+
+ {/* Language toggle — top right */}
+
+
-
-
- {sent && mode === "magic" &&
}
- {sent && mode === "forgot" &&
}
+
+ {/* Logo */}
+
+
+
+
+
Interview OS
+
{s.tagline}
+
+
+ {/* Card */}
+
+
+ {sent && mode === "magic" &&
}
+ {sent && mode === "forgot" &&
}
{!sent && mode === "password" && (
@@ -113,19 +192,19 @@ export function LoginScreen({ onDemo }) {
{!sent && mode === "magic" && (
@@ -133,30 +212,33 @@ export function LoginScreen({ onDemo }) {
{!sent && mode === "forgot" && (
)}
-
diff --git a/src/components/modals/ImportChatGPTModal.jsx b/src/components/modals/ImportChatGPTModal.jsx
deleted file mode 100644
index 9196c8b..0000000
--- a/src/components/modals/ImportChatGPTModal.jsx
+++ /dev/null
@@ -1,409 +0,0 @@
-import { useState, useRef } from "react";
-import JSZip from "jszip";
-import { STAGE } from "../../utils/constants.js";
-import { T } from "../../constants/index.js";
-import { callAI } from "../../lib/ai.js";
-import { supabase } from "../../supabase.js";
-import Ic from "../ui/Ic.jsx";
-import Btn from "../ui/Btn.jsx";
-import Badge from "../ui/Badge.jsx";
-
-// ─── ChatGPT Import helpers ──────────────────────────────────────────────────
-function extractMessagesFromConversation(conv) {
- const mapping = conv.mapping || {};
- const msgs = [];
- Object.values(mapping).forEach(node => {
- const msg = node?.message;
- if (!msg) return;
- const role = msg.author?.role;
- if (role !== "user" && role !== "assistant") return;
- const parts = msg.content?.parts || [];
- const text = parts.filter(p => typeof p === "string").join("\n").trim();
- if (text) msgs.push({ role, content: text, time: msg.create_time || 0 });
- });
- return msgs.sort((a, b) => a.time - b.time);
-}
-
-function conversationText(conv) {
- return extractMessagesFromConversation(conv)
- .map(m => `[${m.role === "user" ? "Eu" : "ChatGPT"}]: ${m.content}`)
- .join("\n\n");
-}
-
-function parseConversationsJson(raw) {
- try {
- const data = JSON.parse(raw);
- return Array.isArray(data) ? data : [];
- } catch { return []; }
-}
-
-async function loadConversationsFromFile(file) {
- const name = file.name.toLowerCase();
- if (name.endsWith(".json")) {
- return parseConversationsJson(await file.text());
- }
- if (name.endsWith(".zip")) {
- const zip = await JSZip.loadAsync(file);
- const jsonFile = zip.file("conversations.json");
- if (!jsonFile) throw new Error("conversations.json não encontrado no ZIP.");
- return parseConversationsJson(await jsonFile.async("string"));
- }
- throw new Error("Formato não suportado. Use o arquivo .zip ou conversations.json.");
-}
-
-function filterRecentConversations(convs, months = 2) {
- const cutoff = Date.now() / 1000 - months * 30 * 24 * 3600;
- return convs.filter(c => (c.update_time || c.create_time || 0) >= cutoff);
-}
-
-function filterByProject(convs, projectId) {
- if (!projectId) return convs;
- return convs.filter(c => c.project_id === projectId);
-}
-
-function getProjects(convs) {
- const map = {};
- convs.forEach(c => {
- if (c.project_id) map[c.project_id] = (map[c.project_id] || 0) + 1;
- });
- return Object.entries(map).map(([id, count]) => ({ id, count }));
-}
-
-function looksLikeRecruiterChat(conv) {
- const title = (conv.title || "").toLowerCase();
- const keywords = ["recrutador","recruiter","vaga","job","oportunidade","opportunity","entrevista","interview","empresa","company","contratação","hiring","processo seletivo","selection process","tech lead","engineer","developer","desenvolvedor","linkedin","headhunter"];
- return keywords.some(k => title.includes(k));
-}
-
-export function ImportChatGPTModal({ onClose, onImport, isMobile, isDemo }) {
- const [step, setStep] = useState("upload"); // upload | filter | processing | review | done
- const [allConvs, setAllConvs] = useState([]);
- const [projects, setProjects] = useState([]);
