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| import subprocess | |
| import os | |
| from io import StringIO | |
| import sys | |
| import black | |
| import streamlit as st | |
| from pylint import lint | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
| from transformers import pipeline as transformers_pipeline | |
| from huggingface_hub import hf_hub_url, cached_download | |
| import json | |
| import time | |
| import shutil | |
| import gradio as gr | |
| # --- Global State --- | |
| if 'chat_history' not in st.session_state: | |
| st.session_state['chat_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 'available_clusters' not in st.session_state: | |
| st.session_state.available_clusters = [] | |
| if 'current_project' not in st.session_state: | |
| st.session_state.current_project = None | |
| # --- Agent Class --- | |
| class AIAgent: | |
| def __init__(self, name, description, skills, persona_prompt=None): | |
| self.name = name | |
| self.description = description | |
| self.skills = skills | |
| self.persona_prompt = persona_prompt | |
| def create_agent_prompt(self): | |
| skills_str = '\n'.join([f"* {skill}" for skill in self.skills]) | |
| agent_prompt = f""" | |
| I am an AI agent named {self.name}, designed to assist developers with their projects. | |
| My expertise lies in the following areas: | |
| {skills_str} | |
| {self.persona_prompt if self.persona_prompt else ''} | |
| I am here to help you build, deploy, and improve your applications. | |
| Feel free to ask me any questions or present me with any challenges you encounter. | |
| I will do my best to provide helpful and insightful responses. | |
| """ | |
| 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 | |
| # --- Agent Management --- | |
| def save_agent_to_file(agent): | |
| """Saves the agent's prompt to a file.""" | |
| if not os.path.exists("agents"): | |
| os.makedirs("agents") | |
| file_path = os.path.join("agents", 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("agents", 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, persona_prompt=None): | |
| skills = text.split('\n') | |
| agent = AIAgent(name, "AI agent created from text input.", skills, persona_prompt) | |
| save_agent_to_file(agent) | |
| return agent.create_agent_prompt() | |
| # --- Cluster Management --- | |
| def create_agent_cluster(cluster_name, agent_names): | |
| """Creates a cluster of agents.""" | |
| if not os.path.exists("clusters"): | |
| os.makedirs("clusters") | |
| cluster_path = os.path.join("clusters", f"{cluster_name}.json") | |
| with open(cluster_path, "w") as file: | |
| json.dump({"agents": agent_names}, file) | |
| st.session_state.available_clusters.append(cluster_name) | |
| def load_agent_cluster(cluster_name): | |
| """Loads an agent cluster from a file.""" | |
| cluster_path = os.path.join("clusters", f"{cluster_name}.json") | |
| if os.path.exists(cluster_path): | |
| with open(cluster_path, "r") as file: | |
| cluster_data = json.load(file) | |
| return cluster_data["agents"] | |
| else: | |
| return None | |
| # --- Chat Interface --- | |
| 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 which is compatible with AutoModelForCausalLM | |
| model_name = "gpt2" | |
| try: | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| 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 = model.config.max_length | |
| input_ids = tokenizer.encode(combined_input, return_tensors="pt") | |
| if input_ids.shape[1] > max_input_length: | |
| input_ids = input_ids[:, :max_input_length] | |
| outputs = model.generate(input_ids, max_length=max_input_length, do_sample=True) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response | |
| def chat_interface_with_cluster(input_text, cluster_name): | |
| agent_names = load_agent_cluster(cluster_name) | |
| if agent_names is None: | |
| return f"Cluster {cluster_name} not found." | |
| # Load the GPT-2 model which is compatible with AutoModelForCausalLM | |
