add dbiz, add history

supabase/functions/writing-check-dbiz/index.ts

@@ -0,0 +1,92 @@
+// Supabase Edge Function: writing-check-dbiz
+// Uses DBIZ LLM API (qwen-35b, OpenAI-compatible) to analyze English writing.
+// Deploy: supabase functions deploy writing-check-dbiz --no-verify-jwt
+// Secrets: supabase secrets set DBIZ_API_KEY=<your_key>
+
+import OpenAI from "npm:openai@^4"
+
+const dbiz = new OpenAI({
+  apiKey: Deno.env.get("DBIZ_API_KEY") ?? "",
+  baseURL: "https://ai-api.dbiz.com/v1",
+})
+
+const CORS_HEADERS = {
+  "Access-Control-Allow-Origin": "*",
+  "Access-Control-Allow-Headers": "authorization, x-client-info, apikey, content-type",
+}
+
+// Structure enforced via system prompt — qwen-35b via LiteLLM drops response_format params.
+const SYSTEM_PROMPT = `You are an expert English writing teacher specialising in TOEIC and IELTS assessment.
+Analyse the student's writing and respond ONLY with valid JSON — no markdown, no extra text:
+{
+  "score": "<estimated band score, e.g. 6.5>",
+  "grammar": ["<issue 1 with correction, mix English example + Vietnamese explanation>"],
+  "vocabulary": ["<vocabulary observation in Vietnamese>"],
+  "structure": "<2–3 sentence structure assessment in Vietnamese>",
+  "improved_version": "<the full improved text in English>",
+  "summary": "<2–3 sentence overall assessment in Vietnamese>"
+}
+All string values must use double quotes. Do not use single quotes or unquoted keys.`
+
+Deno.serve(async (req: Request) => {
+  if (req.method === "OPTIONS") {
+    return new Response("ok", { headers: CORS_HEADERS })
+  }
+
+  try {
+    const { content } = await req.json() as { content: string }
+
+    if (!content || content.trim().length < 10) {
+      return new Response(
+        JSON.stringify({ error: "Bài viết quá ngắn. Vui lòng nhập ít nhất 10 ký tự." }),
+        { status: 400, headers: { ...CORS_HEADERS, "Content-Type": "application/json" } },
+      )
+    }
+
+    const stream = await dbiz.chat.completions.create({
+      model: "qwen-35b",
+      messages: [
+        { role: "system", content: SYSTEM_PROMPT },
+        { role: "user", content: `Analyse this writing:\n\n${content.slice(0, 2000)}` },
+      ],
+      temperature: 0.3,
+      max_tokens: 10000,
+      stream: true,
+    })
+
+    const encoder = new TextEncoder()
+    const body = new ReadableStream({
+      async start(controller) {
+        try {
+          for await (const chunk of stream) {
+            const text = chunk.choices[0]?.delta?.content ?? ""
+            if (text) {
+              controller.enqueue(encoder.encode(`data: ${JSON.stringify({ text })}\n\n`))
+            }
+          }
+        } catch (err) {
+          console.error("writing-check-dbiz stream error:", err)
+          controller.enqueue(
+            encoder.encode(`data: ${JSON.stringify({ error: "Đã có lỗi khi chấm bài. Vui lòng thử lại." })}\n\n`),
+          )
+        }
+        controller.enqueue(encoder.encode("data: [DONE]\n\n"))
+        controller.close()
+      },
+    })
+
+    return new Response(body, {
+      headers: {
+        ...CORS_HEADERS,
+        "Content-Type": "text/event-stream",
+        "Cache-Control": "no-cache",
+      },
+    })
+  } catch (err) {
+    console.error("writing-check-dbiz error:", err)
+    return new Response(
+      JSON.stringify({ error: "Đã có lỗi khi chấm bài. Vui lòng thử lại." }),
+      { status: 500, headers: { ...CORS_HEADERS, "Content-Type": "application/json" } },
+    )
+  }
+})