- const [selectedProject, setSelectedProject] = useState("__all__");
- const [months, setMonths] = useState(2);
- const [filtered, setFiltered] = useState([]);
- const [selected, setSelected] = useState({});
- const [processing, setProcessing] = useState({ current: 0, total: 0, label: "" });
- const [extracted, setExtracted] = useState([]);
- const [approved, setApproved] = useState({});
- const [error, setError] = useState("");
- const fileRef = useRef();
-
- const handleFile = async (file) => {
- setError("");
- try {
- const convs = await loadConversationsFromFile(file);
- if (!convs.length) throw new Error("Nenhuma conversa encontrada no arquivo.");
- setAllConvs(convs);
- setProjects(getProjects(convs));
- applyFilter(convs, "__all__", months);
- setStep("filter");
- } catch(e) { setError(e.message); }
- };
-
- const applyFilter = (convs, projectId, m) => {
- let result = filterRecentConversations(convs, m);
- if (projectId && projectId !== "__all__") result = filterByProject(result, projectId);
- const sel = {};
- result.forEach(c => { sel[c.id] = looksLikeRecruiterChat(c); });
- setFiltered(result);
- setSelected(sel);
- };
-
- const onProjectChange = (pid) => {
- setSelectedProject(pid);
- applyFilter(allConvs, pid, months);
- };
-
- const onMonthsChange = (m) => {
- setMonths(m);
- applyFilter(allConvs, selectedProject, m);
- };
-
- const selectedCount = Object.values(selected).filter(Boolean).length;
-
- const processSelected = async () => {
- const toProcess = filtered.filter(c => selected[c.id]);
- if (!toProcess.length) return;
- setStep("processing");
- setProcessing({ current: 0, total: toProcess.length, label: "" });
-
- const results = [];
- const { data: { session: s } } = await supabase.auth.getSession();
- const sys = `Você é um especialista em análise de conversas de recrutamento. Analise a conversa e retorne APENAS JSON válido, sem markdown nem explicações.`;
-
- for (let i = 0; i < toProcess.length; i++) {
- const conv = toProcess[i];
- setProcessing({ current: i + 1, total: toProcess.length, label: conv.title || `Conversa ${i+1}` });
- const text = conversationText(conv);
- if (!text.trim()) continue;
-
- const prompt = `Analise esta conversa e determine se é sobre um processo seletivo/recrutamento.
-
-CONVERSA:
-${text.slice(0, 4000)}
-
-Se NÃO for sobre recrutamento, retorne: {"is_recruitment": false}
-Se FOR sobre recrutamento, retorne este JSON:
-{
- "is_recruitment": true,
- "company": "nome da empresa (ou null)",
- "role": "cargo/vaga (ou null)",
- "recruiter": "nome do recrutador (ou null)",
- "recruiterEmail": "email do recrutador (ou null)",
- "stage": "contacted|screening|interview|technical|offer|rejected|archived",
- "origin": "inbound|outbound",
- "location": "cidade/remoto (ou null)",
- "salary": "faixa salarial mencionada (ou null)",
- "contactedDate": "YYYY-MM-DD da data de contato (ou null)",
- "notes": "resumo em 2-3 linhas do que aconteceu nesta conversa",
- "tags": ["array de tags relevantes ex: react, remoto, fintech"]
-}`;
-
- try {
- const reply = await callAI([{role:"user",content:prompt}], sys, s?.access_token);
- const clean = reply.replace(/```json\n?|\n?```/g,"").trim();
- const parsed = JSON.parse(clean);
- if (parsed.is_recruitment) {
- results.push({
- ...parsed,
- id: crypto.randomUUID(),
- convTitle: conv.title || `Conversa ${i+1}`,
- convDate: new Date((conv.update_time || conv.create_time || 0) * 1000).toISOString().split("T")[0],
- });
- }
- } catch { /* skip malformed */ }
- }
-
- setExtracted(results);
- const approvedMap = {};
- results.forEach(r => { approvedMap[r.id] = true; });
- setApproved(approvedMap);
- setStep("review");
- };
-
- const doImport = async () => {
- const toImport = extracted.filter(r => approved[r.id]);
- const now = new Date().toISOString().split("T")[0];
- const newProcesses = toImport.map(r => ({
