import streamlit as st import os import subprocess from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer import black from pylint import lint from io import StringIO import sys PROJECT_ROOT = "projects" AGENT_DIRECTORY = "agents" if "chat_history" not in st.session_state: st.session_state.chat_history = [] if "terminal_history" not in st.session_state: st.session_state.terminal_history = [] if "workspace_projects" not in st.session_state: st.session_state.workspace_projects = {} if "available_agents" not in st.session_state: st.session_state.available_agents = [] class AIAgent: def __init__(self, name, description, skills): self.name = name self.description = description self.skills = skills def create_agent_prompt(self): skills_str = '\n'.join([f"* {skill}" for skill in self.skills]) agent_prompt = f""" As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas: {skills_str} I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter. """ return agent_prompt def autonomous_build(self, chat_history, workspace_projects): """ Autonomous build logic that continues based on the state of chat history and workspace projects. """ # Example logic: Generate a summary of chat history and workspace state summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history]) summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()]) # Example: Generate the next logical step in the project next_step = "Based on the current state, the next logical step is to implement the main application logic." return summary, next_step def save_agent_to_file(agent): """Saves the agent's prompt to a file.""" if not os.path.exists(AGENT_DIRECTORY): os.makedirs(AGENT_DIRECTORY) file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt") with open(file_path, "w") as file: file.write(agent.create_agent_prompt()) st.session_state.available_agents.append(agent.name) def load_agent_prompt(agent_name): """Loads an agent prompt from a file.""" file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt") if os.path.exists(file_path): with open(file_path, "r") as file: agent_prompt = file.read() return agent_prompt else: return None def create_agent_from_text(name, text): skills = text.split('\n') agent = AIAgent(name, "AI agent created from text input.", skills) save_agent_to_file(agent) return agent.create_agent_prompt() def chat_interface_with_agent(input_text, agent_name): agent_prompt = load_agent_prompt(agent_name) if agent_prompt is None: return f"Agent {agent_name} not found." model_name = "gpt2" try: model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) generator = pipeline("text-generation", model=model, tokenizer=tokenizer) except EnvironmentError as e: return f"Error loading model: {e}" # Combine the agent prompt with user input combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:" # Truncate input text to avoid exceeding the model's maximum length max_input_length = 900 input_ids = tokenizer.encode(combined_input, return_tensors="pt") if input_ids.shape[1] > max_input_length: input_ids = input_ids[:, :max_input_length] # Generate chatbot response outputs = model.generate( input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response def terminal_interface(command, project_name=None): if project_name: project_path = os.path.join(PROJECT_ROOT, project_name) result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_path) else: result = subprocess.run(command, shell=True, capture_output=True, text=True) return result.stdout def code_editor_interface(code): formatted_code = black.format_str(code, mode=black.FileMode()) pylint_output = lint.Run([formatted_code], do_exit=False) pylint_output_str = StringIO() pylint_output.linter.reporter.write_messages(pylint_output_str) return formatted_code, pylint_output_str.getvalue() def summarize_text(text): summarizer = pipeline("summarization") summary = summarizer(text, max_length=130, min_length=30, do_sample=False) return summary[0]['summary_text'] def sentiment_analysis(text): analyzer = pipeline("sentiment-analysis") result = analyzer(text) return result[0]['label'] def translate_code(code, source_language, target_language): # Placeholder for translation logic return f"Translated {source_language} code to {target_language}." def generate_code(idea): # Placeholder for code generation logic return f"Generated code based on the idea: {idea}." def workspace_interface(project_name): project_path = os.path.join(PROJECT_ROOT, project_name) if not os.path.exists(project_path): os.makedirs(project_path) st.session_state.workspace_projects[project_name] = {'files': []} return f"Project '{project_name}' created successfully." def add_code_to_workspace(project_name, code, file_name): project_path = os.path.join(PROJECT_ROOT, project_name) if not os.path.exists(project_path): return f"Project '{project_name}' does not exist." file_path = os.path.join(project_path, file_name) with open(file_path, "w") as file: file.write(code) st.session_state.workspace_projects[project_name]['files'].append(file_name) return f"Code added to '{file_name}' in project '{project_name}'." def chat_interface(input_text): # Placeholder for chat interface logic return f"Chatbot response: {input_text}" st.title("AI Agent Creator") sidebar = st.sidebar sidebar.title("Navigation") app_mode = sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"]) if app_mode == "AI Agent Creator": st.header("Create an AI Agent from Text") subheader = st.subheader agent_name = subheader("Enter agent name:") text_input = subheader("Enter skills (one per line):") if st.button("Create Agent"): agent_prompt = create_agent_from_text(agent_name, text_input)