update

test/runtests.jl

@@ -0,0 +1,108 @@
+
+using JSON, Dates, UUIDs, PrettyPrinting, Base64, NATS, HTTP
+using GeneralUtils, msghandler
+
+config = JSON.parsefile("./appconfig.json")
+
+agent_conn  = NATS.connect(config["nats_server_info"]["url"])
+
+function text2text_instruct_llm(openai_msg::AbstractDict)
+  payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
+  _, msg_envelope_json_str = msghandler.smartpack(
+        config["externalService"]["servicesloadbalancer"]["nats"], 
+        payloads;   
+        msg_purpose="text2text",  
+        broker_url=config["nats_server_info"]["url"],
+        fileserver_url=config["externalService"]["fileserver"]["url"])
+
+  reply = NATS.request(agent_conn, 
+                      config["externalService"]["servicesloadbalancer"]["nats"], 
+                      msg_envelope_json_str, timeout=120)
+
+  incoming_env_json_str = String(reply.payload)
+  incoming_env = msghandler.smartunpack(incoming_env_json_str)
+  _llm_response = incoming_env["payloads"][1][2]
+  llm_response = _llm_response["choices"][1]["message"]["content"]
+  return llm_response
+end
+
+
+
+
+# 1. Read local file and encode to base64 string
+image1_path = "test/large_image.png"
+image1_bytes = read(image1_path)
+image1_base64_string = base64encode(image1_bytes)
+
+# 2. Match the MIME type according to your file extension (e.g., png, jpeg)
+mime_type = "image/png" 
+data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"
+
+# 3. Construct payload with the Data URI
+openai_msg = Dict(
+  "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
+  "messages" => [
+    Dict(
+      "role" => "user",
+      "content" => [
+        Dict("type" => "text", "text" => "Do you know this wine? Just give me brief intro."),
+        Dict(
+          "type" => "image_url",
+          "image_url" => Dict("url" => data1_uri)
+        )
+      ]
+    )
+  ],
+  "temperature" => 0.7
+)
+
+llm_response = text2text_instruct_llm(openai_msg)
+
+
+
+# 1. Read local file and encode to base64 string
+image2_path = "test/small_image.png"
+image2_bytes = read(image2_path)
+image2_base64_string = base64encode(image2_bytes)
+
+# 2. Match the MIME type according to your file extension (e.g., png, jpeg)
+mime_type = "image/png" 
+data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
+
+
+openai_msg = Dict(
+  "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
+  "messages" => [
+    Dict(
+      "role" => "user",
+      "content" => [
+        Dict("type" => "text", "text" => "Do you know this wine? Just give me brief intro."),
+        Dict(
+          "type" => "image_url",
+          "image_url" => Dict("url" => data1_uri)
+        )
+      ]
+    ),
+    Dict(
+      "role" => "assistant",
+      "content" => [
+        Dict("type" => "text", "text" => "Yes, I do!\n\nThis is **Asolo Bella Principessa**, a high-quality Italian sparkling wine made from the Prosecco region.\n\n### 🥂 Brief Intro\n\n*   **What it is:** A Prosecco Superiore D.O.C.G., meaning it meets strict quality standards for a premium sparkling wine.\n*   **Style:** It is a **Sparkling White Wine** and is designated as **Extra Dry**. This means it is crisp, refreshing, and has a dry finish (not overly sweet).\n*   **Flavor Profile:** Expect bright, lively bubbles, often with notes of green apple, pear, and citrus.\n*   **Best For:** It's a versatile wine, perfect for celebratory toasts, enjoying with appetizers (like seafood or charcuterie), or simply as a refreshing aperitivo."),
+      ]
+    ),
+    Dict(
+      "role" => "user",
+      "content" => [
+        Dict("type" => "text", "text" => "How does this wine differ from earlier wine?"),
+        Dict(
+          "type" => "image_url",
+          "image_url" => Dict("url" => data2_uri)
+        )
+      ]
+    ),
+  ],
+  "temperature" => 0.7
+)
+
+llm_response = text2text_instruct_llm(openai_msg)
+
+