feat: improve upload validation and error handling

src/app/api/analyze/route.ts

@@ -1,5 +1,6 @@
 import { NextRequest } from 'next/server';
 import { streamAnalysis } from '@/lib/vertex';
+import '@/lib/env-validation';
 import type { DataProfile } from '@/types';
 
 export const runtime = 'nodejs';
@@ -23,43 +24,38 @@ export async function POST(request: NextRequest) {
     }
 
     let resultText = '';
-    for await (const chunk of streamAnalysis(
-      question,
-      profile,
-      dataSample,
-      history
-    )) {
-      resultText += chunk;
-    }
+    try {
+      for await (const chunk of streamAnalysis(
+        question,
+        profile,
+        dataSample,
+        history
+      )) {
+        resultText += chunk;
+      }
 
-    resultText = resultText.trim();
+      resultText = resultText.trim();
 
-    // Strip markdown code fences if AI added them
-    if (resultText.startsWith('```')) {
-      resultText = resultText
-        .replace(/^```(?:json)?\s*/i, '')
-        .replace(/```\s*$/, '')
-        .trim();
-    }
+      // Strip markdown code fences if AI added them
+      if (resultText.startsWith('```')) {
+        resultText = resultText
+          .replace(/^```(?:json)?\s*/i, '')
+          .replace(/```\s*$/, '')
+          .trim();
+      }
 
-    if (!resultText) {
-      return new Response(JSON.stringify({ error: 'Empty response from AI' }), {
-        status: 500,
-      });
-    }
+      if (!resultText) {
+        throw new Error('Empty response from AI');
+      }
 
-    // Validate JSON before sending
-    try {
+      // Validate JSON before sending
       JSON.parse(resultText);
-    } catch {
+    } catch (e) {
+      const errorMsg = e instanceof Error ? e.message : String(e);
+      console.error('[AI Data Lens] Pipeline error:', errorMsg);
       return new Response(
-        JSON.stringify({
-          chartConfig: null,
-          findings: resultText.substring(0, 500) || 'No response from AI',
-          limitations: 'The AI response could not be parsed as JSON.',
-          stats: {},
-        }),
-        { headers: { 'Content-Type': 'application/json' } }
+        JSON.stringify({ error: `Analysis failed: unable to interpret dataset. Details: ${errorMsg}` }),
+        { status: 500, headers: { 'Content-Type': 'application/json' } }
       );
     }
 
@@ -72,6 +68,6 @@ export async function POST(request: NextRequest) {
   } catch (error) {
     const msg = error instanceof Error ? error.message : String(error);
     console.error('Analysis error:', msg);
-    return new Response(JSON.stringify({ error: msg }), { status: 500 });
+    return new Response(JSON.stringify({ error: `Analysis failed: ${msg}` }), { status: 500 });
   }
 }

src/app/api/upload/route.ts

@@ -1,5 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 import { parseFile, getDataSample } from '@/lib/data-parser';
+import '@/lib/env-validation';
 
 export const runtime = 'nodejs';
 export const maxDuration = 30;
@@ -12,9 +13,9 @@ export async function POST(request: NextRequest) {
       return NextResponse.json({ error: 'No file provided' }, { status: 400 });
 
     const ext = file.name.split('.').pop()?.toLowerCase();
-    if (!['csv', 'json', 'xlsx', 'xls'].includes(ext || '')) {
+    if (!['csv', 'json', 'xlsx'].includes(ext || '')) {
       return NextResponse.json(
-        { error: 'Unsupported file type. Use CSV, JSON, or Excel.' },
+        { error: 'Unsupported file type. Only CSV, JSON, and XLSX files are allowed.' },
         { status: 400 }
       );
     }

src/app/layout.tsx

@@ -1,5 +1,6 @@
 import type { Metadata } from 'next';
 import { Inter, JetBrains_Mono } from 'next/font/google';
+import '@/lib/env-validation';
 import './globals.css';
 
