fix text editor tool and scroll action in computer tool

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "stream/thirdparty/llama.cpp"]
-	path = stream/thirdparty/llama.cpp
-	url = https://github.com/ggml-org/llama.cpp

README.md

@@ -0,0 +1 @@
+# computeruse

computeruse/README.md

@@ -1,2 +0,0 @@
-# computeruse
-This is a prototype.

stream/.gitignore

@@ -1,4 +0,0 @@
-.cache
-stream_app
-*.gguf
-compile_commands.json

stream/Makefile

@@ -1,11 +0,0 @@
-CC = gcc
-CFLAGS = -Wall -g -I./thirdparty/llama.cpp/include -I./thirdparty/llama.cpp/ggml/include
-TARGET = stream_app
-SRC = main.c
-LIBS = -L./thirdparty/llama.cpp/build/bin -lllama -lggml -lggml-base
-
-$(TARGET): $(SRC)
-	$(CC) $(CFLAGS) -o $(TARGET) $(SRC) $(LIBS)
-
-clean:
-	rm -f *.o $(TARGET)

stream/README.md

@@ -1,38 +0,0 @@
-# flowy.stream
-
-This is a proof of concept of a showing widgets within a conversation based on intent.
-
-## Todo
-- [x] Integrate llama.cpp with local inference. This will set us up for building many parts of experience.
-- [ ] Disect what llama is doing and what the Phi model is doing.
-- [ ] Create conversational loop with chat and running context.
-- [ ] Generate & render different types of blocks: list, email, doc, etc.
-- [ ] Try different models with hugging face
-- [ ] Render TUI elements based on commands
-
-## Dependencies
-
-### llama.cpp
-```sh
-#model weights
-wget https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/main/Phi-3-mini-4k-instruct-q4.gguf
-```
-
-```sh
-cd ./thirdparty/llama.cpp
-
-rm -rf build
-mkdir -p build
-cmake -S. -Bbuild -DLLAMA_CURL=OFF
-cmake --build build
-```
-
-The libraries should be in `./thirdparty/llama.cpp/build/bin`.
-
-NOTE: you may have to disable curl as a flag when configuring llama.cpp via cmake.
-
-## Getting started
-```sh
-make
-LD_LIBRARY_PATH=./thirdparty/llama.cpp/build/bin/ ./stream_app
-```

