Update main.py

main.py

@@ -1,142 +1,25 @@
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import FileResponse
+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 typing import Tuple
-import cv2
-import fastai
-from fastai.vision import *
-from fastai.utils.mem import *
-from fastai.vision import open_image, load_learner, image, torch
-import numpy as np
-import urllib.request
-import PIL.Image
-from io import BytesIO
-import torchvision.transforms as T
-from PIL import Image
-import requests
-from io import BytesIO
-import fastai
-from fastai.vision import *
-from fastai.utils.mem import *
-from fastai.vision import open_image, load_learner, image, torch
-import numpy as np
-import urllib.request
-import PIL.Image
-from PIL import Image
-from io import BytesIO
-import torchvision.transforms as T
-import requests
-import model_loader
+from fastapi.templating import Jinja2Templates
+from gradio_client import Client
+import os
 
 app = FastAPI()
 
-# Download the model if not already downloaded
-MODEL_URL = "https://www.dropbox.com/s/04suaimdpru76h3/ArtLine_920.pkl?dl=1"
-MODEL_FILENAME = "ArtLine_920.pkl"
-if not os.path.exists(MODEL_FILENAME):
-    model_loader.download_model(MODEL_URL, MODEL_FILENAME)
+hf_token = os.environ.get('HF_TOKEN')
+client = Client("https://ashrafb-image-to-sketch.hf.space/", hf_token=hf_token)
 
-# Load the model
-learn = model_loader.load_model(MODEL_FILENAME)
-
-
-class FeatureLoss(nn.Module):
-    def __init__(self, m_feat, layer_ids, layer_wgts):
-        super().__init__()
-        self.m_feat = m_feat
-        self.loss_features = [self.m_feat[i] for i in layer_ids]
-        self.hooks = hook_outputs(self.loss_features, detach=False)
-        self.wgts = layer_wgts
-        self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
-              ] + [f'gram_{i}' for i in range(len(layer_ids))]
-
-    def make_features(self, x, clone=False):
-        self.m_feat(x)
-        return [(o.clone() if clone else o) for o in self.hooks.stored]
-    
-    def forward(self, input, target):
-        out_feat = self.make_features(target, clone=True)
-        in_feat = self.make_features(input)
-        self.feat_losses = [base_loss(input,target)]
-        self.feat_losses += [base_loss(f_in, f_out)*w
-                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
-        self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3
-                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
-        self.metrics = dict(zip(self.metric_names, self.feat_losses))
-        return sum(self.feat_losses)
-    
-    def __del__(self): self.hooks.remove()
-
-def add_margin(pil_img, top, right, bottom, left, color):
-    width, height = pil_img.size
-    new_width = width + right + left
-    new_height = height + top + bottom
-    result = Image.new(pil_img.mode, (new_width, new_height), color)
-    result.paste(pil_img, (left, top))
-    return result
-
-
-
-
-
-import gradio as gr
-import cv2
-
-
-def get_filename(prefix="sketch"):
-  from datetime import datetime
-  from pytz import timezone
-  return datetime.now(timezone('Asia/Seoul')).strftime('sketch__%Y-%m-%d %H:%M:%S.jpg')
-
-def predict(img):
-  img = PIL.Image.fromarray(img)
-  img = add_margin(img, 250, 250, 250, 250, (255, 255, 255))
-  img = np.array(img)
-
-  h, w = img.shape[:-1]
-  cv2.imwrite("test.jpg", img)
-  img_test = open_image("test.jpg")
-
-  p,img_hr,b = learn.predict(img_test)
-
-  res = (img_hr / img_hr.max()).numpy()
-  res = res[0]  # take only first channel as result
-  res = cv2.resize(res, (w,h))
-
-  output_file = get_filename()
-
-  cv2.imwrite(output_file, (res * 255).astype(np.uint8), [cv2.IMWRITE_JPEG_QUALITY, 50])
-
-  return res, output_file
-
-@app.post("/predict/")
-async def predict(file: UploadFile = File(...)) -> Tuple[str, bytes]:
-    contents = await file.read()
-    img = cv2.imdecode(np.fromstring(contents, np.uint8), cv2.IMREAD_COLOR)
-    img = PIL.Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-    img = add_margin(img, 250, 250, 250, 250, (255, 255, 255))
-    img = np.array(img)
-
-    h, w = img.shape[:-1]
-    cv2.imwrite("test.jpg", img)
-    img_test = open_image("test.jpg")
-
-    p,img_hr,b = learn.predict(img_test)
-
-    res = (img_hr / img_hr.max()).numpy()
-    res = res[0]  # take only first channel as result
-    res = cv2.resize(res, (w,h))
-
-    output_file = get_filename()
-
-    cv2.imwrite(output_file, (res * 255).astype(np.uint8), [cv2.IMWRITE_JPEG_QUALITY, 50])
-
-    return output_file, res.tobytes()
+@app.post("/predict")
+async def predict_sketch(file: UploadFile = File(...)):
+    content = await file.read()
+    # Call the Gradio client to get the sketch result
+    result = client.predict(content, api_name="/predict")
+    return {"sketch_image": result[0], "result_file": result[1]}
 
 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")
-    
+    return FileResponse(path="/app/static/index.html", media_type="text/html")