 const inter = Inter({
@@ -14,7 +15,7 @@ const mono = JetBrains_Mono({
 });
 
 export const metadata: Metadata = {
-  title: 'DataLensAI - See your data. Ask anything.',
+  title: 'AI Data Lens - See your data. Ask anything.',
   description: 'Drop in a CSV. Ask a business question. Get the full analysis.',
 };
 

src/app/page.tsx

@@ -4,11 +4,11 @@ import { Hero } from '@/components/Hero';
 import { AppShell } from '@/components/AppShell';
 
 export default function Home() {
-  const data = useSessionStore((s) => s.data);
+  const currentSession = useSessionStore((s) => s.currentSession);
 
   return (
     <div className="h-screen flex flex-col bg-black">
-      {data.length === 0 ? <Hero /> : <AppShell />}
+      {!currentSession ? <Hero /> : <AppShell />}
     </div>
   );
 }

src/components/AppShell.tsx

@@ -27,7 +27,18 @@ export function AppShell() {
     });
   }, [messages]);
 
-  if (!currentSession) return null;
+  if (!currentSession) {
+    return (
+      <div className="flex-1 flex flex-col items-center justify-center p-8 text-center text-muted-foreground">
+        <div className="max-w-md mx-auto space-y-4">
+          <p className="text-lg font-medium text-white">No dataset uploaded yet</p>
+          <p className="text-sm">
+            Please upload a dataset (.csv, .xlsx, or .json) on the home page to start analyzing your data with AI Data Lens.
+          </p>
+        </div>
+      </div>
+    );
+  }
 
   return (
     <>
@@ -50,7 +61,7 @@ export function AppShell() {
             </div>
             <div>
               <h1 className="text-sm font-semibold tracking-tight">
-                DataLensAI
+                AI Data Lens
               </h1>
               <p className="text-[10px] text-muted-foreground leading-none">
                 Conversational analytics

src/components/UploadZone.tsx

@@ -15,6 +15,11 @@ export function UploadZone() {
 
   const handleFile = useCallback(
     async (file: File) => {
+      const ext = file.name.split('.').pop()?.toLowerCase();
+      if (!ext || !['csv', 'json', 'xlsx'].includes(ext)) {
+        setError(`Unsupported file type: .${ext || ''}. Only .csv, .json, and .xlsx files are allowed.`);
+        return;
+      }
       setIsLoading(true);
       setError(null);
       try {
@@ -71,7 +76,7 @@ export function UploadZone() {
             <input
               ref={inputRef}
               type="file"
-              accept=".csv,.json,.xlsx,.xls"
+              accept=".csv,.json,.xlsx"
               className="hidden"
               onChange={(e) =>
                 e.target.files?.[0] && handleFile(e.target.files[0])

src/lib/data-parser.ts

@@ -6,11 +6,13 @@ export async function parseFile(
   file: File
 ): Promise<{ data: Record<string, unknown>[]; profile: DataProfile }> {
   const ext = file.name.split('.').pop()?.toLowerCase();
-  let data: Record<string, unknown>[] = []; // eslint-disable-line no-useless-assignment
+  if (!ext || !['csv', 'json', 'xlsx'].includes(ext)) {
+    throw new Error(`Unsupported file type: .${ext || ''}. Only .csv, .json, and .xlsx files are allowed.`);
+  }
+  let data: Record<string, unknown>[] = [];
   if (ext === 'csv') data = await parseCSV(file);
   else if (ext === 'json') data = await parseJSON(file);
-  else if (ext === 'xlsx' || ext === 'xls') data = await parseExcel(file);
-  else throw new Error(`Unsupported format: ${ext}`);
+  else if (ext === 'xlsx') data = await parseExcel(file);
   const profile = createProfile(data, file.name, file.size);
   return { data, profile };
 }