- id: r.id,
- company: r.company || "Empresa não identificada",
- role: r.role || "Cargo não identificado",
- stage: Object.keys(STAGE).includes(r.stage) ? r.stage : "contacted",
- location: r.location || "",
- salary: r.salary || "",
- recruiter: r.recruiter || "",
- recruiterEmail: r.recruiterEmail || "",
- origin: r.origin === "outbound" ? "outbound" : "inbound",
- contactedDate: r.contactedDate || r.convDate || now,
- nextStepDate: null,
- nextStepNote: "",
- jobUrl: "",
- tags: Array.isArray(r.tags) ? r.tags : [],
- notes: r.notes || "",
- steps: r.contactedDate ? [{ date: r.contactedDate || r.convDate || now, type: "contacted", note: "Importado do ChatGPT" }] : [],
- aiContext: "",
- starred: false,
- }));
- await onImport(newProcesses);
- setStep("done");
- };
-
- const approvedCount = Object.values(approved).filter(Boolean).length;
-
- return (
-
-
- {/* Header */}
-
- {isMobile &&
}
-
-
-
Importar do ChatGPT
-
- {step==="upload" && "Selecione o arquivo exportado do ChatGPT"}
- {step==="filter" && `${allConvs.length} conversas encontradas`}
- {step==="processing" && `Analisando ${processing.current} de ${processing.total}...`}
- {step==="review" && `${extracted.length} processo${extracted.length!==1?"s":""} detectado${extracted.length!==1?"s":""}`}
- {step==="done" && "Importação concluída"}
-
-
-
-
-
- {/* Steps indicator */}
-
- {["upload","filter","processing","review"].map((s,i)=>(
-
i ? "var(--acc)" : "var(--border)", transition:"background 0.3s" }}/>
- ))}
-
-
- {/* Body */}
-
-
- {/* Upload */}
- {step==="upload" && (
-
-
fileRef.current?.click()}
- onDragOver={e=>e.preventDefault()}
- onDrop={e=>{ e.preventDefault(); const f=e.dataTransfer.files[0]; if(f) handleFile(f); }}
- style={{ border:"2px dashed var(--border-md)", borderRadius:14, padding:"40px 24px", textAlign:"center", cursor:"pointer", transition:"all 0.15s", background:"var(--bg-o)" }}
- onMouseEnter={e=>{e.currentTarget.style.borderColor="var(--acc-b)";e.currentTarget.style.background="var(--acc-d)"}}
- onMouseLeave={e=>{e.currentTarget.style.borderColor="var(--border-md)";e.currentTarget.style.background="var(--bg-o)"}}
- >
-
📁
-
Arraste o arquivo aqui ou clique para selecionar
-
Aceita o arquivo .zip exportado do ChatGPT ou conversations.json extraído
-
{ if(e.target.files[0]) handleFile(e.target.files[0]); }}/>
-
- {error &&
{error}
}
-
-
Como exportar do ChatGPT
- {["1. Abra o ChatGPT → clique no seu avatar (canto superior direito)", "2. Vá em Settings → Data Controls", "3. Clique em Export data → Confirm export", "4. Aguarde o email com o link de download (pode levar alguns minutos)", "5. Baixe o .zip e faça o upload aqui"].map((t,i)=>(
-
- {i+1}.{t.slice(3)}
-
- ))}
-
-
- )}
-
- {/* Filter */}
- {step==="filter" && (
-
-
-
-
Período
-
-
- {projects.length > 0 && (
-
-
Projeto ChatGPT
-
-
- )}
-
-
- {selectedCount} de {filtered.length} conversas selecionadas para análise
- {selectedCount > 0 && · Conversas com aparência de recrutamento foram pré-selecionadas}
-
-
- {filtered.length === 0 ? (
-
Nenhuma conversa no período selecionado
- ) : filtered.map(conv=>{
- const date = new Date((conv.update_time||conv.create_time||0)*1000).toLocaleDateString("pt-BR",{day:"2-digit",month:"short"});
- const isRecruiter = looksLikeRecruiterChat(conv);
- return (
-
- );
- })}
-
-
-
-
-
-
- )}
-
- {/* Processing */}
- {step==="processing" && (
-
-
{[0,1,2].map(i=>)}
-
-
Analisando com IA...
-
{processing.label}
-
{processing.current} de {processing.total} conversas
-
-
-
- )}
-
- {/* Review */}
- {step==="review" && (
-
- {extracted.length === 0 ? (
-
-
🔍
-
Nenhum processo detectado
-
As conversas analisadas não pareceram ser sobre processos seletivos. Tente selecionar mais conversas.
-
- ) : extracted.map(r=>(
-
- ))}
-
- )}
-
- {/* Done */}
- {step==="done" && (
-
-
✅
-
Importação concluída!
