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agent.py

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+from agentllm import AgGPT
+import os
+import json
+
+class Agent:
+
+    def __init__(self, mission):
+        self.mission = mission
+        self.llm = AgGPT("agent.gguf")
+        self.mission_name = self.llm.ask("Based on this mission, create a one-word name for it. the mission is " + mission + " send a single word to identify the mission. send one word only.")
+        self.folder = f"agent/{self.mission_name}"
+        print(self.folder)
+        if not os.path.exists(self.folder):
+            os.makedirs(self.folder)
+
+    def create_file(self, name, content):
+        with open(f"{self.folder}/{name}", "w") as file:
+            file.write(content)
+
+    def summarize_file(self, path):
+        with open(path, "r") as file:
+            content = file.read()
+        summary = self.llm.ask("Summarize this text: " + content + " in 5 bullet points. send only the summary. ")
+        return summary
+    
+    def get_full_project_summary(self):
+        files = [f for f in os.listdir(self.folder) if os.path.isfile(os.path.join(self.folder, f))]
+        summary = ""
+        for i, file in enumerate(files):
+            with open(os.path.join(self.folder, file), "r") as f:
+                content = f.read().strip()
+            if i > 0:
+                summary += "\n"
+            summary += f"{file} - {content}"
+        return summary
+
+    def deploy_agent(self):
+        system = f'''
+        You are an AI agent that can write files to the computer.
+        The files you have created so far are:
+          {self.get_full_project_summary()}
+
+        The project is called {self.mission_name} and the folder is called {self.folder}.
+        You must work on the README.md file and all other project files, you can only respond in this format:
+        {{
+          "file_name": "line1\\nline2\\nline3"
+        }}
+        if the file already exists it will be overwritten, for example if you want to overwrite the file README.md with the content "Hello World" you will respond with:
+        {{
+           "README.md": "Hello World"
+        }}
+        You will only respond with the JSON object, nothing else.
+        to create new files you will use the same format, for example if you want to create a new file called "test.txt" with the content "Hello World" you will respond with:
+           {{
+               "test.txt": "Hello World"
+           }}
+        You will only respond with the JSON object, nothing else.
+        The mission objective is: {self.mission}
+continue working on the project. you may send more than one file name and content per response as follows:
+    {{
+  "README.md": "This is the README\\nIt has multiple lines\\nPretty cool, right?",
+  "main.py": "print('Hello, world!')\\ndef main():\\n    return 42"
+}}
+continue the project.
+
+Do not assume any module or library, you must create everything with the respective programming language.
+create in the readme all the files you will create, and then create the files that you havent created yet. make sure the project is fully functional.
+Make sure to write fucntional code and no placeholders, keep iterating.
+        '''
+        system += "\nRespond ONLY with the JSON. No explanation, no commentary, just raw JSON."
+
+        print(system)
+
+        response = self.llm.ask(system)
+        print(response)
+        try:
+            files_to_write = json.loads(response.strip())
+        except json.JSONDecodeError:
+            print("Error decoding JSON response. Please check the format.")
+        for file_name, content in files_to_write.items():
+            file_path = os.path.join(self.folder, file_name)
+            with open(file_path, 'w', encoding='utf-8') as f:
+                f.write(content)
+
+        self.deploy_agent()
+
+if __name__ == "__main__":
+    mission = input("Enter your mission: ")
+    agent = Agent(mission)
+    agent.deploy_agent()

agentllm.py

@@ -0,0 +1,34 @@
+from llama_cpp import Llama
+
+class AgGPT:
+    def __init__(self, model_path):
+        self.model_path = model_path
+        self.model = Llama(model_path=model_path, n_ctx=4048, n_gpu_layers=35)
+
+    def run(self):
+        while True:
+            prompt = input("\nEnter your prompt: ")
+
+            messages = [
+                {"role": "system", "content": f"You are AgGPT-10, an AGI system. The user said: '{prompt}'."},
+                {"role": "user", "content": prompt}
+            ]
+
+            output = self.model.create_chat_completion(messages, max_tokens=2050, temperature=0.7)
+            print(output["choices"][0]["message"]["content"])
+
+    def ask(self, question):
+        ''' Ask AgGPT-10 a question and return the answer. '''
+        messages = [
+            {"role": "system", "content": f"You are AgGPT-10, an AGI system. The user said: '{question}'."},
+            {"role": "user", "content": question}
+        ]
+
+        output = self.model.create_chat_completion(messages, max_tokens=2050, temperature=0.7)
+
+        return output["choices"][0]["message"]["content"]
+
+if __name__ == "__main__":
+    model_path = "agent.gguf"
+    aggpt = AgGPT(model_path)
+    aggpt.run()