| model_name = "gpt2" | |
| try: | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| except EnvironmentError as e: | |
| return f"Error loading model: {e}" | |
| # Combine the agent prompt with user input | |
| combined_input = f"User: {input_text}\n" | |
| for agent_name in agent_names: | |
| agent_prompt = load_agent_prompt(agent_name) | |
| combined_input += f"\n{agent_name}:\n{agent_prompt}\n" | |
| # Truncate input text to avoid exceeding the model's maximum length | |
| max_input_length = model.config.max_length | |
| input_ids = tokenizer.encode(combined_input, return_tensors="pt") | |
| if input_ids.shape[1] > max_input_length: | |
| input_ids = input_ids[:, :max_input_length] | |
| outputs = model.generate(input_ids, max_length=max_input_length, do_sample=True) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response | |
| # --- Code Editor --- | |
| def code_editor_interface(code): | |
| """Provides code completion, formatting, and linting in the code editor.""" | |
| # Format code using black | |
| try: | |
| formatted_code = black.format_str(code, mode=black.FileMode()) | |
| except black.InvalidInput: | |
| formatted_code = code # Keep original code if formatting fails | |
| # Lint code using pylint | |
| try: | |
| pylint_output = StringIO() | |
| sys.stdout = pylint_output | |
| sys.stderr = pylint_output | |
| lint.Run(['--from-stdin'], stdin=StringIO(formatted_code)) | |
| sys.stdout = sys.__stdout__ | |
| sys.stderr = sys.__stderr__ | |
| lint_message = pylint_output.getvalue() | |
| except Exception as e: | |
| lint_message = f"Pylint error: {e}" | |
| return formatted_code, lint_message | |
| # --- Workspace Management --- | |
| def workspace_interface(project_name): | |
| """Manages projects, files, and resources in the workspace.""" | |
| project_path = os.path.join("projects", project_name) | |
| # Create project directory | |
| try: | |
| os.makedirs(project_path) | |
| requirements_path = os.path.join(project_path, "requirements.txt") | |
| with open(requirements_path, "w") as req_file: | |
| req_file.write("") # Initialize an empty requirements.txt file | |
| status = f'Project "{project_name}" created successfully.' | |
| st.session_state.workspace_projects[project_name] = {'files': []} | |
| except FileExistsError: | |
| status = f'Project "{project_name}" already exists.' | |
| return status | |
| def add_code_to_workspace(project_name, code, file_name): | |
| """Adds selected code files to the workspace.""" | |
| project_path = os.path.join("projects", project_name) | |
| file_path = os.path.join(project_path, file_name) | |
| try: | |
| with open(file_path, "w") as code_file: | |
| code_file.write(code) | |
| status = f'File "{file_name}" added to project "{project_name}" successfully.' | |
| st.session_state.workspace_projects[project_name]['files'].append(file_name) | |
| except Exception as e: | |
| status = f"Error: {e}" | |
| return status | |
| # --- AI Tools --- | |
| def summarize_text(text): | |
| """Summarizes a given text using a Hugging Face model.""" | |
| model_name = "facebook/bart-large-cnn" | |
| try: | |
| summarizer = pipeline("summarization", model=model_name) | |
| except EnvironmentError as e: | |
| return f"Error loading model: {e}" | |
| # Truncate input text to avoid exceeding the model's maximum length | |
| max_input_length = model.config.max_length | |
| inputs = text | |
| if len(text) > max_input_length: | |
| inputs = text[:max_input_length] | |
| # Generate summary | |
| summary = summarizer(inputs, max_length=100, min_length=30, do_sample=False)[0][ | |
| "summary_text" | |
| ] | |
| return summary | |
| def sentiment_analysis(text): | |
| """Performs sentiment analysis on a given text using a Hugging Face model.""" | |
| model_name = "distilbert-base-uncased-finetuned-sst-2-english" | |
| try: | |
| analyzer = pipeline("sentiment-analysis", model=model_name) | |
