import os import streamlit as st import subprocess from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM import black from pylint import lint from io import StringIO import sys import torch from huggingface_hub import hf_hub_url, cached_download, HfApi import base64 # Add the new HTML code below custom_html = '''
''' # Update the markdown function to accept custom HTML code def markdown_with_custom_html(md, html): md_content = md if html: return f"{md_content}\n\n{html}" else: return md_content markdown_text = "Compare model responses with me!" markdown_with_custom_html(markdown_text, custom_html) # Set your Hugging Face API key here # hf_token = "YOUR_HUGGING_FACE_API_KEY" # Replace with your actual token # Get Hugging Face token from secrets.toml - this line should already be in the main code hf_token = "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" 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': {} } # List of top downloaded free code-generative models from Hugging Face Hub AVAILABLE_CODE_GENERATIVE_MODELS = [ "bigcode/starcoder", # Popular and powerful "Salesforce/codegen-350M-mono", # Smaller, good for quick tasks "microsoft/CodeGPT-small", # Smaller, good for quick tasks "google/flan-t5-xl", # Powerful, good for complex tasks "facebook/bart-large-cnn", # Good for text-to-code tasks ] # Load pre-trained RAG retriever rag_retriever = RagRetriever.from_pretrained("facebook/rag-token-base") # Use a Hugging Face RAG model # Load pre-trained chat model chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium") # Use a Hugging Face chat model # Load tokenizer tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") def process_input(user_input): # Input pipeline: Tokenize and preprocess user input input_ids = tokenizer(user_input, return_tensors="pt").input_ids attention_mask = tokenizer(user_input, return_tensors="pt").attention_mask # RAG model: Generate response with torch.no_grad(): output = rag_retriever(input_ids, attention_mask=attention_mask) response = output.generator_outputs[0].sequences[0] # Chat model: Refine response chat_input = tokenizer(response, return_tensors="pt") chat_input["input_ids"] = chat_input["input_ids"].unsqueeze(0) chat_input["attention_mask"] = chat_input["attention_mask"].unsqueeze(0) with torch.no_grad(): chat_output = chat_model(**chat_input) refined_response = chat_output.sequences[0] # Output pipeline: Return final response return refined_response class AIAgent: def __init__(self, name, description, skills, hf_api=None): self.name = name self.description = description self.skills = skills self._hf_api = hf_api self._hf_token = hf_token # Store the token here @property def hf_api(self): if not self._hf_api and self.has_valid_hf_token(): self._hf_api = HfApi(token=self._hf_token) return self._hf_api def has_valid_hf_token(self): return bool(self._hf_token) async def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model, hf_token): self._hf_token = hf_token # Continuation of previous methods 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()]) # Analyze chat history and workspace projects to suggest actions # Example: # - Check if the user has requested to create a new file # - Check if the user has requested to install a package # - Check if the user has requested to run a command # - Check if the user has requested to generate code # - Check if the user has requested to translate code # - Check if the user has requested to summarize text # - Check if the user has requested to analyze sentiment # Generate a response based on the analysis next_step = "Based on the current state, the next logical step is to implement the main application logic." # Ensure project folder exists project_path = os.path.join(PROJECT_ROOT, project_name) if not os.path.exists(project_path): os.makedirs(project_path) # Create requirements.txt if it doesn't exist requirements_file = os.path.join(project_path, "requirements.txt") if not os.path.exists(requirements_file): with open(requirements_file, "w") as f: f.write("# Add your project's dependencies here\n") # Create app.py if it doesn't exist app_file = os.path.join(project_path, "app.py") if not os.path.exists(app_file): with open(app_file, "w") as f: f.write("# Your project's main application logic goes here\n") # Generate GUI code for app.py if requested if "create a gui" in summary.lower(): gui_code = generate_code("Create a simple GUI for this application", selected_model) with open(app_file, "a") as f: f.write(gui_code) # Run the default build process build_command = "pip install -r requirements.txt && python app.py" try: result = subprocess.run(build_command, shell=True, capture_output=True, text=True, cwd=project_path) st.write(f"Build