supabase/functions/writing-check/index.ts

@@ -1,6 +1,6 @@
 // Supabase Edge Function: writing-check
 // Uses GLM API (OpenAI-compatible) to analyze English writing submissions.
-// Deploy: supabase functions deploy writing-check
+// Deploy: supabase functions deploy writing-check --no-verify-jwt
 // Secrets: supabase secrets set GLM_API_KEY=<your_key>
 
 import OpenAI from "npm:openai@^4"
@@ -13,10 +13,8 @@ const glm = new OpenAI({
 const CORS_HEADERS = {
   "Access-Control-Allow-Origin": "*",
   "Access-Control-Allow-Headers": "authorization, x-client-info, apikey, content-type",
-  "Content-Type": "application/json",
 }
 
-// Instructs the model to return a strict JSON structure with Vietnamese feedback.
 const SYSTEM_PROMPT = `You are an expert English writing teacher specialising in TOEIC and IELTS assessment.
 Analyse the student's writing and respond ONLY with valid JSON — no markdown, no extra text:
 {
@@ -29,7 +27,6 @@ Analyse the student's writing and respond ONLY with valid JSON — no markdown,
 }`
 
 Deno.serve(async (req: Request) => {
-  // Handle CORS pre-flight
   if (req.method === "OPTIONS") {
     return new Response("ok", { headers: CORS_HEADERS })
   }
@@ -40,13 +37,11 @@ Deno.serve(async (req: Request) => {
     if (!content || content.trim().length < 10) {
       return new Response(
         JSON.stringify({ error: "Bài viết quá ngắn. Vui lòng nhập ít nhất 10 ký tự." }),
-        { status: 400, headers: CORS_HEADERS },
+        { status: 400, headers: { ...CORS_HEADERS, "Content-Type": "application/json" } },
       )
     }
 
-    const completion = await glm.chat.completions.create({
-      // GLM-4-32B-0414-128K: cheapest paid model at $0.1/$0.1 per 1M tokens.
-      // Override via: supabase secrets set GLM_MODEL=<other-model>
+    const stream = await glm.chat.completions.create({
       model: Deno.env.get("GLM_MODEL") ?? "GLM-4.5-Flash",
       messages: [
         { role: "system", content: SYSTEM_PROMPT },
@@ -54,20 +49,42 @@ Deno.serve(async (req: Request) => {
       ],
       temperature: 0.3,
       max_tokens: 1500,
+      stream: true,
     })
 
-    const raw = completion.choices[0]?.message?.content ?? "{}"
+    const encoder = new TextEncoder()
+    const body = new ReadableStream({
+      async start(controller) {
+        try {
+          for await (const chunk of stream) {
+            const text = chunk.choices[0]?.delta?.content ?? ""
+            if (text) {
+              controller.enqueue(encoder.encode(`data: ${JSON.stringify({ text })}\n\n`))
+            }
+          }
+        } catch (err) {
+          console.error("writing-check stream error:", err)
+          controller.enqueue(
+            encoder.encode(`data: ${JSON.stringify({ error: "Đã có lỗi khi chấm bài. Vui lòng thử lại." })}\n\n`),
+          )
+        }
+        controller.enqueue(encoder.encode("data: [DONE]\n\n"))
+        controller.close()
+      },
+    })
 
-    // Strip markdown code fences if the model adds them despite instructions
-    const cleaned = raw.replace(/^```(?:json)?\s*/i, "").replace(/\s*```$/, "").trim()
-    const feedback = JSON.parse(cleaned)
-
-    return new Response(JSON.stringify(feedback), { headers: CORS_HEADERS })
+    return new Response(body, {
+      headers: {
+        ...CORS_HEADERS,
+        "Content-Type": "text/event-stream",
+        "Cache-Control": "no-cache",
+      },
+    })
   } catch (err) {
     console.error("writing-check error:", err)
     return new Response(
       JSON.stringify({ error: "Đã có lỗi khi chấm bài. Vui lòng thử lại." }),
-      { status: 500, headers: CORS_HEADERS },
+      { status: 500, headers: { ...CORS_HEADERS, "Content-Type": "application/json" } },
     )
   }
 })