| except EnvironmentError as e: | |
| return f"Error loading model: {e}" | |
| # Perform sentiment analysis | |
| result = analyzer(text)[0] | |
| return result | |
| def translate_code(code, source_language, target_language): | |
| """Translates code from one programming language to another using OpenAI Codex.""" | |
| # You might want to replace this with a Hugging Face translation model | |
| # for example, "Helsinki-NLP/opus-mt-en-fr" | |
| # Refer to Hugging Face documentation for model usage. | |
| prompt = f"Translate the following {source_language} code to {target_language}:\n\n{code}" | |
| try: | |
| # Use a Hugging Face translation model instead of OpenAI Codex | |
| # ... | |
| translated_code = "Translated code" # Replace with actual translation | |
| except Exception as e: | |
| translated_code = f"Error: {e}" | |
| return translated_code | |
| def generate_code(idea): | |
| """Generates code based on a given idea using the EleutherAI/gpt-neo-2.7B model.""" | |
| model_name = "EleutherAI/gpt-neo-2.7B" | |
| try: | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| except EnvironmentError as e: | |
| return f"Error loading model: {e}" | |
| # Generate the code | |
| input_text = f""" | |
| # Idea: {idea} | |
| # Code: | |
| """ | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
| output_sequences = model.generate( | |
| input_ids=input_ids, | |
| max_length=model.config.max_length, | |
| num_return_sequences=1, | |
| no_repeat_ngram_size=2, | |
| early_stopping=True, | |
| temperature=0.7, # Adjust temperature for creativity | |
| top_k=50, # Adjust top_k for diversity | |
| ) | |
| generated_code = tokenizer.decode(output_sequences[0], skip_special_tokens=True) | |
| # Remove the prompt and formatting | |
| parts = generated_code.split("\n# Code:") | |
| if len(parts) > 1: | |
| generated_code = parts[1].strip() | |
| else: | |
| generated_code = generated_code.strip() | |
| return generated_code | |
| # --- AI Personas Creator --- | |
| def create_persona_from_text(text): | |
| """Creates an AI persona from the given text.""" | |
| persona_prompt = f""" | |
| As an elite expert developer with the highest level of proficiency in Streamlit, Gradio, and Hugging Face, I possess a comprehensive understanding of these technologies and their applications in web development and deployment. My expertise encompasses the following areas: | |
| Streamlit: | |
| * In-depth knowledge of Streamlit's architecture, components, and customization options. | |
| * Expertise in creating interactive and user-friendly dashboards and applications. | |
| * Proficiency in integrating Streamlit with various data sources and machine learning models. | |
| Gradio: | |
| * Thorough understanding of Gradio's capabilities for building and deploying machine learning interfaces. | |
| * Expertise in creating custom Gradio components and integrating them with Streamlit applications. | |
| * Proficiency in using Gradio to deploy models from Hugging Face and other frameworks. | |
| Hugging Face: | |
| * Comprehensive knowledge of Hugging Face's model hub and Transformers library. | |
| * Expertise in fine-tuning and deploying Hugging Face models for various NLP and computer vision tasks. | |
| * Proficiency in using Hugging Face's Spaces platform for model deployment and sharing. | |
| Deployment: | |
| * In-depth understanding of best practices for deploying Streamlit and Gradio applications. | |
| * Expertise in deploying models on cloud platforms such as AWS, Azure, and GCP. | |
| * Proficiency in optimizing deployment configurations for performance and scalability. | |
| Additional Skills: | |
| * Strong programming skills in Python and JavaScript. | |
| * Familiarity with Docker and containerization technologies. | |
| * Excellent communication and problem-solving abilities. | |
| I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications using Streamlit, Gradio, and Hugging Face. Please feel free to ask any questions or present any challenges you may encounter. | |
