app.py

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 sys

PROJECT_ROOT = "projects"
AGENT_DIRECTORY = "agents"

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.
        """
        # 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

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 = "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

def terminal_interface(command, project_name=None):
    if project_name:
        project_path = os.path.join(PROJECT_ROOT, project_name)
        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

def code_editor_interface(code):
    formatted_code = black.format_str(code, mode=black.FileMode())
    pylint_output = lint.Run([formatted_code], do_exit=False)
    pylint_output_str = StringIO()
    pylint_output.linter.reporter.write_messages(pylint_output_str)
    return formatted_code, pylint_output_str.getvalue()

def summarize_text(text):
    summarizer = pipeline("summarization")
    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
    return summary[0]['summary_text']

def sentiment_analysis(text):
    analyzer = pipeline("sentiment-analysis")
    result = analyzer(text)
    return result[0]['label']

def translate_code(code, source_language, target_language):
    # Placeholder for translation logic
    return f"Translated {source_language} code to {target_language}."

def generate_code(idea):
    # Placeholder for code generation logic
    return f"Generated code based on the idea: {idea}."

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."

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 chat_interface(input_text):
    # Placeholder for chat interface logic
    return f"Chatbot response: {input_text}"

st.title("AI Agent Creator")

sidebar = st.sidebar
sidebar.title("Navigation")
app_mode = sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])

if app_mode == "AI Agent Creator":
    st.header("Create an AI Agent from Text")

    subheader = st.subheader
    agent_name = subheader("Enter agent name:")
    text_input = subheader("Enter skills (one per line):")

    if st.button("Create Agent"):
        agent_prompt = create_agent_from_text(agent_name, text_input)