Rename app.py to main.py

app.py → main.py

@@ -1,10 +1,19 @@
-import os
-
+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
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import StreamingResponse
 import cv2
-import gradio as gr
 import numpy as np
-import onnxruntime as ort
 from PIL import Image
+import io
+
+import onnxruntime as ort
 
 _sess_options = ort.SessionOptions()
 _sess_options.intra_op_num_threads = os.cpu_count()
@@ -12,9 +21,7 @@ MODEL_SESS = ort.InferenceSession(
     "cartoonizer.onnx", _sess_options, providers=["CPUExecutionProvider"]
 )
 
-
-def preprocess_image(image: Image) -> np.ndarray:
-    image = np.array(image)
+def preprocess_image(image: np.ndarray) -> np.ndarray:
     image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
     h, w, c = np.shape(image)
@@ -29,26 +36,26 @@ def preprocess_image(image: Image) -> np.ndarray:
     image = image.astype(np.float32) / 127.5 - 1
     return np.expand_dims(image, axis=0)
 
-
-def inference(image: np.ndarray) -> Image:
+def inference(image: np.ndarray) -> np.ndarray:
     image = preprocess_image(image)
     results = MODEL_SESS.run(None, {"input_photo:0": image})
     output = (np.squeeze(results[0]) + 1.0) * 127.5
     output = np.clip(output, 0, 255).astype(np.uint8)
     output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-    return Image.fromarray(output)
+    return output
 
+app = FastAPI()
 
-title = "Generate cartoonized images"
-article = "Demo of CartoonGAN model (https://systemerrorwang.github.io/White-box-Cartoonization/). \nDemo image is from https://unsplash.com/photos/f0SgAs27BYI."
+@app.post("/cartoonize/")
+async def cartoonize_image(file: UploadFile = File(...)):
+    contents = await file.read()
+    image = Image.open(io.BytesIO(contents))
+    image = np.array(image)
+    cartoonized_image = inference(image)
+    return StreamingResponse(io.BytesIO(cv2.imencode('.jpg', cartoonized_image)[1]), media_type="image/jpeg")
 
-iface = gr.Interface(
-    inference,
-    inputs=gr.inputs.Image(type="pil", label="Input Image"),
-    outputs="image",
-    title=title,
-    article=article,
-    allow_flagging="never",
-    examples=[["mountain.jpeg"]],
-)
-iface.launch()
+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")