src/lib/env-validation.ts

@@ -0,0 +1,30 @@
+if (typeof window === 'undefined') {
+  const required = ['GCP_PROJECT_ID', 'GCP_LOCATION', 'GCP_JSON_BASE64'];
+  const missing: string[] = [];
+
+  for (const name of required) {
+    const val = process.env[name];
+    if (!val) {
+      missing.push(name);
+    } else if (name === 'GCP_JSON_BASE64') {
+      try {
+        const decoded = Buffer.from(val, 'base64').toString('utf-8');
+        const parsed = JSON.parse(decoded);
+        if (!parsed.client_email || !parsed.private_key) {
+          missing.push('GCP_JSON_BASE64 (missing client_email or private_key)');
+        }
+      } catch {
+        console.error(
+          '[env-validation] GCP_JSON_BASE64 is set but contains invalid or malformed base64/JSON'
+        );
+        missing.push('GCP_JSON_BASE64 (invalid/malformed format)');
+      }
+    }
+  }
+
+  if (missing.length > 0) {
+    console.error(
+      `[AI Data Lens] Environment variable validation failed. Missing or invalid variables: ${missing.join(', ')}`
+    );
+  }
+}

src/lib/hooks/useAnalyze.ts

@@ -33,11 +33,21 @@ export function useAnalyze() {
         }),
       });
       if (!res.ok) {
+        const errorText = await res.text();
+        let errorMessage = 'unable to interpret dataset';
+        try {
+          const errorObj = JSON.parse(errorText);
+          if (errorObj.error) {
+            errorMessage = errorObj.error.replace(/^Analysis failed:\s*/i, '');
+          }
+        } catch {
+          // ignore parsing error, fallback to default error message
+        }
         finishStreaming(
           id,
           errorResult(
-            'Analysis service unavailable.',
-            'Check API configuration.'
+            `Analysis failed: ${errorMessage}`,
+            'Please try again with a different question or verify the dataset structure.'
           )
         );
         return;
@@ -54,10 +64,11 @@ export function useAnalyze() {
           )
         );
       }
-    } catch {
+    } catch (err) {
+      const msg = err instanceof Error ? err.message : String(err);
       finishStreaming(
         id,
-        errorResult('Network error.', 'Check your connection and try again.')
+        errorResult('Network error.', `Details: ${msg}. Check your connection and try again.`)
       );
     }
   };

src/lib/vertex/intents/intro.ts

@@ -12,15 +12,15 @@ export const introIntent: IntentHandler = {
   handle() {
     const result: IntentResult = {
       chartType: 'bar',
-      title: 'DataLensAI - at a glance',
+      title: 'AI Data Lens - at a glance',
       data: [
         { name: 'CSV / JSON / Excel parsing', value: 100 },
         { name: 'Natural-language Q&A', value: 95 },
         { name: 'Auto chart selection', value: 90 },
         { name: 'Limitations surfaced', value: 100 },
       ],
       findings:
-        "I'm DataLensAI - your autonomous data analysis partner. Upload any data file and ask questions in plain English. I surface the right visualization, synthesize findings, and flag limitations honestly. No SQL, no Python - just answers.\n\n**Try asking me**:\n• Show me the top performers\n• Plot a trend over time\n• Break down by category",
+        "I'm AI Data Lens - your autonomous data analysis partner. Upload any data file and ask questions in plain English. I surface the right visualization, synthesize findings, and flag limitations honestly. No SQL, no Python - just answers.\n\n**Try asking me**:\n• Show me the top performers\n• Plot a trend over time\n• Break down by category",
     };
     return result;
   },

src/lib/vertex/prompt.ts

@@ -12,7 +12,7 @@ export function buildSystemPrompt(
     p?.columns?.map((c) => `${c.name} (${c.type})`).join(', ') || 'unknown';
   const sample = dataSample.slice(0, 3);
 
-  return `You are DataLensAI, a rigorous data analyst. Always respond with valid JSON only (no markdown, no prose outside the JSON).
+  return `You are AI Data Lens, a rigorous data analyst. Always respond with valid JSON only (no markdown, no prose outside the JSON).
 
 Schema (${p?.rowCount || '?'} rows): ${schema}