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import streamlit as st | |
import os | |
import subprocess | |
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer | |
import black | |
from pylint import lint | |
from io import StringIO | |
import openai | |
import sys | |
# Set your OpenAI API key here | |
openai.api_key = "YOUR_OPENAI_API_KEY" | |
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 = [] | |
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.""" | |
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() | |
# 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 | |
) | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return response | |
# Preset commands for no-code-knowledge developers | |
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')", | |
} | |
# 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") | |
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) |