import streamlit as st from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, RagRetriever, AutoModelForSeq2SeqLM import os import subprocess import black from pylint import lint from io import StringIO import sys import torch from huggingface_hub import hf_hub_url, cached_download, HfApi # Access Hugging Face API key from secrets hf_token = st.secrets["hf_token"] if not hf_token: st.error("Hugging Face API key not found. Please make sure it is set in the secrets.") HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit" PROJECT_ROOT = "projects" AGENT_DIRECTORY = "agents" AVAILABLE_CODE_GENERATIVE_MODELS = ["bigcode/starcoder", "Salesforce/codegen-350M-mono", "microsoft/CodeGPT-small"] # 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 'selected_language' not in st.session_state: st.session_state.selected_language = "Python" # AI Guide Toggle ai_guide_level = st.sidebar.radio("AI Guide Level", ["Full Assistance", "Partial Assistance", "No Assistance"]) class AIAgent: def __init__(self, name, description, skills): self.name = name self.description = description self.skills = skills self._hf_api = HfApi() # Initialize HfApi here 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, project_name, selected_model, hf_token): 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 deploy_built_space_to_hf(self, project_name): # Assuming you have a function that generates the space content space_content = generate_space_content(project_name) repository = self._hf_api.create_repo( repo_id=project_name, private=True, token=hf_token, exist_ok=True, space_sdk="streamlit" ) self._hf_api.upload_file( path_or_fileobj=space_content, path_in_repo="app.py", repo_id=project_name, repo_type="space", token=hf_token ) return repository def has_valid_hf_token(self): return self._hf_api.whoami(token=hf_token) is not None def process_input(input_text): chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium", tokenizer="microsoft/DialoGPT-medium") response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text'] return response def run_code(code): try: result = subprocess.run(code, shell=True, capture_output=True, text=True) return result.stdout except Exception as e: return str(e) 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." 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 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 display_chat_history(chat_history): return "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history]) def display_workspace_projects(workspace_projects): return "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()]) def generate_space_content(project_name): # Logic to generate the Streamlit app content based on project_name #... (This is where you'll need to implement the actual code generation) return "import streamlit as st\nst.title('My Streamlit App')\nst.write('Hello, world!')" def get_code_generation_model(language): # Return the code generation model based on the selected language if language == "Python": return "bigcode/starcoder" elif language == "Java": return "Salesforce/codegen-350M-mono" elif language == "JavaScript": return "microsoft/CodeGPT-small" else: return "bigcode/starcoder" def generate_code(input_text, language): # Use the selected code generation model to generate code model_name = get_code_generation_model(language) model = pipeline("text2text-generation", model=model_name) response = model(input_text, max_length=50, num_return_sequences=1)[0]['generated_text'] return response if __name__ == "__main__": st.sidebar.title("Navigation") app_mode = st.sidebar.selectbox("Choose the app mode", ["Home", "Terminal", "Explorer", "Code Editor", "Build & Deploy"]) if app_mode == "Home": st.title("Welcome to AI-Guided Development") st.write("This application helps you build and deploy applications with the assistance of an AI Guide.") st.write("Toggle the AI Guide from the sidebar to choose the level of assistance you need.") elif app_mode == "Terminal": st.header("Terminal") terminal_input = st.text_input("Enter a command:") if st.button("Run"): output = run_code(terminal_input) st.session_state.terminal_history.append((terminal_input, output)) st.code(output, language="bash") if ai_guide_level!= "No Assistance": st.write("Run commands here to add packages to your project. For example: `pip install `.") if terminal_input and "install" in terminal_input: package_name = terminal_input.split("install")[-1].strip() st.write(f"Package `{package_name}` will be added to your project.") elif app_mode == "Explorer": st.header("Explorer") uploaded_file = st.file_uploader("Upload a file", type=["py"]) if uploaded_file: file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type} st.write(file_details) save_path = os.path.join(PROJECT_ROOT, uploaded_file.name) with open(save_path, "wb") as f: f.write(uploaded_file.getbuffer()) st.success(f"File {uploaded_file.name} saved successfully!") st.write("Drag and drop files into the 'app' folder.") for project, details in st.session_state.workspace_projects.items(): st.write(f"Project: {project}") for file in details['files']: st.write(f" - {file}") if st.button(f"Move {file} to app folder"): # Logic to move file to 'app' folder pass if ai_guide_level!= "No Assistance": st.write("You can upload files and move them into the 'app' folder for building your application.") elif app_mode == "Code Editor": st.header("Code Editor") code_editor = st.text_area("Write your code:", height=300) if st.button("Save Code"): # Logic to save code pass if ai_guide_level!= "No Assistance": st.write("The function `foo()` requires the `bar` package. Add it to `requirements.txt`.") elif app_mode == "Build & Deploy": st.header("Build & Deploy") project_name_input = st.text_input("Enter Project Name for Automation:") selected_language = st.selectbox("Select a programming language:", ["Python", "Java", "JavaScript"]) st.session_state.selected_language = selected_language if st.button("Automate"): selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents) selected_model = get_code_generation_model(selected_language) agent = AIAgent(selected_agent, "", []) # Load the agent without skills for now summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects, project_name_input, selected_model, hf_token) st.write("Autonomous Build Summary:") st.write(summary) st.write("Next Step:") st.write(next_step) if agent._hf_api and agent.has_valid_hf_token(): repository = agent.deploy_built_space_to_hf(project_name_input) st.markdown("## Congratulations! Successfully deployed Space 🚀 ##") st.markdown("[Check out your new Space here](hf.co/" + repository.name + ")") # Code Generation if ai_guide_level!= "No Assistance": code_input = st.text_area("Enter code to generate:", height=300) if st.button("Generate Code"): language = st.session_state.selected_language generated_code = generate_code(code_input, language) st.code(generated_code, language=language) # CSS for styling st.markdown(""" """, unsafe_allow_html=True)