Output:\n{result.stdout}") if result.stderr: st.error(f"Build Errors:\n{result.stderr}") except Exception as e: st.error(f"Build Error: {e}") return summary, next_step def deploy_built_space_to_hf(self): if not self._hf_api or not self._hf_token: raise ValueError("Cannot deploy the Space since no valid Hugoging Face API connection was established.") # Assuming you have a function to get the files for your Space repository_name = f"my-awesome-space_{datetime.now().timestamp()}" files = get_built_space_files() # Placeholder - you'll need to define this function # Create the Space create_space(self.hf_api, repository_name, "Description", True, files) st.markdown("## Congratulations! Successfully deployed Space 🚀 ##") st.markdown(f"[Check out your new Space here](https://huggingface.co/spaces/{repository_name})") # Add any missing functions from your original code (e.g., get_built_space_files) def get_built_space_files(): # Replace with your logic to gather the files you want to deploy return { "app.py": "# Your Streamlit app code here", "requirements.txt": "streamlit\ntransformers" # Add other files as needed } 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 ="MaziyarPanahi/Codestral-22B-v0.1-GGUF" try: from transformers import AutoModel, AutoTokenizer # Import AutoModel here model = AutoModel.from_pretrained("MaziyarPanahi/Codestral-22B-v0.1-GGUF") 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 # Terminal interface 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, 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 # Code editor interface 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 # Text summarization tool def summarize_text(text): summarizer = pipeline("summarization") summary = summarizer(text, max_length=130, min_length=30, do_sample=False) return summary[0]['summary_text'] # Sentiment analysis tool def sentiment_analysis(text): analyzer = pipeline("sentiment-analysis") result = analyzer(text) return result[0]['label'] # Text translation tool (code translation) def translate_code(code, source_language, target_language): # Use a Hugging Face translation model instead of OpenAI translator = pipeline("translation", model="bartowski/Codestral-22B-v0.1-GGUF") # Example: English to Spanish translated_code = translator(code, target_lang=target_language)[0]['translation_text'] return translated_code def generate_code(code_idea, model_name): """Generates code using the selected model.""" try: generator = pipeline('text-generation', model=model_name) generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text'] return generated_code except Exception as e: return f"Error generating code: {e}" def chat_interface(input_text): """Handles general chat interactions with the user.""" # Use a Hugging Face chatbot model or your own logic chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium") response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text'] return response # Workspace interface 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." # Add code to workspace 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 create_space(api, name, description, public, files, entrypoint="launch.py"): url = f"{hf_hub_url()}spaces/{name}/prepare-repo" headers = {"Authorization": f"Bearer {api.access_token}"} payload = { "public": public, "gitignore_template": "web", "default_branch": "main", "archived": False, "files": [] } for filename, contents in files.items(): data = { "content": contents, "path": filename, "encoding": "utf-8", "mode": "overwrite" if "#\{random.randint(0, 1)\}" not in contents else "merge", } payload["files"].append(data) response = requests.post(url, json=payload, headers=headers) response.raise_for_status() location = response.headers.get("Location") # wait_for_processing(location, api) # You might need to implement this if it's not already defined return Repository(name=name, api=api) # 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"]) # Get Hugging Face token from secrets.toml hf_token = st.secrets["huggingface"]["hf_token"] if app_mode == "AI Agent Creator": # 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": # Tool Box st.header("AI-Powered Tools") # Chat Interface st.subheader("Chat with CodeCraft") chat_input = st.text_area("Enter your message:") if st.button("Send"): chat_response = chat_interface(chat_input) st.session_state.chat_history.append((chat_input, chat_response)) st.write(f"CodeCraft: {chat_response}") # Terminal Interface 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") # Code Editor Interface 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) # Text Summarization Tool 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}") # Sentiment Analysis Tool 