import os import subprocess import random from huggingface_hub import InferenceClient import gradio as gr from safe_search import safe_search from i_search import google from i_search import i_search as i_s from datetime import datetime import logging import json # --- Configuration --- MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1" # Model to use MAX_HISTORY_TURNS = 5 # Number of history turns to keep VERBOSE = True # Enable verbose logging # --- Logging Setup --- logging.basicConfig( filename="app.log", # Name of the log file level=logging.INFO, # Set the logging level (INFO, DEBUG, etc.) format="%(asctime)s - %(levelname)s - %(message)s", ) # --- Agent Definitions --- agents = { "WEB_DEV": { "description": "Specialized in web development tasks.", "system_prompt": "You are a helpful AI assistant specializing in web development. You can generate code, answer questions, and provide guidance on web technologies.", }, "AI_SYSTEM_PROMPT": { "description": "Focuses on generating system prompts for AI agents.", "system_prompt": "You are a helpful AI assistant that generates effective system prompts for AI agents. Your prompts should be clear, concise, and provide specific instructions.", }, "PYTHON_CODE_DEV": { "description": "Expert in Python code development.", "system_prompt": "You are a helpful AI assistant specializing in Python code development. You can generate Python code, debug code, and answer questions about Python.", }, "DATA_SCIENCE": { "description": "Expert in data science tasks.", "system_prompt": "You are a helpful AI assistant specializing in data science. You can analyze data, build models, and provide insights.", }, "GAME_DEV": { "description": "Expert in game development tasks.", "system_prompt": "You are a helpful AI assistant specializing in game development. You can generate game logic, design levels, and provide guidance on game engines.", }, # Add more agents as needed } # --- Function to format prompt with history --- def format_prompt(message, history, agent_name, system_prompt): prompt = " " for user_prompt, bot_response in history[-MAX_HISTORY_TURNS:]: prompt += f"[INST] {user_prompt} [/ " prompt += f" {bot_response}" prompt += f"[INST] {message} [/ " # Add system prompt if provided if system_prompt: prompt = f"{system_prompt}\n\n{prompt}" return prompt # --- Function to run the LLM with specified parameters --- def run_llm( prompt, stop_sequences, max_tokens, temperature=0.7, top_p=0.8, repetition_penalty=1.5, ): seed = random.randint(1, 1111111111111111) logging.info(f"Seed: {seed}") # Log the seed client = InferenceClient(MODEL_NAME) resp = client.text_generation( prompt, max_new_tokens=max_tokens, stop_sequences=stop_sequences, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, ) if VERBOSE: logging.info(f"Prompt: {prompt}") logging.info(f"Response: {resp}") return resp # --- Function to handle agent interactions --- def agent_interaction( purpose, message, agent_name, system_prompt, history, temperature, max_new_tokens, top_p, repetition_penalty, ): # Format the prompt with history prompt = format_prompt(message, history, agent_name, system_prompt) # Run the LLM response = run_llm( prompt, stop_sequences=["observation:", "task:", "action:", "thought:"], max_tokens=max_new_tokens, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, ) # Update history history.append((message, response)) return history, history # --- Function to parse actions from LLM response --- def parse_action(line): """Parse the action line to get the action name and input.""" parts = line.split(":", 1) if len(parts) == 2: action_name = parts[0].replace("action", "").strip() action_input = parts[1].strip() else: action_name = parts[0].replace("action", "").strip() action_input = "" return action_name, action_input # --- Function to execute actions based on agent's response --- def execute_action(purpose, task, history, action_name, action_input): logging.info(f"Executing Action: {action_name} - {action_input}") if action_name == "SEARCH": try: if "http" in action_input: if "<" in action_input: action_input = action_input.strip("<") if ">" in action_input: action_input = action_input.strip(">") response = i_s(action_input) logging.info(f"Search Result: {response}") history += "observation: search result is: {}\n".format(response) else: history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=https://URL'\n" except Exception as e: history += "observation: {}\n".format(e) return "MAIN", None, history, task elif action_name == "COMPLETE": task = "END" return "COMPLETE", "COMPLETE", history, task elif action_name == "GENERATE_CODE": # Simulate