refactor: major overhaul

src/main.py

@@ -1,71 +1,56 @@
+import enum
 import os
-import pathlib
-import numpy as np
 from base64 import b64decode
+from urllib.parse import unquote
 
-from fastapi.responses import JSONResponse
+import numpy as np
-from pydantic import BaseModel
 from doctr.io import DocumentFile
 from doctr.models import ocr_predictor
-from fastapi import FastAPI, HTTPException, Response
+from fastapi import FastAPI, HTTPException
 from jinja2 import Environment, FileSystemLoader, select_autoescape
-from urllib.parse import unquote
+from pydantic import BaseModel
+from starlette.requests import Request
+from starlette.responses import HTMLResponse, JSONResponse
 
 app = FastAPI()
 
 env = Environment(loader=FileSystemLoader("templates"), autoescape=select_autoescape())
 
 
-@app.get("/")
-def root():
-    base_dir = "/home/m/Projects/sandbox/data/app/upload/classifier/deal/389/"
-    files = os.listdir(base_dir)
-
-    predictor = ocr_predictor(
-        "db_resnet50",
-        pretrained=True,
-        assume_straight_pages=False,
-        preserve_aspect_ratio=True,
-    ).cuda()
-
-    result = {}
-    i = 0
-    for f in files:
-        if i >= 1:
-            break
-
-        if pathlib.Path(f).suffix not in [".jpg", ".png"]:
-            continue
-
-        print("Working on: " + f)
-        doc = DocumentFile.from_images(base_dir + f)
-
-        pred_res = predictor(doc)
-        result[f] = pred_res.render()
-
-        i += 1
-
-    return Response(renderTemplate("main.html", {"results": result}))
-
-
 class Image(BaseModel):
     file_name: str
     file_contents: str
 
 
-@app.post("/detect/")
+class DetectionStrength(str, enum.Enum):
-def file(file: Image):
+    high = "high"
+    medium = "medium"
+    low = "low"
+
+
+@app.get("/")
+def get_root(request: Request):
+    if request.url.path != "/":
+        raise HTTPException(status_code=404)
+
+    return renderTemplate("main.html")
+
+
+@app.post("/detect/{strength}")
+def post_detect(file: Image, strength: DetectionStrength):
     file_name = unquote(file.file_name)
 
+    print("\nWorking on: " + file_name)
+    print("\nStrength: " + strength + "\n")
+
     with open("data/" + file_name, "wb") as f:
         f.write(b64decode(file.file_contents))
 
-    print("Working on: " + file_name)
+    [det_model, rec_model] = get_models_for_detection_strength(strength)
-
     try:
         predictor = ocr_predictor(
-            "db_resnet50",
+            det_model,
-            "vitstr_base",
+            rec_model,
             pretrained=True,
             straighten_pages=True,
             preserve_aspect_ratio=True,
@@ -110,6 +95,15 @@ def convert_dict_items_to_list(d: dict):
     return converted
 
 
-def renderTemplate(template, context=None):
+def get_models_for_detection_strength(strength: DetectionStrength):
+    if strength == DetectionStrength.high:
+        return ["linknet_resnet50", "vitstr_base"]
+    elif strength == DetectionStrength.medium:
+        return ["db_resnet50", "vitstr_base"]
+    elif strength == DetectionStrength.low:
+        return ["db_resnet50", "vitstr_tiny"]
+
+
+def renderTemplate(template, context={}):
     template = env.get_template(template)
-    return template.render(context)
+    return HTMLResponse(template.render(context))