| Example: | |
| Task: | |
| Develop a Streamlit application that allows users to generate text using a Hugging Face model. The application should include a Gradio component for user input and model prediction. | |
| Solution: | |
| import streamlit as st | |
| import gradio as gr | |
| from transformers import pipeline | |
| # Create a Hugging Face pipeline | |
| huggingface_model = pipeline("text-generation") | |
| # Create a Streamlit app | |
| st.title("Hugging Face Text Generation App") | |
| # Define a Gradio component | |
| demo = gr.Interface( | |
| fn=huggingface_model, | |
| inputs=gr.Textbox(lines=2), | |
| outputs=gr.Textbox(lines=1), | |
| ) | |
| # Display the Gradio component in the Streamlit app | |
| st.write(demo) | |
| """ | |
| return persona_prompt | |
| # --- Terminal Interface --- | |
| def terminal_interface(command, project_name=None): | |
| """Executes commands in the terminal.""" | |
| # Execute command | |
| try: | |
| process = subprocess.run(command.split(), capture_output=True, text=True) | |
| output = process.stdout | |
| # If the command is to install a package, update the workspace | |
| if "install" in command and project_name: | |
| requirements_path = os.path.join("projects", project_name, "requirements.txt") | |
| with open(requirements_path, "a") as req_file: | |
| package_name = command.split()[-1] | |
| req_file.write(f"{package_name}\n") | |
| except Exception as e: | |
| output = f"Error: {e}" | |
| return output | |
| # --- Build and Deploy --- | |
| def build_project(project_name): | |
| """Builds a project based on the workspace files.""" | |
| project_path = os.path.join("projects", project_name) | |
| requirements_path = os.path.join(project_path, "requirements.txt") | |
| # Install dependencies | |
| os.chdir(project_path) | |
| terminal_interface(f"pip install -r {requirements_path}") | |
| os.chdir("..") | |
| # Create a temporary directory for the built project | |
| build_dir = os.path.join("build", project_name) | |
| os.makedirs(build_dir, exist_ok=True) | |
| # Copy project files to the build directory | |
| for filename in os.listdir(project_path): | |
| if filename == "requirements.txt": | |
| continue | |
| shutil.copy(os.path.join(project_path, filename), build_dir) | |
| # Create a `main.py` file if it doesn't exist | |
| main_file = os.path.join(build_dir, "main.py") | |
| if not os.path.exists(main_file): | |
| with open(main_file, "w") as f: | |
| f.write("# Your Streamlit app code goes here\n") | |
| # Return the path to the built project | |
| return build_dir | |
| def deploy_to_huggingface(build_dir, hf_token, repo_name): | |
| """Deploys the built project to Hugging Face Spaces.""" | |
| # Authenticate with Hugging Face | |
| os.environ["HF_TOKEN"] = hf_token | |
| # Create a new Hugging Face Space repository | |
| try: | |
| subprocess.run(f"huggingface-cli repo create {repo_name}", shell=True, check=True) | |
| except subprocess.CalledProcessError as e: | |
| st.error(f"Error creating Hugging Face Space repository: {e}") | |
| return | |
| # Upload the built project to the repository | |
| try: | |
| subprocess.run(f"huggingface-cli upload {repo_name} {build_dir}", shell=True, check=True) | |
| except subprocess.CalledProcessError as e: | |
| st.error(f"Error uploading project to Hugging Face Space repository: {e}") | |
| return | |
| # Deploy the project to Hugging Face Spaces | |
| try: | |
| subprocess.run(f"huggingface-cli space deploy {repo_name}", shell=True, check=True) | |
| except subprocess.CalledProcessError as e: | |
| st.error(f"Error deploying project to Hugging Face Spaces: {e}") | |
| return | |
| # Display the deployment URL | |
| st.success(f"Project deployed successfully to Hugging Face Spaces: https://huggingface.co/spaces/{repo_name}") | |
| def deploy_locally(build_dir): | |
