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import subprocess
def run_terminal_command(command):
try:
# Run the terminal command and capture its output
output = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
return output.decode("utf-8") # Decode bytes to string
except subprocess.CalledProcessError as e:
# Handle errors if the command fails
return f"Error: {e.output.decode('utf-8')}"
# Example command: list files in the current directory
command = "ls"
output = run_terminal_command(command)
print(output)
# import streamlit as st
# import torch
# from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
# from huggingface_hub import hf_hub_download
# from safetensors.torch import load_file
# # Model Path/Repo Information
# base = "stabilityai/stable-diffusion-xl-base-1.0"
# repo = "ByteDance/SDXL-Lightning"
# ckpt = "sdxl_lightning_4step_unet.safetensors"
# # Load model (Executed only once for efficiency)
# @st.cache_resource
# def load_sdxl_pipeline():
# unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cpu", torch.float32)
# unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cpu"))
# pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float32, variant="fp16").to("cpu")
# pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# return pipe
# # Streamlit UI
# st.title("Image Generation")
# prompt = st.text_input("Enter your image prompt:")
# if st.button("Generate Image"):
# if not prompt:
# st.warning("Please enter a prompt.")
# else:
# pipe = load_sdxl_pipeline() # Load the pipeline from cache
# with torch.no_grad():
# image = pipe(prompt).images[0]
# st.image(image)
# GOOGLE_API_KEY = ""
# genai.configure(api_key=GOOGLE_API_KEY)
# model = genai.GenerativeModel('gemini-pro')
# def add_to_json(goal):
# try:
# with open("test.json", "r") as file:
# data = json.load(file)
# except FileNotFoundError:
# data = {"goals": []} # Create the file with an empty 'goals' list if it doesn't exist
# new_item = {"Goal": goal}
# data["goals"].append(new_item)
# with open("test.json", "w") as file:
# json.dump(data, file, indent=4)
# def main():
# if prompt := st.chat_input("Hi, how can I help you?"):
# goals_prompt = f"""Act as a personal assistant... {prompt} """
# completion = model.generate_content(goals_prompt)
# add_to_json(prompt)
# with st.chat_message("Assistant"):
# st.write(completion.text)
# # Display JSON Data
# if st.button("Show JSON Data"):
# with open("test.json", "r") as file:
# data = json.load(file)
# st.json(data) # Streamlit's way to display JSON
# if __name__ == "__main__":
# main()
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