feat: ✨ Working tool calls

o
	new file:   llm_interface.py

llm_interface.py

@@ -0,0 +1,73 @@
+import config
+from tools import ToolExecutor
+import requests
+from rich import print
+from rich.prompt import Prompt
+
+#TODO: context squish when it gets big
+
+class llm():
+    def __init__(self, logger) -> None:
+        self.logger = logger
+        self.first_prompt = 1
+        self.context = {
+            "history": [],
+            "summary": None,
+            "metadata": {
+                "created_at": None,
+                "token_budget": 8000,
+            }
+        }
+
+    def summarise_context(self):
+        # add ai summary & created at time
+        pass
+
+    def add_to_history(self, history_object):
+        self.context['history'].append(history_object)
+        self.logger.debug(self.context['history'])
+
+    def ask_model(self, prompt: str, url=config.LM_STUDIO_URL) -> str:
+        # prompt = config.PRE_PROMPT + prompt
+        payload = {
+        "model": 'qwen/qwen3-coder-30b',
+        "messages": [],
+        "temperature": 0.7,
+        "max_tokens": 2048,
+        }
+        self.add_to_history({"role": "user", "content": prompt})
+
+        payload["messages"] = [
+                {"role": "system", "content": config.SYSTEM_MESSAGE}
+            ] + self.context['history']
+        self.logger.debug(f'json payload: {payload}')
+        resp = requests.post(url, json=payload)
+        resp.raise_for_status()
+        self.logger.debug(resp.json())
+        self.add_to_history(resp.json()["choices"][0]["message"])
+        return resp.json()["choices"][0]["message"]["content"].strip()
+    
+    def tool_response(self, prompt: str, url=config.LM_STUDIO_URL) -> str:
+        payload = {
+        "model": 'qwen/qwen3-coder-30b',
+        "messages": [],
+        "temperature": 0.7,
+        "max_tokens": 2048,
+        }
+        payload["messages"] =  [{"role": "tool", "content": prompt}]
+        resp = requests.post(url, json=payload)
+        resp.raise_for_status()
+        self.logger.debug(resp.json())
+        return resp.json()["choices"][0]["message"]["content"].strip()
+
+    def get_llm_prompt(self):
+        while True:
+            llm_prompt = Prompt.ask("[bold cyan]Ask your local llm[/]")
+            self.logger.debug(f'prompt is: {llm_prompt}')
+            if llm_prompt == '':
+                self.logger.error("Cannot be empty!")
+            return llm_prompt
+
+    def handle_llm_reply(self, llm_reply:str):
+        architect = ToolExecutor(self.logger)
+        return architect.process_llm_response(llm_output=llm_reply)