streamlit_run.py

import streamlit as st
import gradio as gr
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
import subprocess
import os

# Initialize Hugging Face pipelines
text_generator = pipeline("text-generation", model="gpt2")
code_generator = pipeline("text2text-generation", model="t5-base")

# Streamlit App
st.title("AI Dev Tool Kit")

# Sidebar for Navigation
st.sidebar.title("Navigation")
app_mode = st.sidebar.selectbox("Choose the app mode", ["Explorer", "In-Chat Terminal", "Tool Box"])

if app_mode == "Explorer":
    st.header("Explorer")
    st.write("Explore files and projects here.")
    # Implement your explorer functionality here

elif app_mode == "In-Chat Terminal":
    st.header("In-Chat Terminal")

    def run_terminal_command(command):
        try:
            result = subprocess.run(command, shell=True, capture_output=True, text=True)
            return result.stdout if result.returncode == 0 else result.stderr
        except Exception as e:
            return str(e)

    def terminal_interface(command):
        response = run_terminal_command(command)
        return response

    def nlp_code_interpreter(text):
        response = code_generator(text, max_length=150)
        code = response[0]['generated_text']
        return code, run_terminal_command(code)

    with gr.Blocks() as iface:
        terminal_input = gr.Textbox(label="Enter Command or Code")
        terminal_output = gr.Textbox(label="Terminal Output", lines=10)
        terminal_button = gr.Button("Run")

        terminal_button.click(
            nlp_code_interpreter,
            inputs=terminal_input,
            outputs=[terminal_output, terminal_output]
        )

        iface.launch()

    st.write("Use the terminal to execute commands or interpret natural language into code.")

elif app_mode == "Tool Box":
    st.header("Tool Box")
    st.write("Access various AI development tools here.")
    # Implement your tool box functionality here

# Deploy to Hugging Face Spaces
def deploy_to_huggingface(app_name):
    code = f"""
import gradio as gr
def run_terminal_command(command):
    try:
        result = subprocess.run(command, shell=True, capture_output=True, text=True)
        return result.stdout if result.returncode == 0 else result.stderr
    except Exception as e:
        return str(e)
def nlp_code_interpreter(text):
    response = code_generator(text, max_length=150)
        code = response[0]['generated_text']
        return code, run_terminal_command(code)
with gr.Blocks() as iface:
    terminal_input = gr.Textbox(label="Enter Command or Code")
    terminal_output = gr.Textbox(label="Terminal Output", lines=10)
    terminal_button = gr.Button("Run")

    terminal_button.click(
        nlp_code_interpreter,
        inputs=terminal_input,
        outputs=[terminal_output, terminal_output]
    )
iface.launch()
"""

    with open("app.py", "w") as f:
        f.write(code)

    try:
        subprocess.run(["huggingface-cli", "repo", "create", "--type", "space", "--space_sdk", "gradio", app_name], check=True)
        subprocess.run(["git", "init"], cwd=f"./{app_name}", check=True)
        subprocess.run(["git", "add", "."], cwd=f"./{app_name}", check=True)
        subprocess.run(['git', 'commit', '-m', '"Initial commit"'], cwd=f'./{app_name}', check=True)
        subprocess.run(["git", "push", "https://huggingface.co/spaces/" + app_name, "main"], cwd=f'./{app_name}', check=True)
        return f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{app_name}"
    except Exception as e:
        return f"Error deploying to Hugging Face Spaces: {e}"

# Example usage
if st.button("Deploy to Hugging Face"):
    app_name = "ai-dev-toolkit"
    deploy_status = deploy_to_huggingface(app_name)
    st.write(deploy_status)