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| import subprocess | |
| import streamlit as st | |
| from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer | |
| import black | |
| import os | |
| from pylint import lint | |
| from io import StringIO | |
| HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit" | |
| 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 = [] | |
| if 'current_state' not in st.session_state: | |
| st.session_state.current_state = { | |
| 'toolbox': {}, | |
| 'workspace_chat': {} | |
| } | |
| 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. | |
| For now, it provides a simple summary and suggests the next step. | |
| """ | |
| 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 information to files.""" | |
| if not os.path.exists(AGENT_DIRECTORY): | |
| os.makedirs(AGENT_DIRECTORY) | |
| file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt") | |
| config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt") | |
| with open(file_path, "w") as file: | |
| file.write(agent.create_agent_prompt()) | |
| with open(config_path, "w") as file: | |
| file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}") | |
| st.session_state.available_agents.append(agent.name) | |
| # (Optional) Commit and push if you have set up Hugging Face integration. | |
| # commit_and_push_changes(f"Add agent {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): | |
| """Creates an AI agent from the provided text input.""" | |
| 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." | |
| # Load the GPT-2 model | |
| 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 agent prompt and user input (truncate if necessary) | |
| combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:" | |
| 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 response | |
| outputs = model.generate( | |
| input_ids, | |
| max_new_tokens=50, | |
| num_return_sequences=1, | |
| do_sample=True, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response | |
| # Basic chat interface (no agent) | |
| def chat_interface(input_text): | |
| # Load the GPT-2 model | |
| 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}" | |
| # Generate response | |
| outputs = generator(input_text, max_new_tokens=50, num_return_sequences=1, do_sample=True) | |
| response = outputs[0]['generated_text'] | |
| return response | |
| def workspace_interface(project_name): | |
| """Manages project creation.""" | |
| project_path = os.path.join(PROJECT_ROOT, project_name) | |
| if not os.path.exists(PROJECT_ROOT): | |
| os.makedirs(PROJECT_ROOT) | |
| if not os.path.exists(project_path): | |
| os.makedirs(project_path) | |
| st.session_state.workspace_projects[project_name] = {"files": []} | |
| st.session_state.current_state['workspace_chat']['project_name'] = project_name | |
| # (Optional) Commit and push if you have set up Hugging Face integration. | |
| # commit_and_push_changes(f"Create project {project_name}") | |
| 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): | |
| """Adds code to a file in the specified project.""" | |
| 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) | |
| st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code} | |
| # (Optional) Commit and push if you have set up Hugging Face integration. | |
| # commit_and_push_changes(f"Add code to {file_name} in project {project_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): | |
| """Executes commands in the terminal, optionally within a project's directory.""" | |
| 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: | |
| st.session_state.current_state['toolbox']['terminal_output'] = result.stdout | |
| return result.stdout | |
| else: | |
| st.session_state.current_state['toolbox']['terminal_output'] = result.stderr | |
| return result.stderr | |
| def summarize_text(text): | |
| """Summarizes text using a Hugging Face pipeline.""" | |
| summarizer = pipeline("summarization") | |
| summary = summarizer(text, max_length=100, min_length=25, do_sample=False) | |
| st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text'] | |
| return summary[0]['summary_text'] | |
| def sentiment_analysis(text): | |
| """Analyzes sentiment of text using a Hugging Face pipeline.""" | |
| analyzer = pipeline("sentiment-analysis") | |
| sentiment = analyzer(text) | |
| st.session_state.current_state['toolbox']['sentiment'] = sentiment[0] | |
| return sentiment[0] | |
| def code_editor_interface(code): | |
| """Formats and lints Python code.""" | |
| try: | |
| formatted_code = black.format_str(code, mode=black.FileMode()) | |
| lint_result = StringIO() | |
| lint.Run([ | |
| '--disable=C0114,C0115,C0116', | |
| '--output-format=text', | |
| '--reports=n', | |
| '-' | |
| ]) | |
| lint_message = lint_result.getvalue() | |
| return formatted_code, lint_message | |
| except Exception as e: | |
| return code, f"Error formatting or linting code: {e}" | |
| def translate_code(code, input_language, output_language): | |
| """Translates code between programming languages.""" | |
| try: | |
| translator = pipeline("translation", model=f"{input_language}-to-{output_language}") | |
| translated_code = translator(code, max_length=10000)[0]['translation_text'] | |
| st.session_state.current_state['toolbox']['translated_code'] = translated_code | |
| return translated_code | |
| except Exception as e: | |
| return f"Error translating code: {e}" | |
| def generate_code(code_idea): | |
| """Generates code from a user idea using a Hugging Face pipeline.""" | |
| try: | |
| generator = pipeline('text-generation', model='gpt2') | |
| generated_code = generator(f"```python\n{code_idea}\n```", max_length=1000, num_return_sequences=1)[0][ | |
| 'generated_text'] | |
| # Extract code from the generated text | |
| start_index = generated_code.find("```python") + len("```python") | |
| end_index = generated_code.find("```", start_index) | |
| if start_index != -1 and end_index != -1: | |
| generated_code = generated_code[start_index:end_index].strip() | |
| st.session_state.current_state['toolbox']['generated_code'] = generated_code | |
| return generated_code | |
| except Exception as e: | |
| return f"Error generating code: {e}" | |
| def commit_and_push_changes(commit_message): | |
| """(Optional) Commits and pushes changes. | |
| Needs to be configured for your Hugging Face repository. | |
| """ | |
| commands = [ | |
| "git add .", | |
| f"git commit -m '{commit_message}'", | |
| "git push" | |
| ] | |
| for command in commands: | |
| result = subprocess.run(command, shell=True, capture_output=True, text=True) | |
| if result.returncode != 0: | |
| st.error(f"Error executing command '{command}': {result.stderr}") | |
| break | |
| # --- Streamlit App --- | |
| st.title("AI Agent Creator") | |
| # Sidebar navigation | |
| 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") | |
| 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(chat_input) | |
| 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.selectbox("Source Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"]) | |
| target_language = st.selectbox("Target Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"]) | |
| 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(chat_input) | |
| 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") | |
| 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}") | |
| st.subheader("Automate Build Process") | |
| if st.button("Automate"): | |
| if selected_agent: | |
| 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.warning("Please select an AI agent first.") | |