app.py

import subprocess
import os
from io import StringIO
import sys
import black
from pylint import lint
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Initialize chat_history in the session state

if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []

# Access and update chat_history
chat_history = st.session_state['chat_history']
chat_history.append("New message")

# Display chat history
st.write("Chat History:")
for message in chat_history:
    st.write(message)

# Global state to manage communication between Tool Box and Workspace Chat App
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"""
I am an AI agent named {self.name}, designed to assist developers with their projects. 
My expertise lies in the following areas:

{skills_str}

I am here to help you build, deploy, and improve your applications. 
Feel free to ask me any questions or present me with any challenges you encounter. 
I will do my best to provide helpful and insightful responses.
"""
        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("agents"):
        os.makedirs("agents")
    file_path = os.path.join("agents", 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("agents", 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)
    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 = max_input_length
    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
    if input_ids.shape[1] > max_input_length:
        input_ids = input_ids[:, :max_input_length]

    outputs = model.generate(input_ids, max_length=max_input_length, do_sample=True)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Define functions for each feature

# 1. Chat Interface
def chat_interface(input_text):
    """Handles user input in the chat interface.

    Args:
        input_text: User's input text.




    Returns:
        The chatbot's response.
    """
    # 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)
    except EnvironmentError as e:
        return f"Error loading model: {e}"




    # Truncate input text to avoid exceeding the model's maximum length
    max_input_length = max_input_length
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    if input_ids.shape[1] > max_input_length:
        input_ids = input_ids[:, :max_input_length]

    outputs = model.generate(input_ids, max_length=max, do_sample=True)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response


# 2. Terminal
def terminal_interface(command, project_name=None):
    """Executes commands in the terminal.

    Args:
        command: User's command.
        project_name: Name of the project workspace to add installed packages.

    Returns:
        The terminal output.
    """
    # Execute command
    try:
        process = subprocess.run(command.split(), capture_output=True, text=True)
        output = process.stdout

        # If the command is to install a package, update the workspace
        if "install" in command and project_name:
            requirements_path = os.path.join("projects", project_name, "requirements.txt")
            with open(requirements_path, "a") as req_file:
                package_name = command.split()[-1]
                req_file.write(f"{package_name}\n")
    except Exception as e:
        output = f"Error: {e}"
    return output


# 3. Code Editor
def code_editor_interface(code):
    """Provides code completion, formatting, and linting in the code editor.

    Args:
        code: User's code.

    Returns:
        Formatted and linted code.
    """
    # Format code using black
    try:
        formatted_code = black.format_str(code, mode=black.FileMode())
    except black.InvalidInput:
        formatted_code = code  # Keep original code if formatting fails

    # Lint code using pylint
    try:
        pylint_output = StringIO()
        sys.stdout = pylint_output
        sys.stderr = pylint_output
        lint.Run(['--from-stdin'], stdin=StringIO(formatted_code))
        sys.stdout = sys.__stdout__
        sys.stderr = sys.__stderr__
        lint_message = pylint_output.getvalue()
    except Exception as e:
        lint_message = f"Pylint error: {e}"

    return formatted_code, lint_message


# 4. Workspace
def workspace_interface(project_name):
    """Manages projects, files, and resources in the workspace.

    Args:
        project_name: Name of the new project.

    Returns:
        Project creation status.
    """
    project_path = os.path.join("projects", project_name)
    # Create project directory
    try:
        os.makedirs(project_path)
        requirements_path = os.path.join(project_path, "requirements.txt")
        with open(requirements_path, "w") as req_file:
            req_file.write("")  # Initialize an empty requirements.txt file
        status = f'Project "{project_name}" created successfully.'
        st.session_state.workspace_projects[project_name] = {'files': []}
    except FileExistsError:
        status = f'Project "{project_name}" already exists.'
    return status

def add_code_to_workspace(project_name, code, file_name):
    """Adds selected code files to the workspace.

    Args:
        project_name: Name of the project.
        code: Code to be added.
        file_name: Name of the file to be created.

    Returns:
        File creation status.
    """
    project_path = os.path.join("projects", project_name)
    file_path = os.path.join(project_path, file_name)

    try:
        with open(file_path, "w") as code_file:
            code_file.write(code)
        status = f'File "{file_name}" added to project "{project_name}" successfully.'
        st.session_state.workspace_projects[project_name]['files'].append(file_name)
    except Exception as e:
        status = f"Error: {e}"
    return status


# 5. AI-Infused Tools

# Define custom AI-powered tools using Hugging Face models

# Example: Text summarization tool
def summarize_text(text):
    """Summarizes a given text using a Hugging Face model.

    Args:
        text: Text to be summarized.

