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 openai import sys # Set your OpenAI API key here openai.api_key = "YOUR_OPENAI_API_KEY" PROJECT_ROOT = "projects" AGENT_DIRECTORY = "agents" # Global state to manage communication between Tool Box and Workspace Chat App 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. """ 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()]) 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}" combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:" input_ids = tokenizer.encode(combined_input, return_tensors="pt") max_input_length = 900 if input_ids.shape[1] > max_input_length: input_ids = input_ids[:, :max_input_length] outputs = model.generate( input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, pad_token_id=tokenizer.eos_token_id # Set pad_token_id to eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response def workspace_interface(project_name): if not os.path.exists(PROJECT_ROOT): os.makedirs(PROJECT_ROOT) 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." else: return f"Project {project_name} already exists." def add_code_to_workspace(project_name, code, file_name): project_path = os.path.join(PROJECT_ROOT, project_name) if os.path.exists(project_path): 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} successfully." else: return f"Project {project_name} does not exist." def terminal_interface(command, project_name=None): if project_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." result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True) else: result = subprocess.run(command, shell=True, capture_output=True, text=True) if result.returncode == 0: return result.stdout else: return result.stderr def code_editor_interface(code): try: formatted_code = black.format_str(code, mode=black.FileMode()) except black.NothingChanged: formatted_code = code result = StringIO() sys.stdout = result sys.stderr = result (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True) sys.stdout = sys.__stdout__ sys.stderr = sys.__stderr__ lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue() return formatted_code, lint_message def summarize_text(text): summarizer = pipeline("summarization") summary = summarizer(text, max_length=50, min_length=25, do_sample=False) return summary[0]['summary_text'] def sentiment_analysis(text): analyzer = pipeline("sentiment-analysis") sentiment = analyzer(text) return sentiment[0] def translate_code(code, source_language, target_language): prompt = f"Translate this code from {source_language} to {target_language}:\n\n{code}" response = openai.ChatCompletion.create( model="gpt-4", messages=[ {"role": "system", "content": "You are an expert software developer."}, {"role": "user", "content": prompt} ] ) return response.choices[0].message['content'].strip() def generate_code(code_idea): response = openai.ChatCompletion.create( model="gpt-4", messages=[ {"role": "system", "content": "You are an expert software developer."}, {"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"} ] ) return response.choices[0].message['content'].strip() st.title("AI Agent Creator") st.sidebar.title("Navigation") app_mode = st.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") st.subheader("From Text") agent_name = st.text_input("Enter agent name:") text_input = st.text_area("Enter skills (one per line):") if st.button("Create Agent"): agent_prompt = create_agent_from_text(agent_name, text_input) st.success(f"Agent '{agent_name}' created and saved successfully.") st.session_state.available_agents.append(agent_name) elif app_mode == "Tool Box": st.header("AI-Powered Tools") st.subheader("Chat with CodeCraft") chat_input = st.text_area("Enter your message:") if st.button("Send"): if chat_input.startswith("@"): agent_name = chat_input.split(" ")[0][1:] chat_input = " ".join(chat_input.split(" ")[1:]) chat_response = chat_interface_with_agent(chat_input, agent_name) else: chat_response = "Chat interface function not provided." st.session_state.chat_history.append((chat_input, chat_response)) st.write(f"CodeCraft: {chat_response}") st.subheader("Terminal") terminal_input = st.text_input("Enter a command:") if st.button("Run"): terminal_output = terminal_interface(terminal_input) st.session_state.terminal_history.append((terminal_input, terminal_output)) st.code(terminal_output, language="bash") st.subheader("Code Editor") code_editor = st.text_area("Write your code:", height=300) if st.button("Format & Lint"): formatted_code, lint_message = code_editor_interface(code_editor) st.code(formatted_code, language="python") st.info(lint_message) st.subheader("Summarize Text") text_to_summarize = st.text_area("Enter text to summarize:") if st.button("Summarize"): summary = summarize_text(text_to_summarize) st.write(f"Summary: {summary}") st.subheader("Sentiment Analysis") sentiment_text = st.text_area("Enter text for sentiment analysis:") if st.button("Analyze Sentiment"): sentiment = sentiment_analysis(sentiment_text) st.write(f"Sentiment: {sentiment}") st.subheader("Translate Code") code_to_translate = st.text_area("Enter code to translate:") source_language = st.text_input("Enter source language (e.g. 'Python'):") target_language = st.text_input("Enter target language (e.g. 'JavaScript'):") if st.button("Translate Code"): translated_code = translate_code(code_to_translate, source_language, target_language) st.code(translated_code, language=target_language.lower()) st.subheader("Code Generation") code_idea = st.text_input("Enter your code idea:") if st.button("Generate Code"): generated_code = generate_code(code_idea) st.code(generated_code, language="python") st.subheader("Preset Commands") preset_commands = { "Create a new project": "create_project('project_name')", "Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')", "Run terminal command": "terminal_interface('command', 'project_name')", "Generate code": "generate_code('code_idea')", "Summarize text": "summarize_text('text')", "Analyze sentiment": "sentiment_analysis('text')", "Translate code": "translate_code('code', 'source_language', 'target_language')", } for command_name, command in preset_commands.items(): st.write(f"{command_name}: `{command}`") elif app_mode == "Workspace Chat App": st.header("Workspace Chat App") st.subheader("Create a New Project") project_name = st.text_input("Enter project name:") if st.button("Create Project"): workspace_status = workspace_interface(project_name) st.success(workspace_status) st.subheader("Add Code to Workspace") code_to_add = st.text_area("Enter code to add to workspace:") file_name = st.text_input("Enter file name (e.g. 'app.py'):") if st.button("Add Code"): add_code_status = add_code_to_workspace(project_name, code_to_add, file_name) st.success(add_code_status) st.subheader("Terminal (Workspace Context)") terminal_input = st.text_input("Enter a command within the workspace:") if st.button("Run Command"): terminal_output = terminal_interface(terminal_input, project_name) st.code(terminal_output, language="bash") st.subheader("Chat with CodeCraft for Guidance") chat_input = st.text_area("Enter your message for guidance:") if st.button("Get Guidance"): chat_response = "Chat interface function not provided." st.session_state.chat_history.append((chat_input, chat_response)) st.write(f"CodeCraft: {chat_response}") st.subheader("Chat History") for user_input, response in st.session_state.chat_history: st.write(f"User: {user_input}") st.write(f"CodeCraft: {response}") st.subheader("Terminal History") for command, output in st.session_state.terminal_history: st.write(f"Command: {command}") st.code(output, language="bash") st.subheader("Workspace Projects") for project, details in st.session_state.workspace_projects.items(): st.write(f"Project: {project}") for file in details['files']: st.write(f" - {file}") st.subheader("Chat with AI Agents") if st.session_state.available_agents: selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents) agent_chat_input = st.text_area("Enter your message for the agent:") if st.button("Send to Agent"): agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent) st.session_state.chat_history.append((agent_chat_input, agent_chat_response)) st.write(f"{selected_agent}: {agent_chat_response}") else: st.write("No agents available. Please create an agent first.") st.subheader("Automate Build Process") if st.button("Automate"): if st.session_state.available_agents: selected_agent = st.session_state.available_agents[0] agent = AIAgent(selected_agent, "", []) summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects) st.write("Autonomous Build Summary:") st.write(summary) st.write("Next Step:") st.write(next_step) else: st.write("No agents available. Please create an agent first.")