| """Deploys the built project locally.""" | |
| # Run the project locally | |
| os.chdir(build_dir) | |
| subprocess.run("streamlit run main.py", shell=True, check=True) | |
| os.chdir("..") | |
| # Display a success message | |
| st.success(f"Project deployed locally!") | |
| # --- 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"]) | |
| # --- AI Agent Creator --- | |
| 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):") | |
| persona_prompt_option = st.selectbox("Choose a persona prompt", ["None", "Expert Developer"]) | |
| persona_prompt = None | |
| if persona_prompt_option == "Expert Developer": | |
| persona_prompt = create_persona_from_text("Expert Developer") | |
| if st.button("Create Agent"): | |
| agent_prompt = create_agent_from_text(agent_name, text_input, persona_prompt) | |
| st.success(f"Agent '{agent_name}' created and saved successfully.") | |
| st.session_state.available_agents.append(agent_name) | |
| st.subheader("Create an Agent Cluster") | |
| cluster_name = st.text_input("Enter cluster name:") | |
| agent_names = st.multiselect("Select agents for the cluster", st.session_state.available_agents) | |
| if st.button("Create Cluster"): | |
| create_agent_cluster(cluster_name, agent_names) | |
| st.success(f"Cluster '{cluster_name}' created successfully.") | |
| st.session_state.available_clusters.append(cluster_name) | |
| # --- Tool Box --- | |
| elif app_mode == "Tool Box": | |
| st.header("Tool Box") | |
| # --- Workspace --- | |
| st.subheader("Workspace") | |
| project_name = st.selectbox("Select a project", list(st.session_state.workspace_projects.keys()), key="project_select") | |
| if project_name: | |
| st.session_state.current_project = project_name | |
| for file in st.session_state.workspace_projects[project_name]['files']: | |
| st.write(f" - {file}") | |
| # --- Chat with AI Agents --- | |
| st.subheader("Chat with AI Agents") | |
| selected_agent_or_cluster = st.selectbox("Select an AI agent or cluster", st.session_state.available_agents + st.session_state.available_clusters) | |
| agent_chat_input = st.text_area("Enter your message:") | |
| if st.button("Send"): | |
| if selected_agent_or_cluster in st.session_state.available_agents: | |
| agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent_or_cluster) | |
| elif selected_agent_or_cluster in st.session_state.available_clusters: | |
| agent_chat_response = chat_interface_with_cluster(agent_chat_input, selected_agent_or_cluster) | |
| else: | |
| agent_chat_response = "Invalid selection." | |
| st.session_state.chat_history.append((agent_chat_input, agent_chat_response)) | |
| st.write(f"{selected_agent_or_cluster}: {agent_chat_response}") | |
| # --- Automate Build Process --- | |
| st.subheader("Automate Build Process") | |
| if st.button("Automate"): | |
| agent = AIAgent(selected_agent_or_cluster, "", []) # Load the agent without skills for now | |
| 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) | |
| # --- Workspace Chat App --- | |
| elif app_mode == "Workspace Chat App": | |
| st.header("Workspace Chat App") | |
| # --- Project Selection --- | |
| project_name = st.selectbox("Select a project", list(st.session_state.workspace_projects.keys()), key="project_select") | |
| if project_name: | |
| st.session_state.current_project = project_name | |
| # --- Chat with AI Agents --- | |
| st.subheader("Chat with AI Agents") | |
| selected_agent_or_cluster = st.selectbox("Select an AI agent or cluster", st.session_state.available_agents + st.session_state.available_clusters) | |
| agent_chat_input = st.text_area("Enter your message:") | |
| if st.button("Send"): | |
| if selected_agent_or_cluster in st.session_state.available_agents: | |
| agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent_or_cluster) | |
| elif selected_agent_or_cluster in st.session_state.available_clusters: | |
| agent_chat_response = chat_interface_with_cluster(agent_chat_input, selected_agent_or_cluster) | |