-
{approvedCount} processo{approvedCount!==1?"s foram":"foi"} adicionado{approvedCount!==1?"s":""} ao seu pipeline.
-
Ver pipeline
-
- )}
-
-
- {/* Footer */}
- {(step==="filter" || step==="review") && (
-
- step==="review"?setStep("filter"):setStep("upload")} size="sm">
- {step==="filter" && (
-
- Analisar {selectedCount} conversa{selectedCount!==1?"s":""}
-
- )}
- {step==="review" && (
-
- {isDemo ? "Indisponível no modo demo" : `Importar ${approvedCount} processo${approvedCount!==1?"s":""}`}
-
- )}
-
- )}
-
-
- );
-}
-
-export default ImportChatGPTModal;
diff --git a/src/components/modals/ImportModal.jsx b/src/components/modals/ImportModal.jsx
deleted file mode 100644
index 6f1d802..0000000
--- a/src/components/modals/ImportModal.jsx
+++ /dev/null
@@ -1,460 +0,0 @@
-import { useState, useRef } from "react";
-import JSZip from "jszip";
-import pdfWorkerUrl from "pdfjs-dist/build/pdf.worker.mjs?url";
-import { T } from "../../constants/index.js";
-import { callAI } from "../../lib/ai.js";
-import { supabase } from "../../supabase.js";
-import { isChatGPTFormat, isICCFormat, looksLikeRecruitment, parseCSV, normalizeProcess } from "../../utils/importHelpers.js";
-import Ic from "../ui/Ic.jsx";
-import Btn from "../ui/Btn.jsx";
-import Badge from "../ui/Badge.jsx";
-
-// ── ChatGPT conversation text extractor ───────────────────────────────────────
-function extractConvText(conv) {
- const msgs = [];
- Object.values(conv.mapping || {}).forEach(node => {
- const msg = node?.message;
- if (!msg) return;
- const role = msg.author?.role;
- if (role !== "user" && role !== "assistant") return;
- const text = (msg.content?.parts || []).filter(p => typeof p === "string").join("\n").trim();
- if (text) msgs.push({ role, content: text, time: msg.create_time || 0 });
- });
- return msgs.sort((a, b) => a.time - b.time)
- .map(m => `[${m.role === "user" ? "Eu" : "IA"}]: ${m.content}`)
- .join("\n\n");
-}
-
-async function extractPdfText(file) {
- let pdfjsLib;
- try {
- pdfjsLib = await import("pdfjs-dist");
- pdfjsLib.GlobalWorkerOptions.workerSrc = pdfWorkerUrl;
- } catch { throw new Error("Falha ao carregar leitor de PDF."); }
- const buffer = await file.arrayBuffer();
- const pdf = await pdfjsLib.getDocument({ data: buffer }).promise;
- let text = "";
- for (let i = 1; i <= Math.min(pdf.numPages, 10); i++) {
- const page = await pdf.getPage(i);
- const content = await page.getTextContent();
- text += content.items.map(item => item.str).join(" ") + "\n";
- }
- return text;
-}
-
-const AI_SYS = `Você analisa textos e extrai informações sobre processos seletivos. Retorne APENAS JSON válido, sem markdown.`;
-
-function buildExtractPrompt(text) {
- return `Analise o texto e extraia TODOS os processos seletivos mencionados. Retorne array JSON:
-[{
- "company": "nome da empresa",
- "role": "cargo",
- "stage": "contacted|screening|interview|technical|offer|rejected|archived",
- "origin": "inbound|outbound",
- "location": "cidade ou remoto",
- "salary": "faixa salarial ou null",
- "recruiter": "nome do recrutador ou null",
- "recruiterEmail": "email ou null",
- "contactedDate": "YYYY-MM-DD ou null",
- "notes": "resumo em 2-3 linhas",
- "tags": []
-}]
-Se não houver processos, retorne [].
