Rename app.py to main.py

app.py

@@ -1,112 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torchvision.transforms.functional import normalize
-from huggingface_hub import hf_hub_download
-import gradio as gr
-from gradio_imageslider import ImageSlider
-from briarmbg import BriaRMBG
-import PIL
-from PIL import Image
-from typing import Tuple
-
-net=BriaRMBG()
-# model_path = "./model1.pth"
-model_path = hf_hub_download("briaai/RMBG-1.4", 'model.pth')
-if torch.cuda.is_available():
-    net.load_state_dict(torch.load(model_path))
-    net=net.cuda()
-else:
-    net.load_state_dict(torch.load(model_path,map_location="cpu"))
-net.eval() 
-
-    
-def resize_image(image):
-    image = image.convert('RGB')
-    model_input_size = (1024, 1024)
-    image = image.resize(model_input_size, Image.BILINEAR)
-    return image
-
-
-def process(image):
-
-    # prepare input
-    orig_image = Image.fromarray(image)
-    w,h = orig_im_size = orig_image.size
-    image = resize_image(orig_image)
-    im_np = np.array(image)
-    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
-    im_tensor = torch.unsqueeze(im_tensor,0)
-    im_tensor = torch.divide(im_tensor,255.0)
-    im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
-    if torch.cuda.is_available():
-        im_tensor=im_tensor.cuda()
-
-    #inference
-    result=net(im_tensor)
-    # post process
-    result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
-    ma = torch.max(result)
-    mi = torch.min(result)
-    result = (result-mi)/(ma-mi)    
-    # image to pil
-    im_array = (result*255).cpu().data.numpy().astype(np.uint8)
-    pil_im = Image.fromarray(np.squeeze(im_array))
-    # paste the mask on the original image
-    new_im = Image.new("RGBA", pil_im.size, (0,0,0,0))
-    new_im.paste(orig_image, mask=pil_im)
-    # new_orig_image = orig_image.convert('RGBA')
-
-    return new_im
-    # return [new_orig_image, new_im]
-
-
-# block = gr.Blocks().queue()
-
-# with block:
-#     gr.Markdown("## BRIA RMBG 1.4")
-#     gr.HTML('''
-#       <p style="margin-bottom: 10px; font-size: 94%">
-#         This is a demo for BRIA RMBG 1.4 that using
-#         <a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone. 
-#       </p>
-#     ''')
-#     with gr.Row():
-#         with gr.Column():
-#             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
-#             # input_image = gr.Image(sources=None, type="numpy") # None for upload, ctrl+v and webcam
-#             run_button = gr.Button(value="Run")
-            
-#         with gr.Column():
-#             result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[1], height='auto')
-#     ips = [input_image]
-#     run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
-
-# block.launch(debug = True)
-
-# block = gr.Blocks().queue()
-
-gr.Markdown("## BRIA RMBG 1.4")
-gr.HTML('''
-  <p style="margin-bottom: 10px; font-size: 94%">
-    This is a demo for BRIA RMBG 1.4 that using
-    <a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone. 
-  </p>
-''')
-
-title = ""
-
-
-
-description = r"""
-"""
-examples = [['./input.jpeg'],]
-
-
-
-# output = ImageSlider(position=0.5,label='Image without background', type="pil", show_download_button=True)
-# demo = gr.Interface(fn=process,inputs="image", outputs=output, examples=examples, title=title, description=description)
-demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description)
-
-if __name__ == "__main__":
-    demo.launch(share=True)

main.py

@@ -0,0 +1,80 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi import FastAPI, File, UploadFile, Form, Request
+from fastapi.responses import HTMLResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.responses import StreamingResponse
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+from huggingface_hub import hf_hub_download
+from briarmbg import BriaRMBG
+import PIL
+from PIL import Image
+import io
+
+app = FastAPI()
+
+net = BriaRMBG()
+model_path = hf_hub_download("briaai/RMBG-1.4", 'model.pth')
+
+if torch.cuda.is_available():
+    net.load_state_dict(torch.load(model_path))
+    net = net.cuda()
+else:
+    net.load_state_dict(torch.load(model_path, map_location="cpu"))
+net.eval() 
+
+def resize_image(image):
+    image = image.convert('RGB')
+    model_input_size = (1024, 1024)
+    image = image.resize(model_input_size, Image.BILINEAR)
+    return image
+
+def process_image(image):
+    orig_image = image
+    w, h = orig_image.size
+    image = resize_image(orig_image)
+    im_np = np.array(image)
+    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1)
+    im_tensor = torch.unsqueeze(im_tensor, 0)
+    im_tensor = torch.divide(im_tensor, 255.0)
+    im_tensor = normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
+    
+    if torch.cuda.is_available():
+        im_tensor = im_tensor.cuda()
+
+    result = net(im_tensor)
+    result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode='bilinear'), 0)
+    ma = torch.max(result)
+    mi = torch.min(result)
+    result = (result - mi) / (ma - mi)    
+    im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
+    pil_im = Image.fromarray(np.squeeze(im_array))
+    new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
+    new_im.paste(orig_image, mask=pil_im)
+
+    return new_im
+
+@app.post("/process-image/")
+async def process_image_endpoint(file: UploadFile = File(...)):
+    contents = await file.read()
+    pil_image = Image.open(io.BytesIO(contents))
+    processed_image = process_image(pil_image)
+    
+    # Save the processed image temporarily
+    temp_file_path = "processed_image.png"
+    processed_image.save(temp_file_path)
+    
+    # Return the processed image
+    return FileResponse(temp_file_path, media_type="image/png")
+
+
+app.mount("/", StaticFiles(directory="static", html=True), name="static")
+
+@app.get("/")
+def index() -> FileResponse:
+    return FileResponse(path="/app/static/index.html", media_type="text/html")