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}") # Text Translation Tool (Code Translation) 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()) # Code Generation 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") elif app_mode == "Workspace Chat App": # Workspace Chat App st.header("Workspace Chat App") def get_built_space_files(): """ Gathers the necessary files for the Hugging Face Space, handling different project structures and file types. """ files = {} # Get the current project name (adjust as needed) project_name = st.session_state.get('project_name', 'my_project') project_path = os.path.join(PROJECT_ROOT, project_name) # Define a list of files/directories to search for targets = [ "app.py", "requirements.txt", "Dockerfile", "docker-compose.yml", # Example YAML file "src", # Example subdirectory "assets" # Another example subdirectory ] # Iterate through the targets for target in targets: target_path = os.path.join(project_path, target) # If the target is a file, add it to the files dictionary if os.path.isfile(target_path): add_file_to_dictionary(files, target_path) # If the target is a directory, recursively search for files within it elif os.path.isdir(target_path): for root, _, filenames in os.walk(target_path): for filename in filenames: file_path = os.path.join(root, filename) add_file_to_dictionary(files, file_path) return files def add_file_to_dictionary(files, file_path): """Helper function to add a file to the files dictionary.""" filename = os.path.relpath(file_path, PROJECT_ROOT) # Get relative path # Handle text and binary files if filename.endswith((".py", ".txt", ".json", ".html", ".css", ".yml", ".yaml")): with open(file_path, "r") as f: files[filename] = f.read() else: with open(file_path, "rb") as f: file_content = f.read() files[filename] = base64.b64encode(file_content).decode("utf-8") # Project Workspace Creation 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) # Automatically create requirements.txt and app.py project_path = os.path.join(PROJECT_ROOT, project_name) requirements_file = os.path.join(project_path, "requirements.txt") if not os.path.exists(requirements_file): with open(requirements_file, "w") as f: f.write("# Add your project's dependencies here\n") app_file = os.path.join(project_path, "app.py") if not os.path.exists(app_file): with open(app_file, "w") as f: f.write("# Your project's main application logic goes here\n") # Add Code to Workspace 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.session_state.terminal_history.append((f"Add Code: {code_to_add}", add_code_status)) st.success(add_code_status) # Terminal Interface with Project Context 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.session_state.terminal_history.append((terminal_input, terminal_output)) st.code(terminal_output, language="bash") # Chat Interface for Guidance 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}") # Display Chat History 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}") # Display Terminal History st.subheader("Terminal History") for command, output in st.session_state.terminal_history: st.write(f"Command: {command}") st.code(output, language="bash") # Display Projects and Files 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}") # Chat with AI Agents 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}") # Code Generation st.subheader("Code Generation") code_idea = st.text_input("Enter your code idea:") # Model Selection Menu selected_model = st.selectbox("Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS) if st.button("Generate Code"): generated_code = generate_code(code_idea, selected_model) st.code(generated_code, language="python") # Automate Build Process st.subheader("Automate Build Process") if st.button("Automate"): 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, selected_model) st.write("Autonomous Build Summary:") st.write(summary) st.write("Next Step:") st.write(next_step) # Using the modified and extended class and functions, update the callback for the 'Automate' button in the Streamlit UI: if st.button("Automate", args=(hf_token,)): 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, selected_model, hf_token) st.write("Autonomous Build Summary:") st.write(summary) st.write("Next Step:") st.write(next_step) # If everything went well, proceed to deploy the Space if agent._hf_api and agent.has_valid_hf_token(): agent.deploy_built_space_to_hf() # Use the hf_token to interact with the Hugging Face API api = HfApi(token=hf_token) # Function to create a Space on Hugging Face def create_space(api, name, description, public, files, entrypoint="launch.py"): url = f"{hf_hub_url()}spaces/{name}/prepare-repo" headers = {"Authorization": f"Bearer {api.access_token}"}