-
-TEXTO:
-${text.slice(0, 6000)}`;
-}
-
-// ── Component ──────────────────────────────────────────────────────────────────
-export function ImportModal({ onClose, onImport, isMobile, isDemo }) {
- const [tab, setTab] = useState("file");
- const [step, setStep] = useState("upload");
- const [pasteText, setPasteText] = useState("");
- const [months, setMonths] = useState(2);
- const [allConvs, setAllConvs] = useState([]);
- const [selectedConvs, setSelectedConvs] = useState({});
- const [processing, setProcessing] = useState({ current: 0, total: 0, label: "" });
- const [extracted, setExtracted] = useState([]);
- const [approved, setApproved] = useState({});
- const [error, setError] = useState("");
- const [sourceType, setSourceType] = useState(null);
- const fileRef = useRef();
-
- const now = new Date().toISOString().split("T")[0];
-
- const setReview = (results) => {
- setExtracted(results);
- const ap = {};
- results.forEach(r => { ap[r.id] = true; });
- setApproved(ap);
- setStep("review");
- };
-
- const handleFile = async (file) => {
- setError("");
- try {
- const name = file.name.toLowerCase();
-
- if (name.endsWith(".zip")) {
- const zip = await JSZip.loadAsync(file);
- const jsonFile = zip.file("conversations.json");
- if (!jsonFile) throw new Error("conversations.json não encontrado no ZIP.");
- loadChatGPT(JSON.parse(await jsonFile.async("string")));
- return;
- }
-
- if (name.endsWith(".json")) {
- const data = JSON.parse(await file.text());
- if (isChatGPTFormat(data)) { loadChatGPT(data); return; }
- if (isICCFormat(data)) {
- setSourceType("icc-json");
- setReview((Array.isArray(data) ? data : []).map(normalizeProcess));
- return;
- }
- throw new Error("JSON não reconhecido. Use o formato ICC (array de processos com campo 'company') ou export do ChatGPT.");
- }
-
- if (name.endsWith(".csv")) {
- const rows = parseCSV(await file.text());
- if (!rows.length) throw new Error("Nenhuma linha válida encontrada no CSV.");
- setSourceType("csv");
- setReview(rows.map(r => normalizeProcess({ ...r, id: crypto.randomUUID() })));
- return;
- }
-
- if (name.endsWith(".pdf")) {
- const text = await extractPdfText(file);
- if (!text.trim()) throw new Error("Não foi possível extrair texto do PDF.");
- await extractFromText(text);
- return;
- }
-
- throw new Error("Formato não suportado. Use .json, .zip, .csv ou .pdf");
- } catch (e) { setError(e.message); }
- };
-
- const loadChatGPT = (convs) => {
- setSourceType("chatgpt");
- setAllConvs(convs);
- const cutoff = Date.now() / 1000 - months * 30 * 24 * 3600;
- const recent = convs.filter(c => (c.update_time || c.create_time || 0) >= cutoff);
- const sel = {};
- recent.forEach(c => { sel[c.id] = looksLikeRecruitment(c); });
- setSelectedConvs(sel);
- setStep("filter");
- };
-
- const extractFromText = async (text) => {
- setSourceType("text");
- setStep("processing");
- setProcessing({ current: 0, total: 1, label: "Analisando com IA..." });
- try {
- const { data: { session: s } } = await supabase.auth.getSession();
- const reply = await callAI([{ role: "user", content: buildExtractPrompt(text) }], AI_SYS, s?.access_token);
- const parsed = JSON.parse(reply.replace(/```json\n?|\n?```/g, "").trim());
- setReview((Array.isArray(parsed) ? parsed : []).map(r => normalizeProcess({ ...r, id: crypto.randomUUID() })));
- } catch (e) {
- setError("Erro ao analisar o texto. Tente novamente.");
- setStep("upload");
- }
- };
-
- const processChatGPT = async () => {
- const cutoff = Date.now() / 1000 - months * 30 * 24 * 3600;
- const filtered = allConvs.filter(c => (c.update_time || c.create_time || 0) >= cutoff);
- const toProcess = filtered.filter(c => selectedConvs[c.id]);
- if (!toProcess.length) return;
- setStep("processing");
- const results = [];
- const { data: { session: s } } = await supabase.auth.getSession();
- const sys = `Você analisa conversas de recrutamento. Retorne APENAS JSON válido, sem markdown.`;
- for (let i = 0; i < toProcess.length; i++) {
- const conv = toProcess[i];
- setProcessing({ current: i + 1, total: toProcess.length, label: conv.title || `Conversa ${i + 1}` });