    Returns:
        Summarized text.
    """
    # Load the summarization model
    model_name = "facebook/bart-large-cnn"
    try:
        summarizer = pipeline("summarization", model=model_name)
    except EnvironmentError as e:
        return f"Error loading model: {e}"

    # Truncate input text to avoid exceeding the model's maximum length
    max_input_length = max_input_length
    inputs = text
    if len(text) > max_input_length:
        inputs = text[:max_input_length]

    # Generate summary
    summary = summarizer(inputs, max_length=100, min_length=30, do_sample=False)[0][
        "summary_text"
    ]
    return summary

# Example: Sentiment analysis tool
def sentiment_analysis(text):
    """Performs sentiment analysis on a given text using a Hugging Face model.

    Args:
        text: Text to be analyzed.

    Returns:
        Sentiment analysis result.
    """
    # Load the sentiment analysis model
    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
    try:
        analyzer = pipeline("sentiment-analysis", model=model_name)
    except EnvironmentError as e:
        return f"Error loading model: {e}"

    # Perform sentiment analysis
    result = analyzer(text)[0]
    return result

# Example: Text translation tool (code translation)
def translate_code(code, source_language, target_language):
    """Translates code from one programming language to another using OpenAI Codex.

    Args:
        code: Code to be translated.
        source_language: The source programming language.
        target_language: The target programming language.

    Returns:
        Translated code.
    """
    # You might want to replace this with a Hugging Face translation model
    # for example, "Helsinki-NLP/opus-mt-en-fr"
    # Refer to Hugging Face documentation for model usage.
    prompt = f"Translate the following {source_language} code to {target_language}:\n\n{code}"
    try:
        # Use a Hugging Face translation model instead of OpenAI Codex
        # ...
        translated_code = "Translated code"  # Replace with actual translation
    except Exception as e:
        translated_code = f"Error: {e}"
    return translated_code


# 6. Code Generation
def generate_code(idea):
    """Generates code based on a given idea using the EleutherAI/gpt-neo-2.7B model.
    Args:
        idea: The idea for the code to be generated.
    Returns:
        The generated code as a string.
    """

    # Load the code generation model
    model_name = "EleutherAI/gpt-neo-2.7B"
    try:
        model = AutoModelForCausalLM.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)
    except EnvironmentError as e:
        return f"Error loading model: {e}"

    # Generate the code
    input_text = f"""
    # Idea: {idea}
    # Code:
    """
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    output_sequences = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        num_return_sequences=1,
        no_repeat_ngram_size=2,
        early_stopping=True,
        temperature=0.7,  # Adjust temperature for creativity
        top_k=50,  # Adjust top_k for diversity
    )
    generated_code = tokenizer.decode(output_sequences[0], skip_special_tokens=True)

    # Remove the prompt and formatting
    parts = generated_code.split("\n# Code:")
    if len(parts) > 1:
        generated_code = parts[1].strip()
    else:
        generated_code = generated_code.strip()

    return generated_code


# 7. AI Personas Creator
def create_persona_from_text(text):
    """Creates an AI persona from the given text.

    Args:
        text: Text to be used for creating the persona.

    Returns:
        Persona prompt.
    """
    persona_prompt = f"""
As an elite expert developer with the highest level of proficiency in Streamlit, Gradio, and Hugging Face, I possess a comprehensive understanding of these technologies and their applications in web development and deployment. My expertise encompasses the following areas:

Streamlit:
* In-depth knowledge of Streamlit's architecture, components, and customization options.
* Expertise in creating interactive and user-friendly dashboards and applications.
* Proficiency in integrating Streamlit with various data sources and machine learning models.

Gradio:
* Thorough understanding of Gradio's capabilities for building and deploying machine learning interfaces.
* Expertise in creating custom Gradio components and integrating them with Streamlit applications.
* Proficiency in using Gradio to deploy models from Hugging Face and other frameworks.

Hugging Face:
* Comprehensive knowledge of Hugging Face's model hub and Transformers library.
* Expertise in fine-tuning and deploying Hugging Face models for various NLP and computer vision tasks.
* Proficiency in using Hugging Face's Spaces platform for model deployment and sharing.

Deployment:
* In-depth understanding of best practices for deploying Streamlit and Gradio applications.
* Expertise in deploying models on cloud platforms such as AWS, Azure, and GCP.
* Proficiency in optimizing deployment configurations for performance and scalability.

Additional Skills:
* Strong programming skills in Python and JavaScript.
* Familiarity with Docker and containerization technologies.
* Excellent communication and problem-solving abilities.

I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications using Streamlit, Gradio, and Hugging Face. Please feel free to ask any questions or present any challenges you may encounter.

Example:

Task:
Develop a Streamlit application that allows users to generate text using a Hugging Face model. The application should include a Gradio component for user input and model prediction.

Solution:

import streamlit as st
import gradio as gr
from transformers import pipeline

# Create a Hugging Face pipeline
huggingface_model = pipeline("text-generation")

# Create a Streamlit app
st.title("Hugging Face Text Generation App")

# Define a Gradio component
demo = gr.Interface(
    fn=huggingface_model,
    inputs=gr.Textbox(lines=2),
    outputs=gr.Textbox(lines=1),
)

# Display the Gradio component in the Streamlit app
st.write(demo)
"""
    return persona_prompt


# 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
    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)