Spaces:
Runtime error
Runtime error
File size: 18,682 Bytes
c4177ba 411f166 2de7ebe 984635f 2de7ebe c4177ba fad993f c4177ba 2de7ebe c4177ba 411f166 2de7ebe 411f166 c4177ba 27dcdec 378f793 27dcdec 378f793 27dcdec c4177ba 378f793 27dcdec c4177ba 378f793 c4177ba 27dcdec c4177ba 2de7ebe c4177ba 2de7ebe 27dcdec c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 378f793 c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 378f793 c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 411f166 c4177ba 2de7ebe c4177ba 2de7ebe c4177ba 2de7ebe 984635f 2de7ebe c4177ba 378f793 c4177ba fad993f 984635f c4177ba 984635f fad993f 984635f fad993f 984635f fad993f 984635f fad993f 984635f fad993f 984635f fad993f 984635f fad993f c4177ba 984635f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 |
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
import os
import json
from streamlit_ace import st_ace
from agent import (
AppType, createLlamaPrompt, createSpace, isPythonOrGradioAppPrompt,
isReactAppPrompt, isStreamlitAppPrompt, generateFiles,
)
import importlib
# Dynamically import the Code symbol from the agent module
code_module = importlib.import_module('agent.Code')
Code = getattr(code_module, 'Code')
# Set Hugging Face repository URL and project root path
HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/Mistri"
PROJECT_ROOT = "projects"
AGENT_DIRECTORY = "agents"
# Global state for session management
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': {}
}
# Load Hugging Face models for code generation, translation, and conversation
try:
code_generator = pipeline("text-generation", model="Salesforce/codegen-350M-mono")
translator = pipeline("translation_xx_to_yy", model="Helsinki-NLP/opus-mt-en-fr") # Replace with appropriate language pair
conversational_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
conversational_tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
conversational_generator = pipeline("text-generation", model=conversational_model, tokenizer=conversational_tokenizer)
except EnvironmentError as e:
st.error(f"Error loading Hugging Face models: {e}")
# Define AIAgent class
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 based on chat history and workspace projects.
This function analyzes the chat history and workspace projects to determine the next steps in the development process.
It uses sentiment analysis to gauge the user's satisfaction and summarization to extract key information.
Args:
chat_history (list): A list of tuples containing user input and agent responses.
workspace_projects (dict): A dictionary of projects and their associated files.
Returns:
tuple: A tuple containing a summary of the current state and the suggested 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}: {', '.join(ws_projects.keys())}" for p, ws_projects in workspace_projects.items()])
sentiment_analyzer = pipeline("sentiment-analysis")
sentiment_output = sentiment_analyzer(summary)[0]
# Use a Hugging Face model for more advanced logic
summarizer = pipeline("summarization")
next_step = summarizer(summary, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
return summary, next_step
# Function to save an agent's prompt to a file and commit to the Hugging Face repository
def save_agent_to_file(agent):
"""Saves the agent's prompt to a file locally and then commits to the Hugging Face repository."""
agents_path = os.path.join(PROJECT_ROOT, AGENT_DIRECTORY)
if not os.path.exists(agents_path):
os.makedirs(agents_path)
agent_file = os.path.join(agents_path, f"{agent.name}.txt")
config_file = os.path.join(agents_path, f"{agent.name}Config.txt")
with open(agent_file, "w") as file:
file.write(agent.create_agent_prompt())
with open(config_file, "w") as file:
file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
st.session_state.available_agents.append(agent.name)
commit_and_push_changes(f"Add agent {agent.name}")
# Function to load an agent's prompt from a file
def load_agent_prompt(agent_name):
"""Loads an agent prompt from a file."""
agent_file = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
if os.path.exists(agent_file):
with open(agent_file, "r") as file:
agent_prompt = file.read()
return agent_prompt
else:
return None
# Function to create an agent from text input
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()
# Chat interface using a selected agent
def chat_interface_with_agent(input_text, agent_name):
"""
Provides a chat interface using a selected AI agent.
Loads the agent's prompt and uses a conversational model to generate responses.
Args:
input_text (str): The user's input text.
agent_name (str): The name of the selected AI agent.
Returns:
str: The AI agent's response.
"""
agent_prompt = load_agent_prompt(agent_name)
if agent_prompt is None:
return f"Agent {agent_name} not found."
# Combine agent prompt with user input
combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
# Generate chatbot response
chatbot_response = conversational_generator(combined_input, max_length=150, min_length=30, do_sample=True)[0]['generated_text']
return chatbot_response
# Chat interface (default)
def chat_interface(input_text):
"""
Provides a general chat interface using a conversational model.
Args:
input_text (str): The user's input text.
