fix: resolve SSR bug and optimize backend for HF Spaces

frontend/src/app/page.tsx

@@ -3,7 +3,6 @@
 import { useState, useRef } from "react";
 import { FileText, Send, Briefcase, FileSearch, HelpCircle, Map, Upload, FileSignature } from "lucide-react";
 import { motion } from "framer-motion";
-import html2pdf from "html2pdf.js";
 
 export default function Home() {
   const [activeTab, setActiveTab] = useState("upwork");
@@ -138,8 +137,9 @@ export default function Home() {
     setLoading(false);
   };
 
-  const handleDownloadPDF = () => {
+  const handleDownloadPDF = async () => {
     if (!tailoredResume) return;
+    const html2pdf = (await import("html2pdf.js")).default;
     const element = document.createElement("div");
     element.innerHTML = tailoredResume;
     const opt = {

rag/embeddings.py

@@ -1,7 +1,14 @@
-from sentence_transformers import SentenceTransformer
+import google.generativeai as genai
+import os
+from dotenv import load_dotenv
 
-model = SentenceTransformer("all-MiniLM-L6-v2")
+load_dotenv()
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 
 def get_embedding(text):
-    embedding = model.encode(text)
-    return embedding.tolist()
+    result = genai.embed_content(
+        model="models/embedding-001",
+        content=text,
+        task_type="retrieval_document"
+    )
+    return result['embedding']

rag/qdrant_store.py

@@ -11,7 +11,7 @@ def create_collection():
     if COLLECTION_NAME not in existing:
         client.create_collection(
             collection_name=COLLECTION_NAME,
-            vectors_config=VectorParams(size=384, distance=Distance.COSINE)
+            vectors_config=VectorParams(size=768, distance=Distance.COSINE)
         )
 
 def store_document(text, metadata={}):

requirements.txt

@@ -5,7 +5,6 @@ langchain-openai
 openai
 pdfplumber
 qdrant-client
-sentence-transformers
 python-dotenv
 tiktoken
 google-generativeai