fix: VLM识别修复 - 去System Message + 低温 + 精简提示词

backend/src/Health.Infrastructure/AI/ai_clients.cs

@@ -104,27 +104,23 @@ public sealed class VisionClient(HttpClient http, IConfiguration config)
         int maxTokens = 2048,
         CancellationToken ct = default)
     {
-        var messages = new List<ChatMessage>();
-        if (!string.IsNullOrEmpty(systemPrompt))
-            messages.Add(new ChatMessage { Role = "system", Content = systemPrompt });
-
         var contentParts = new List<object>();
         foreach (var url in imageUrls)
             contentParts.Add(new { type = "image_url", image_url = new { url } });
+        if (!string.IsNullOrEmpty(systemPrompt))
+            contentParts.Add(new { type = "text", text = systemPrompt });
         if (!string.IsNullOrEmpty(userText))
             contentParts.Add(new { type = "text", text = userText });
 
-        var userMessage = new ChatMessage
+        var messages = new List<ChatMessage>
         {
-            Role = "user",
-            Content = JsonSerializer.Serialize(contentParts, _jsonOptions)
+            new() { Role = "user", Content = JsonSerializer.Serialize(contentParts, _jsonOptions) }
         };
-        messages.Add(userMessage);
 
         var request = new ChatCompletionRequest
         {
             Model = _model, Messages = messages, MaxTokens = maxTokens, Stream = false,
-            Temperature = 0.7f, TopP = 0.8f,
+            Temperature = 0.1f, VlHighResolutionImages = true,
         };
 
         var json = JsonSerializer.Serialize(request, _jsonOptions);
@@ -136,7 +132,6 @@ public sealed class VisionClient(HttpClient http, IConfiguration config)
             throw new HttpRequestException($"VLM API {response.StatusCode}: {errorBody}");
         }
         var body = await response.Content.ReadAsStringAsync(ct);
-        Console.WriteLine($"[VLM RAW] Model={_model}, BodyLen={body.Length}, Body={body[..Math.Min(body.Length, 500)]}");
         return JsonSerializer.Deserialize<ChatCompletionResponse>(body, _jsonOptions)!;
     }
 }

backend/src/Health.Infrastructure/AI/open_ai_compatible_client.cs

@@ -167,6 +167,8 @@ public sealed class ChatCompletionRequest
     public float? TopP { get; set; }
     public List<ToolDefinition>? Tools { get; set; }
     public string? ToolChoice { get; set; }
+    [System.Text.Json.Serialization.JsonPropertyName("vl_high_resolution_images")]
+    public bool VlHighResolutionImages { get; set; }
 }
 
 public sealed class ChatMessage

backend/src/Health.WebApi/Endpoints/ai_chat_endpoints.cs

@@ -266,24 +266,26 @@ public static class AiChatEndpoints
                     await file.CopyToAsync(stream, ct);
 
                 var compressedPath = Path.Combine(uploadsDir, $"compressed_{safeName}");
-                CompressImage(filePath, compressedPath, maxWidth: 1280, quality: 85L);
+                CompressImage(filePath, compressedPath, maxWidth: 1024, quality: 90L);
                 var compressedBytes = await File.ReadAllBytesAsync(compressedPath, ct);
                 var base64 = Convert.ToBase64String(compressedBytes);
                 imageUrls.Add($"data:image/jpeg;base64,{base64}");
             }
 
             var prompt = """
-    Describe everything you see in this image in detail. What objects are present? What are their colors, shapes, labels?
-    If there are any food items, drinks, or beverages, identify them specifically by name.
-    Then, for each food/drink item, estimate the portion size and calories (kcal).
-    Return ONLY a JSON array: [{"name":"item name","portion":"estimated portion","calories":estimated_calories}]
-    If you cannot identify something, say so in the name field.
+    识别这张图片中最主要的食物或饮品（最多2个）。只返回JSON数组，格式：
+    [{"name":"名称","portion":"份量","calories":热量}]
+    不要说图片里没有的东西。不要编造。
     """;
 
             try
             {
                 var response = await visionClient.VisionAsync(prompt, imageUrls, userText: "请看图识别食物", maxTokens: 8192, ct: ct);
                 var result = response.Choices?.FirstOrDefault()?.Message?.Content ?? "{}";
+                // 记录VLM原始返回用于排查
+                var uploadsDir2 = Path.Combine(Directory.GetCurrentDirectory(), "uploads");
+                var logPath = Path.Combine(uploadsDir2, $"vlm_log_{DateTime.Now:HHmmss}.txt");
+                await File.WriteAllTextAsync(logPath, $"MODEL: {Environment.GetEnvironmentVariable("VLM_MODEL")}\nIMAGE_SIZE: {imageUrls.FirstOrDefault()?.Length ?? 0}\nRESPONSE:\n{result}", ct);
                 return Results.Ok(new { code = 0, data = result, message = (string?)null });
             }
             catch (Exception ex)

health_app/lib/pages/diet/diet_capture_page.dart

@@ -73,7 +73,9 @@ class DietNotifier extends Notifier<DietState> {
     state = state.copyWith(isAnalyzing: true, errorMessage: null);
     try {
       final api = ref.read(apiClientProvider);
-      final imageFile = File(state.imagePath!);
+      final path = state.imagePath!;
+      debugPrint('[DietDebug] analyzeImage using: $path');
+      final imageFile = File(path);
 
       final formData = FormData.fromMap({
         'images': await MultipartFile.fromFile(