app.py

import gradio as gr
import os
import subprocess
import random
import string
from huggingface_hub import cached_download, hf_hub_url
from transformers import pipeline

# Define functions for each feature

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

    Args:
        input_text: User's input text.
        history: Chat history.

    Returns:
        A tuple containing the updated chat history and the chatbot's response.
    """
    # Load the appropriate language model from Hugging Face
    model_name = 'google/flan-t5-xl'  # Choose a suitable model
    model_url = hf_hub_url(repo_id=model_name, revision='main', filename='config.json')
    model_path = cached_download(model_url)
    generator = pipeline('text-generation', model=model_path)

    # Generate chatbot response
    response = generator(input_text, max_length=50, num_return_sequences=1, do_sample=True)[0]['generated_text']

    # Update chat history
    history.append((input_text, response))
    return history, response

# 2. Terminal
def terminal_interface(command, history):
    """Executes commands in the terminal.

    Args:
        command: User's command.
        history: Terminal command history.

    Returns:
        A tuple containing the updated command history and the terminal output.
    """
    # Execute command
    try:
        process = subprocess.run(command.split(), capture_output=True, text=True)
        output = process.stdout
    except Exception as e:
        output = f'Error: {e}'

    # Update command history
    history.append((command, output))
    return history, 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.
    """
    # Implement code completion, formatting, and linting using appropriate libraries
    # For example, you can use the 'black' library for code formatting
    # and 'pylint' for linting
    # ...
    return code

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

    Args:
        project_name: Name of the new project.
        history: Workspace history.

    Returns:
        A tuple containing the updated workspace history and project creation status.
    """
    # Create project directory
    try:
        os.makedirs(os.path.join('projects', project_name))
        status = f'Project \"{project_name}\" created successfully.'
    except FileExistsError:
        status = f'Project \"{project_name}\" already exists.'

    # Update workspace history
    history.append((project_name, status))
    return history, 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.
    """
    summarizer = pipeline('summarization', model='facebook/bart-large-cnn')
    summary = summarizer(text, max_length=100, min_length=30)[0]['summary_text']
    return summary

# 6. Hugging Face Integration

# Define functions for accessing, training, and deploying models

# Example: Load a pre-trained model
def load_model(model_name):
    """Loads a pre-trained model from Hugging Face.

    Args:
        model_name: Name of the model to be loaded.

    Returns:
        The loaded model.
    """
    model_url = hf_hub_url(repo_id=model_name, revision='main')
    model = cached_download(model_url)
    return model

# Create Gradio interface
with gr.Blocks() as demo:
    # Chat interface
    chat_history = gr.State([])  # Initialize chat history
    chat_input = gr.Textbox(label="Chat with CodeCraft", lines=5)
    chat_output = gr.Textbox(label="CodeCraft Response", lines=5)
    chat_button = gr.Button(value="Send")
    chat_button.click(chat_interface, inputs=[chat_input, chat_history], outputs=[chat_history, chat_output])

    # Terminal interface
    terminal_history = gr.State([])  # Initialize terminal history
    terminal_input = gr.Textbox(label="Enter Command", lines=1)
    terminal_output = gr.Textbox(label="Terminal Output", lines=5)
    terminal_button = gr.Button(value="Run")
    terminal_button.click(terminal_interface, inputs=[terminal_input, terminal_history], outputs=[terminal_history, terminal_output])

    # Code editor interface
    code_editor = gr.Code(label="Code Editor", lines=10, language="python")
    code_editor.change(code_editor_interface, inputs=code_editor, outputs=code_editor)

    # Workspace interface
    workspace_history = gr.State([])  # Initialize workspace history
    workspace_input = gr.Textbox(label="Project Name", lines=1)
    workspace_output = gr.Textbox(label="Workspace Data", lines=5)
    workspace_button = gr.Button(value="Create Project")
    workspace_button.click(workspace_interface, inputs=[workspace_input, workspace_history], outputs=[workspace_history, workspace_output])

    # AI-Infused Tools
    text_input = gr.Textbox(label="Enter text to summarize")
    summary_output = gr.Textbox(label="Summarized Text")
    summarize_button = gr.Button(value="Summarize")
    summarize_button.click(summarize_text, inputs=text_input, outputs=summary_output)

# Launch Gradio app
demo.launch(share=True, server_name='0.0.0.0')