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123 | using System.Text.RegularExpressions;
using LLama.Batched;
using LLama.Common;
using Spectre.Console;
namespace LLama.Examples.Examples
{
// This example shows how to chat with LLaVA model with both image and text as input.
// It uses the interactive executor to inference.
public class LlavaInteractiveModeExecute
{
public static async Task Run()
{
string multiModalProj = UserSettings.GetMMProjPath();
string modelPath = UserSettings.GetModelPath();
string modelImage = UserSettings.GetImagePath();
const int maxTokens = 1024;
var prompt = $"{{{modelImage}}}\nUSER:\nProvide a full description of the image.\nASSISTANT:\n";
var parameters = new ModelParams(modelPath)
{
ContextSize = 4096,
Seed = 1337,
};
using var model = LLamaWeights.LoadFromFile(parameters);
using var context = model.CreateContext(parameters);
// Llava Init
using var clipModel = LLavaWeights.LoadFromFile(multiModalProj);
var ex = new InteractiveExecutor(context, clipModel );
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine("The executor has been enabled. In this example, the prompt is printed, the maximum tokens is set to {0} and the context size is {1}.", maxTokens, parameters.ContextSize );
Console.WriteLine("To send an image, enter its filename in curly braces, like this {c:/image.jpg}.");
var inferenceParams = new InferenceParams() { Temperature = 0.1f, AntiPrompts = new List<string> { "\nUSER:" }, MaxTokens = maxTokens };
do
{
// Evaluate if we have images
//
var imageMatches = Regex.Matches(prompt, "{([^}]*)}").Select(m => m.Value);
var imageCount = imageMatches.Count();
var hasImages = imageCount > 0;
byte[][] imageBytes = null;
if (hasImages)
{
var imagePathsWithCurlyBraces = Regex.Matches(prompt, "{([^}]*)}").Select(m => m.Value);
var imagePaths = Regex.Matches(prompt, "{([^}]*)}").Select(m => m.Groups[1].Value);
try
{
imageBytes = imagePaths.Select(File.ReadAllBytes).ToArray();
}
catch (IOException exception)
{
Console.ForegroundColor = ConsoleColor.Red;
Console.Write(
$"Could not load your {(imageCount == 1 ? "image" : "images")}:");
Console.Write($"{exception.Message}");
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine("Please try again.");
break;
}
int index = 0;
foreach (var path in imagePathsWithCurlyBraces)
{
// First image replace to tag <image, the rest of the images delete the tag
if (index++ == 0)
prompt = prompt.Replace(path, "<image>");
else
prompt = prompt.Replace(path, "");
}
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine($"Here are the images, that are sent to the chat model in addition to your message.");
Console.WriteLine();
foreach (var consoleImage in imageBytes?.Select(bytes => new CanvasImage(bytes)))
{
consoleImage.MaxWidth = 50;
AnsiConsole.Write(consoleImage);
}
Console.WriteLine();
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine($"The images were scaled down for the console only, the model gets full versions.");
Console.WriteLine($"Write /exit or press Ctrl+c to return to main menu.");
Console.WriteLine();
// Initilize Images in executor
//
ex.ImagePaths = imagePaths.ToList();
}
Console.ForegroundColor = Color.White;
await foreach (var text in ex.InferAsync(prompt, inferenceParams))
{
Console.Write(text);
}
Console.Write(" ");
Console.ForegroundColor = ConsoleColor.Green;
prompt = Console.ReadLine();
Console.WriteLine();
// let the user finish with exit
//
if (prompt.Equals("/exit", StringComparison.OrdinalIgnoreCase))
break;
}
while(true);
}
}
}
|