- const text = extractConvText(conv);
- if (!text.trim()) continue;
- try {
- const prompt = `Se NÃO for recrutamento: {"is_recruitment": false}
-Se FOR: {"is_recruitment": true, "company":"...","role":"...","recruiter":"...","recruiterEmail":"...","stage":"contacted|screening|interview|technical|offer|rejected|archived","origin":"inbound|outbound","location":"...","salary":"...","contactedDate":"YYYY-MM-DD","notes":"resumo 2-3 linhas","tags":[]}
-
-CONVERSA:\n${text.slice(0, 4000)}`;
- const reply = await callAI([{ role: "user", content: prompt }], sys, s?.access_token);
- const parsed = JSON.parse(reply.replace(/```json\n?|\n?```/g, "").trim());
- if (parsed.is_recruitment) {
- results.push(normalizeProcess({
- ...parsed,
- id: crypto.randomUUID(),
- convTitle: conv.title,
- contactedDate: parsed.contactedDate || new Date((conv.update_time || conv.create_time || 0) * 1000).toISOString().split("T")[0],
- }));
- }
- } catch { /* skip */ }
- }
- setReview(results);
- };
-
- const doImport = async () => {
- const toImport = extracted.filter(r => approved[r.id]);
- const newProcesses = toImport.map(r => ({
- id: r.id,
- company: r.company,
- role: r.role,
- stage: r.stage,
- location: r.location,
- salary: r.salary,
- recruiter: r.recruiter,
- recruiterEmail: r.recruiterEmail,
- origin: r.origin,
- contactedDate: r.contactedDate,
- nextStepDate: null,
- nextStepNote: "",
- jobUrl: "",
- tags: r.tags,
- notes: r.notes,
- steps: [{ date: r.contactedDate, type: "contacted", note: "Importado" }],
- aiContext: "",
- starred: false,
- channel: "",
- }));
- await onImport(newProcesses);
- setStep("done");
- };
-
- const approvedCount = Object.values(approved).filter(Boolean).length;
- const filteredConvs = allConvs.filter(c => (c.update_time || c.create_time || 0) >= Date.now() / 1000 - months * 30 * 24 * 3600);
- const selectedConvCount = Object.values(selectedConvs).filter(Boolean).length;
-
- const stepIndex = ["upload", "filter", "processing", "review", "done"].indexOf(step);
-
- return (
-
-
-
- {/* Header */}
-
- {isMobile &&
}
-
-
-
-
-
Importar processos
-
- {step==="upload" && "JSON · CSV · PDF · Colar texto"}
- {step==="filter" && `${allConvs.length} conversas encontradas`}
- {step==="processing" && `Analisando ${processing.current} de ${processing.total}...`}
- {step==="review" && `${extracted.length} processo${extracted.length!==1?"s":""} detectado${extracted.length!==1?"s":""}`}
- {step==="done" && "Importação concluída"}
-
-
-
-
-
- {/* Progress bar */}
-
- {[0,1,2,3].map(i => (
-
i ? "var(--acc)" : "var(--border)", transition:"background 0.3s" }}/>
- ))}
-
-
- {/* Body */}
-
-
- {/* ── Upload ── */}
- {step==="upload" && (
-
- {/* Tabs */}
-
- {[{id:"file",label:"Arquivo"},{id:"text",label:"Colar texto"}].map(t => (
-
- ))}
-
-
- {tab==="file" && (
- <>
-
fileRef.current?.click()}
- onDragOver={e=>e.preventDefault()}
- onDrop={e=>{ e.preventDefault(); const f=e.dataTransfer.files[0]; if(f) handleFile(f); }}
- style={{ border:"2px dashed var(--border-md)", borderRadius:14, padding:"36px 24px", textAlign:"center", cursor:"pointer", background:"var(--bg-o)", transition:"all 0.15s" }}
- onMouseEnter={e=>{e.currentTarget.style.borderColor="var(--acc-b)";e.currentTarget.style.background="var(--acc-d)"}}
- onMouseLeave={e=>{e.currentTarget.style.borderColor="var(--border-md)";e.currentTarget.style.background="var(--bg-o)"}}
- >
-
📂
-
Arraste ou clique para selecionar
-
- JSON · CSV · PDF · ZIP
-
-
{ if(e.target.files[0]) handleFile(e.target.files[0]); }}/>
-
-
-
Formatos aceitos
- {[
- {fmt:"JSON",desc:'Array de processos com campo "company" (formato ICC) ou export do ChatGPT'},
- {fmt:"CSV",desc:"Colunas: company, role, stage, location, salary, recruiter, notes, tags"},
- {fmt:"PDF",desc:"Conversa ou proposta — IA extrai os dados automaticamente"},
- {fmt:"ZIP",desc:"Arquivo de export completo do ChatGPT (contém conversations.json)"},
- ].map(({fmt,desc})=>(
-
- {fmt}
- {desc}
-
- ))}
-
-
- )}
-
- {tab==="text" && (
- <>
-
Cole a conversa ou descreva o processo
-