import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
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 that continues based on the state of chat history and workspace projects.
    """
    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 prompt to a file locally and then commits to the Hugging Face repository."""
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)

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):
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):
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)
    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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 = 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 chatbot response
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
def workspace_interface(project_name):
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
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):
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."
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, 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 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()
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
def summarize_text(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']
def sentiment_analysis(text):
analyzer = pipeline("sentiment-analysis")
sentiment = analyzer(text)
st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
return sentiment[0]
def translate_code(code, input_language, output_language):
# Define a dictionary to map programming languages to their corresponding file extensions
language_extensions = {
# ignore the specific languages right now, and continue to EOF
}

# 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 OpenAI API
prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an expert software developer."},
        {"role": "user", "content": prompt}
    ]
)
translated_code = response.choices[0].message['content'].strip()

# Return the translated code
translated_code = response.choices[0].message['content'].strip()
st.session_state.current_state['toolbox']['translated_code'] = translated_code
return translated_code
def generate_code(code_idea):
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are an expert software developer."},
{"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
]
)
generated_code = response.choices[0].message['content'].strip()
st.session_state.current_state['toolbox']['generated_code'] = generated_code
return generated_code
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"])
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"):
    if chat_input.startswith("@"):
        agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
        chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
        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}")

# 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")

# Display Preset Commands
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":
# Workspace Chat App
st.header("Workspace Chat App")

# 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)

# 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.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.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}")

# 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)
    st.write("Autonomous Build Summary:")
    st.write(summary)
    st.write("Next Step:")
    st.write(next_step)