stream/main.c

@@ -1,176 +0,0 @@
-#include "thirdparty/llama.cpp/ggml/include/ggml-backend.h"
-#include <llama.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-typedef enum {
-  Unspecified,
-  Question,
-  Math,
-  Code,
-  Email,
-  Doc,
-  WebSearch,
-} IntentType;
-
-int main(int argc, char *argv[]) {
-  printf("Hello world\n");
-
-  char user_input[500];
-  printf("What's up, what can I help with?\n");
-  fgets(user_input, sizeof(user_input), stdin);
-
-  char prompt[1000];
-  snprintf(prompt, sizeof(prompt), "<|system|>You are a helpful assistant. Your goal is to take in what the user is doing and return 3 predictive actions/3 suggestions based on what the user is trying to do: e.g. change page title when in google sheets, or calculate sum, create chart.<|end|>\n<|user|>%s<|end|>\n<|assistant|>", user_input);
-
-  // number of layers to offload to the GPU
-  int ngl = 99;
-  // number of tokens to predict
-  int n_predict = 1000;
-
-  // load dynamic backends
-
-  ggml_backend_load_all();
-
-  // initialize the model
-
-  struct llama_model_params model_params = llama_model_default_params();
-  model_params.n_gpu_layers = ngl;
-
-  struct llama_model *model = llama_model_load_from_file(
-      "Phi-3-mini-4k-instruct-q4.gguf", model_params);
-
-  if (model == NULL) {
-    fprintf(stderr, "%s: error: unable to load model\n", __func__);
-    return 1;
-  }
-
-  const struct llama_vocab *vocab = llama_model_get_vocab(model);
-  // tokenize the prompt
-
-  // find the number of tokens in the prompt
-  const int n_prompt =
-      -llama_tokenize(vocab, prompt, strlen(prompt), NULL, 0, true, true);
-
-  // allocate space for the tokens and tokenize the prompt
-  llama_token *prompt_tokens = malloc(n_prompt * sizeof(llama_token));
-  if (prompt_tokens == NULL) {
-    fprintf(stderr, "%s: error: failed to allocate memory for prompt tokens\n",
-            __func__);
-    return 1;
-  }
-  if (llama_tokenize(vocab, prompt, strlen(prompt), prompt_tokens, n_prompt,
-                     true, true) < 0) {
-    fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
-    return 1;
-  }
-
-  // initialize the context
-
-  struct llama_context_params ctx_params = llama_context_default_params();
-  // n_ctx is the context size
-  ctx_params.n_ctx = n_prompt + n_predict - 1;
-  // n_batch is the maximum number of tokens that can be processed in a single
-  // call to llama_decode
-  ctx_params.n_batch = n_prompt;
-  // enable performance counters
-  ctx_params.no_perf = false;
-
-  struct llama_context *ctx = llama_init_from_model(model, ctx_params);
-
-  if (ctx == NULL) {
-    fprintf(stderr, "%s: error: failed to create the llama_context\n",
-            __func__);
-    return 1;
-  }
-
-  // initialize the sampler
-
-  struct llama_sampler_chain_params sparams =
-      llama_sampler_chain_default_params();
-  sparams.no_perf = false;
-  struct llama_sampler *smpl = llama_sampler_chain_init(sparams);
-
-  llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
-
-  // print the prompt token-by-token
-
-  for (int i = 0; i < n_prompt; i++) {
-    char buf[128];
-    int n = llama_token_to_piece(vocab, prompt_tokens[i], buf, sizeof(buf), 0,
-                                 true);
-    if (n < 0) {
-      fprintf(stderr, "%s: error: failed to convert token to piece\n",
-              __func__);
-      return 1;
-    }
-    buf[n] = '\0';
-    printf("%s", buf);
-  }
-
-  // prepare a batch for the prompt
-
-  llama_batch batch = llama_batch_get_one(prompt_tokens, n_prompt);
-
-  // main loop
-
-  const int64_t t_main_start = ggml_time_us();
-  int n_decode = 0;
-  llama_token new_token_id;
-
-  for (int n_pos = 0; n_pos + batch.n_tokens < n_prompt + n_predict;) {
-    // evaluate the current batch with the transformer model
-    if (llama_decode(ctx, batch)) {
-      fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
-      return 1;
-    }
-
-    n_pos += batch.n_tokens;
-
-    // sample the next token
-    {
-      new_token_id = llama_sampler_sample(smpl, ctx, -1);
-
-      // is it an end of generation?
-      if (llama_vocab_is_eog(vocab, new_token_id)) {
-        break;
-      }
-
-      char buf[128];
-      int n =
-          llama_token_to_piece(vocab, new_token_id, buf, sizeof(buf), 0, true);
-      if (n < 0) {
-        fprintf(stderr, "%s: error: failed to convert token to piece\n",
-                __func__);
-        return 1;
-      }
-      buf[n] = '\0';
-      printf("%s", buf);
-      fflush(stdout);
-
-      // prepare the next batch with the sampled token
-      batch = llama_batch_get_one(&new_token_id, 1);
-
-      n_decode += 1;
-    }
-  }
-
-  printf("\n");
-
-  const int64_t t_main_end = ggml_time_us();
-
-  fprintf(stderr, "%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
-          __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f,
-          n_decode / ((t_main_end - t_main_start) / 1000000.0f));
-
-  fprintf(stderr, "\n");
-  llama_perf_sampler_print(smpl);
-  llama_perf_context_print(ctx);
-  fprintf(stderr, "\n");
-
-  llama_sampler_free(smpl);
-  llama_free(ctx);
-  llama_model_free(model);
-  free(prompt_tokens);
-}