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# import gradio as gr
# import torch
# from PIL import Image
# from model import CRM
# from inference import generate3d
# import numpy as np
# # Load model
# crm_path = "CRM.pth" # Make sure the model is uploaded to the Space
# model = CRM(torch.load(crm_path, map_location="cpu"))
# model = model.to("cuda:0" if torch.cuda.is_available() else "cpu")
# def generate_3d(image_path, seed=1234, scale=5.5, step=30):
# image = Image.open(image_path).convert("RGB")
# np_img = np.array(image)
# glb_path = generate3d(model, np_img, np_img, "cuda:0" if torch.cuda.is_available() else "cpu")
# return glb_path
# iface = gr.Interface(
# fn=generate_3d,
# inputs=gr.Image(type="filepath"),
# outputs=gr.Model3D(),
# title="Convolutional Reconstruction Model (CRM)",
# description="Upload an image to generate a 3D model."
# )
# iface.launch()
#############2nd################3
# import os
# import torch
# import gradio as gr
# from huggingface_hub import hf_hub_download
# from model import CRM # Make sure this matches your model file structure
# # Define model details
# REPO_ID = "Mariam-Elz/CRM" # Hugging Face model repo
# MODEL_FILES = {
# "ccm-diffusion": "ccm-diffusion.pth",
# "pixel-diffusion": "pixel-diffusion.pth",
# "CRM": "CRM.pth"
# }
# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# # Download models from Hugging Face if not already present
# MODEL_DIR = "./models"
# os.makedirs(MODEL_DIR, exist_ok=True)
# for name, filename in MODEL_FILES.items():
# model_path = os.path.join(MODEL_DIR, filename)
# if not os.path.exists(model_path):
# print(f"Downloading {filename}...")
# hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir=MODEL_DIR)
# # Load the model
# print("Loading CRM Model...")
# model = CRM()
# model.load_state_dict(torch.load(os.path.join(MODEL_DIR, MODEL_FILES["CRM"]), map_location=DEVICE))
# model.to(DEVICE)
# model.eval()
# print("✅ Model Loaded Successfully!")
# # Define Gradio Interface
# def predict(input_image):
# with torch.no_grad():
# output = model(input_image.to(DEVICE)) # Modify based on model input format
# return output.cpu()
# demo = gr.Interface(
# fn=predict,
# inputs=gr.Image(type="pil"),
# outputs=gr.Image(type="pil"),
# title="Convolutional Reconstruction Model (CRM)",
# description="Upload an image to generate a reconstructed output."
# )
# if __name__ == "__main__":
# demo.launch()
########################3rd######################3
import torch
import gradio as gr
import requests
import os
# Download model weights from Hugging Face model repo (if not already present)
model_repo = "Mariam-Elz/CRM" # Your Hugging Face model repo
model_files = {
"ccm-diffusion.pth": "ccm-diffusion.pth",
"pixel-diffusion.pth": "pixel-diffusion.pth",
"CRM.pth": "CRM.pth",
}
os.makedirs("models", exist_ok=True)
for filename, output_path in model_files.items():
file_path = f"models/{output_path}"
if not os.path.exists(file_path):
url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}"
print(f"Downloading {filename}...")
response = requests.get(url)
with open(file_path, "wb") as f:
f.write(response.content)
# Load model (This part depends on how the model is defined)
device = "cuda" if torch.cuda.is_available() else "cpu"
def load_model():
model_path = "models/CRM.pth"
model = torch.load(model_path, map_location=device)
model.eval()
return model
model = load_model()
# Define inference function
def infer(image):
"""Process input image and return a reconstructed image."""
with torch.no_grad():
# Assuming model expects a tensor input
image_tensor = torch.tensor(image).to(device)
output = model(image_tensor)
return output.cpu().numpy()
# Create Gradio UI
demo = gr.Interface(
fn=infer,
inputs=gr.Image(type="numpy"),
outputs=gr.Image(type="numpy"),
title="Convolutional Reconstruction Model",
description="Upload an image to get the reconstructed output."
)
if __name__ == "__main__":
demo.launch()
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