Update app.py

app.py

@@ -1,39 +1,63 @@
-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)
+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)