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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) |