Returns:
str: The chatbot's response.
"""
# Generate response
response = conversational_generator(input_text, max_length=150, min_length=30, do_sample=True)[0]['generated_text']
return response
# Workspace interface for creating projects
def workspace_interface(project_name):
"""
Creates a new project workspace.
Args:
project_name (str): The name of the project.
Returns:
str: A message indicating the status of the 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):
st.session_state.workspace_projects[project_name] = {"files": []}
st.session_state.current_state['workspace_chat']['project_name'] = project_name
commit_and_push_changes(f"Create project {project_name}")
return f"Project {project_name} created successfully."
else:
return f"Project {project_name} already exists."
# Function to add code to the workspace
def add_code_to_workspace(project_name, code, file_name):
"""
Adds code to a specified file in a project workspace.
Args:
project_name (str): The name of the project.
code (str): The code to be added.
file_name (str): The name of the file.
Returns:
str: A message indicating the status of the code addition.
"""
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}
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."
# Terminal interface with optional project context
def terminal_interface(command, project_name=None):
"""
Executes a terminal command with optional project context.
Args:
command (str): The terminal command to execute.
project_name (str, optional): The name of the project to execute the command in. Defaults to None.
Returns:
str: The output of the terminal command.
"""
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
# Code editor interface for formatting and linting
def code_editor_interface(code):
"""
Provides a code editor interface with formatting and linting capabilities.
Args:
code (str): The code to be edited.
Returns:
tuple: A tuple containing the formatted code and any linting messages.
"""
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()
st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
st.session_state.current_state['toolbox']['lint_message'] = lint_message
return formatted_code, lint_message
# Function to summarize text using a summarization pipeline
def summarize_text(text):
"""
Summarizes a given text using a Hugging Face summarization pipeline.
Args:
text (str): The text to be summarized.
Returns:
str: The summarized text.
"""
summarizer = pipeline("summarization")
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
return summary[0]['summary_text']
# Function to perform sentiment analysis using a sentiment analysis pipeline
def sentiment_analysis(text):
"""
Performs sentiment analysis on a given text using a Hugging Face sentiment analysis pipeline.
Args:
text (str): The text to be analyzed.
Returns:
dict: The sentiment analysis result.
"""
analyzer = pipeline("sentiment-analysis")
sentiment = analyzer(text)
st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
return sentiment[0]
# Function to translate code using the Hugging Face API
def translate_code(code, input_language, output_language):
"""
Translates code from one programming language to another using a Hugging Face translation pipeline.
Args:
code (str): The code to be translated.
input_language (str): The source programming language.
output_language (str): The target programming language.
Returns:
str: The translated code.
"""
# Define a dictionary to map programming languages to their corresponding file extensions
language_extensions = {
"Python": ".py",
"JavaScript": ".js",
"C++": ".cpp",
"Java": ".java",
# Add more languages and extensions as needed
}
# Add code to handle edge cases such as invalid input and unsupported programming languages
if input_language not in language_extensions:
raise ValueError(f"Invalid input language: {input_language}")
if output_language not in language_extensions:
raise ValueError(f"Invalid output language: {output_language}")
# Use the dictionary to map the input and output languages to their corresponding file extensions
input_extension = language_extensions[input_language]
output_extension = language_extensions[output_language]
# Translate the code using the Hugging Face API
translated_code = translator(code, max_length=1024)[0]['translation_text']
# Return the translated code
st.session_state.current_state['toolbox']['translated_code'] = translated_code
return translated_code
# Function to generate code based on a code idea using the Hugging Face API
def generate_code(code_idea):
"""
Generates code based on a given code idea using a Hugging Face code generation pipeline.
Args:
code_idea (str): The code idea or description.
Returns:
str: The generated code.
"""
# Generate code using the Hugging Face API
generated_code = code_generator(f"python\n{code_idea}\n", max_length=512)[0]['generated_text']
st.session_state.current_state['toolbox']['generated_code'] = generated_code
return generated_code
# Function to commit and push changes to the Hugging Face repository
def commit_and_push_changes(commit_message):
"""Commits and pushes changes to the 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"])
# 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):")
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)
# Tool Box
elif app_mode == "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"):
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}")
# 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}")
# Workspace Chat App
elif app_mode == "Workspace Chat App":
st.header("Workspace Chat App")
col1, col2 = st.columns(2)
with col1:
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)
with col2:
st.subheader("Chat with AI Assistant")
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"AI: {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"AI: {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}")
st.write("Files:")
for file in details["files"]:
st.write(f"- {file}")
# Display the current state
st.sidebar.subheader("Current State")
st.sidebar.json(st.session_state.current_state) |