OpenAI API response for code generation (using Hugging Face model) # ... (Implement code generation logic using a suitable Hugging Face model) # Example: # code = generate_code_from_huggingface_model(action_input) # Replace with actual code generation function # history += f"observation: Here's the code: {code}\n" # return "MAIN", None, history, task pass # Placeholder for code generation logic elif action_name == "RUN_CODE": # Simulate OpenAI API response for code execution (using Hugging Face model) # ... (Implement code execution logic using a suitable Hugging Face model) # Example: # output = execute_code_from_huggingface_model(action_input) # Replace with actual code execution function # history += f"observation: Code output: {output}\n" # return "MAIN", None, history, task pass # Placeholder for code execution logic else: # Default action: "MAIN" return "MAIN", action_input, history, task # --- Function to handle the main loop of agent interaction --- def run_agent(purpose, history): task = None directory = "./" if history: history = str(history).strip("[]") if not history: history = "" action_name = "UPDATE-TASK" if task is None else "MAIN" action_input = None while True: logging.info(f"---") logging.info(f"Purpose: {purpose}") logging.info(f"Task: {task}") logging.info(f"---") logging.info(f"History: {history}") logging.info(f"---") # Get the agent's next action prompt = f""" You are a helpful AI assistant. You are working on the task: {task} Your current history is: {history} What is your next thought? thought: What is your next action? action: """ response = run_llm( prompt, stop_sequences=["observation:", "task:", "action:", "thought:"], max_tokens=32000, ) # Parse the action lines = response.strip().strip("\n").split("\n") for line in lines: if line.startswith("thought: "): history += "{}\n".format(line) logging.info(f"Thought: {line}") elif line.startswith("action: "): action_name, action_input = parse_action(line) logging.info(f"Action: {action_name} - {action_input}") history += "{}\n".format(line) break # Execute the action action_name, action_input, history, task = execute_action( purpose, task, history, action_name, action_input ) yield (history) if task == "END": return (history) # --- Gradio Interface --- def main(): with gr.Blocks() as demo: gr.Markdown("## FragMixt - No-Code Development Powerhouse") gr.Markdown("### Your AI-Powered Development Companion") # Chat Interface chatbot = gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel") # Input Components message = gr.Textbox(label="Enter your message", placeholder="Ask me anything!") purpose = gr.Textbox(label="Purpose", placeholder="What is the purpose of this interaction?") agent_name = gr.Dropdown(label="Agents", choices=list(agents.keys()), value=list(agents.keys())[0], interactive=True) system_prompt = gr.Textbox(label="System Prompt", max_lines=1, interactive=True) temperature = gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs") max_new_tokens = gr.Slider(label="Max new tokens", value=1048 * 10, minimum=0, maximum=1048 * 10, step=64, interactive=True, info="The maximum numbers of new tokens") top_p = gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens") repetition_penalty = gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens") # Button to submit the message submit_button = gr.Button(value="Send") # Project Explorer Tab (Placeholder) with gr.Tab("Project Explorer"): project_path = gr.Textbox(label="Project Path", placeholder="/home/user/app/current_project") explore_button = gr.Button(value="Explore") project_output = gr.Textbox(label="File Tree", lines=20) # Chat App Logic Tab with gr.Tab("Chat App"): history = gr.State([]) examples = [ ["What is the purpose of this AI agent?", "I am designed to assist with no-code development tasks."], ["Can you help me generate a Python function to calculate the factorial of a number?", "Sure! Here is a Python function to calculate the factorial of a number:"], ] def chat(purpose, message, agent_name, system_prompt, temperature, max_new_tokens, top_p, repetition_penalty, history): # Get the system prompt for the selected agent system_prompt = agents.get(agent_name, {}).get("system_prompt", "") # Run the agent interaction history, history_output = agent_interaction( purpose, message, agent_name, system_prompt, history, temperature, max_new_tokens, top_p, repetition_penalty, ) return history, history_output submit_button.click( chat, inputs=[ purpose, message, agent_name, system_prompt, temperature, max_new_tokens, top_p, repetition_penalty, history, ], outputs=[chatbot, history], ) demo.launch() if __name__ == "__main__": main()