from interpreter import interpreter
import streamlit as st

output = interpreter.chat("hi, how are you")

st.write(output)





# 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()