| else: | |
| agent_chat_response = "Invalid selection." | |
| st.session_state.chat_history.append((agent_chat_input, agent_chat_response)) | |
| st.write(f"{selected_agent_or_cluster}: {agent_chat_response}") | |
| # --- Code Editor --- | |
| st.subheader("Code Editor") | |
| code = st.text_area("Enter your code:") | |
| if st.button("Format & Lint"): | |
| formatted_code, lint_message = code_editor_interface(code) | |
| st.code(formatted_code, language="python") | |
| st.write("Linting Report:") | |
| st.write(lint_message) | |
| # --- Add Code to Workspace --- | |
| st.subheader("Add Code to Workspace") | |
| file_name = st.text_input("Enter file name:") | |
| if st.button("Add Code"): | |
| if st.session_state.current_project: | |
| status = add_code_to_workspace(st.session_state.current_project, code, file_name) | |
| st.write(status) | |
| else: | |
| st.warning("Please select a project first.") | |
| # --- Terminal --- | |
| st.subheader("Terminal") | |
| command = st.text_input("Enter a command:") | |
| if st.button("Execute"): | |
| if st.session_state.current_project: | |
| output = terminal_interface(command, st.session_state.current_project) | |
| st.write(output) | |
| else: | |
| st.warning("Please select a project first.") | |
| # --- AI Tools --- | |
| st.subheader("AI Tools") | |
| st.write("Summarize Text:") | |
| text_to_summarize = st.text_area("Enter text to summarize:") | |
| if st.button("Summarize"): | |
| summary = summarize_text(text_to_summarize) | |
| st.write(summary) | |
| st.write("Sentiment Analysis:") | |
| text_to_analyze = st.text_area("Enter text to analyze:") | |
| if st.button("Analyze"): | |
| result = sentiment_analysis(text_to_analyze) | |
| st.write(result) | |
| st.write("Code Translation:") | |
| code_to_translate = st.text_area("Enter code to translate:") | |
| source_language = st.selectbox("Source Language", ["Python", "JavaScript", "C++"]) | |
| target_language = st.selectbox("Target Language", ["Python", "JavaScript", "C++"]) | |
| if st.button("Translate"): | |
| translated_code = translate_code(code_to_translate, source_language, target_language) | |
| st.write(translated_code) | |
| st.write("Code Generation:") | |
| code_idea = st.text_input("Enter your code idea:") | |
| if st.button("Generate"): | |
| generated_code = generate_code(code_idea) | |
| st.code(generated_code, language="python") | |
| # --- Build and Deploy --- | |
| st.subheader("Build and Deploy") | |
| if st.session_state.current_project: | |
| st.write(f"Current project: {st.session_state.current_project}") | |
| if st.button("Build"): | |
| # Implement build logic here | |
| build_dir = build_project(st.session_state.current_project) | |
| st.write(f"Project built successfully! Build directory: {build_dir}") | |
| st.write("Select a deployment target:") | |
| deployment_target = st.selectbox("Deployment Target", ["Local", "Hugging Face Spaces"]) | |
| if deployment_target == "Hugging Face Spaces": | |
| hf_token = st.text_input("Enter your Hugging Face token:") | |
| repo_name = st.text_input("Enter your Hugging Face Space repository name:") | |
| if st.button("Deploy to Hugging Face Spaces"): | |
| # Implement Hugging Face Spaces deployment logic here | |
| deploy_to_huggingface(build_dir, hf_token, repo_name) | |
| elif deployment_target == "Local": | |
| if st.button("Deploy Locally"): | |
| # Implement local deployment logic here | |
| deploy_locally(build_dir) | |
| else: | |
| st.warning("Please select a project first.") | |
| # --- Run the Streamlit App --- | |
| if __name__ == "__main__": | |
| st.set_page_config(page_title="AI Agent Creator", page_icon="🤖") | |
| st.write("This is the AI Agent Creator application.") | |
| st.write("You can create AI agents and agent clusters, and use them to chat, generate code, and more.") | |
| st.write("You can also manage your project workspace, build and deploy your projects, and use AI tools.") |