# ---------------------------------------------- 100 --------------------------------------------- #


using JSON, Dates, UUIDs, PrettyPrinting, Base64, DataFrames, NATS, HTTP
using GeneralUtils, msghandler



# ------------------------------------------------------------------------------------------------ #
#                           llama.cpp chat request example (direct HTTP)                           #
# ------------------------------------------------------------------------------------------------ #

# 1. Read local file and encode to base64 string
image_path = "test/large_image.png"
image_bytes = read(image_path)
base64_string = base64encode(image_bytes)

# 2. Match the MIME type according to your file extension (e.g., png, jpeg)
mime_type = "image/png" 
data_uri = "data:$(mime_type);base64,$(base64_string)"


headers = [
    "Content-Type" => "application/json",
    "Authorization" => "Bearer no-key"
]

# 3. Construct payload with the Data URI
messages = Dict(
    "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
    "messages" => [
        Dict(
            "role" => "user",
            "content" => [
                Dict("type" => "text", "text" => "Describe this image for me."),
                Dict(
                    "type" => "image_url",
                    "image_url" => Dict("url" => data_uri)
                )
            ]
        )
    ],
    "temperature" => 0.7
)
url = "http://192.168.1.8:9001/v1/chat/completions"
response = HTTP.post(url, headers, JSON.json(messages))
r = JSON.parse(response.body)










# ------------------------------------------------------------------------------------------------ #
#                      Llama.cpp Embedding multiple text example (direct HTTP)                     #
# ------------------------------------------------------------------------------------------------ #
using HTTP
using JSON

url = "http://192.168.88.50:9010/v1/embeddings"
headers = [
    "Content-Type" => "application/json",
    "Authorization" => "Bearer no-key"
]

# Pass an array of strings instead of a single string
documents = [
    "System architecture specification for heart monitor logic.",
    "NATS broker cluster configuration options for low-latency.",
    "Julia signal processing worker initialization sequence."
]

body_data = Dict(
    "model" => "embeddinggemma-300M-BF16",
    "input" => documents
)

try
    response = HTTP.post(url, headers, JSON.json(body_data))
    parsed = JSON.parse(String(response.body))
    
    # Extract multiple vectors (ordered identically to your input array)
    for (i, item) in enumerate(parsed["data"])
        idx = item["index"] + 1 # API indexes are 0-based; Julia is 1-based
        vector = item["embedding"]
        println("Document $idx unified vector size: ", length(vector))
    end

catch e
    println("Batch embedding failed: ", e)
end


# ------------------------------------------------------------------------------------------------ #
#                                    test openai_message (NATS)                                    #
# ------------------------------------------------------------------------------------------------ #
# ------------------------------------------------------------------------------------------------ #
#                         Llama.cpp Embedding multiple text with NATS                              #
# ------------------------------------------------------------------------------------------------ #
using JSON, Dates, UUIDs, PrettyPrinting, Base64, DataFrames, NATS, HTTP
using GeneralUtils, msghandler

const config = JSON.parsefile("./appconfig.json")

documents = [
  "System architecture specification for heart monitor logic.",
  # "NATS broker cluster configuration options for low-latency.",
  # "Julia signal processing worker initialization sequence."
]

documents_dict = Dict("documents"=> documents)

payloads = [("documents", documents_dict, "dictionary")]
message_envelope, forward_msg_envelope_json_str = msghandler.smartpack(
      config["this_service_input_channel"]["nats"], 
      payloads;   
      msg_purpose="embedding",  
      broker_url=config["nats_server_info"]["url"],
      fileserver_url=config["externalService"]["fileserver"]["url"])

connection  = NATS.connect(config["nats_server_info"]["url"])
reply = NATS.request(connection, 
                    config["this_service_input_channel"]["nats"], 
                    forward_msg_envelope_json_str, timeout=120)

incoming_env_json_str = String(reply.payload)
incoming_env = msghandler.smartunpack(incoming_env_json_str)
embedding_response = incoming_env["payloads"][1][2]

for (i, item) in enumerate(embedding_response["data"])
    vector = item["embedding"]
    println("Document $i embedding vector size: ", length(vector))
end


# ------------------------------------------------------------------------------------------------ #
#                                    test openai_message (NATS)                                    #
# ------------------------------------------------------------------------------------------------ #
using JSON, Dates, UUIDs, PrettyPrinting, Base64, DataFrames, NATS, HTTP
using GeneralUtils, msghandler

const config = JSON.parsefile("./appconfig.json")

# 1. Read local file and encode to base64 string
image_path = "test/large_image.png"
image_bytes = read(image_path)
base64_string = base64encode(image_bytes)

# 2. Match the MIME type according to your file extension (e.g., png, jpeg)
mime_type = "image/png" 
data_uri = "data:$(mime_type);base64,$(base64_string)"


headers = [
    "Content-Type" => "application/json",
    "Authorization" => "Bearer no-key"
]

# 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" => "Describe this image for me in detail."),
        Dict(
          "type" => "image_url",
          "image_url" => Dict("url" => data_uri)
        )
      ]
    )
  ],
  "temperature" => 0.7
)

payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
message_envelope, forward_msg_envelope_json_str = msghandler.smartpack(
      config["this_service_input_channel"]["nats"], 
      payloads;   
      msg_purpose="text2text",  
      broker_url=config["nats_server_info"]["url"],
      fileserver_url=config["externalService"]["fileserver"]["url"])


connection  = NATS.connect(config["nats_server_info"]["url"])
reply = NATS.request(connection, 
                    config["this_service_input_channel"]["nats"], 
                    forward_msg_envelope_json_str, timeout=120)

incoming_env_json_str = String(reply.payload)
incoming_env = msghandler.smartunpack(incoming_env_json_str)
ai_response = incoming_env["payloads"][1][2]
content = ai_response["choices"][1]["message"]["content"]