+}
+
+.toast.toast-warning {
+  border-left-color: var(--warning-color, #f59e0b);
+}
+
+.toast.toast-success {
+  border-left-color: var(--success-color, #10b981);
+}
+
+.toast.toast-error {
+  border-left-color: var(--superman-red, #ef4444);
+}
+
+.toast.toast-fade-out {
+  opacity: 0;
+  transform: translateY(10px);
+}
+
+@keyframes toast-slide-in {
+  from {
+    opacity: 0;
+    transform: translateY(20px) scale(0.95);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0) scale(1);
+  }
+}
+
 /* --- Select Dropdown Styling --- */
 .select-input {
   padding: 8px 12px;

pyproject.toml

@@ -15,6 +15,7 @@ dependencies = [
     "nltk>=3.9.4",
     "umap-learn>=0.5.12",
     "numpy>=2.4.4",
-    "ollama>=0.6.2",
     "flashrank>=0.2.10",
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