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inference.py

@@ -1,85 +0,0 @@
-from fastapi import APIRouter, File, UploadFile, Form
-from typing import Optional
-from PIL import Image
-import urllib.request
-from io import BytesIO
-from config import settings
-import utils
-import os
-import json
-from routers.donut_inference import process_document_donut
-
-
-router = APIRouter()
-
-def count_values(obj):
-    if isinstance(obj, dict):
-        count = 0
-        for value in obj.values():
-            count += count_values(value)
-        return count
-    elif isinstance(obj, list):
-        count = 0
-        for item in obj:
-            count += count_values(item)
-        return count
-    else:
-        return 1
-
-
-@router.post("/inference")
-async def run_inference(file: Optional[UploadFile] = File(None), image_url: Optional[str] = Form(None),
-                        model_in_use: str = Form('donut')):
-
-    # if sparrow_key != settings.sparrow_key:
-    #     return {"error": "Invalid Sparrow key."}
-
-    result = []
-    if file:
-        # Ensure the uploaded file is a JPG image
-        if file.content_type not in ["image/jpeg", "image/jpg"]:
-            return {"error": "Invalid file type. Only JPG images are allowed."}
-
-        image = Image.open(BytesIO(await file.read()))
-        processing_time = 0
-        if model_in_use == 'donut':
-            result, processing_time = process_document_donut(image)
-        utils.log_stats(settings.inference_stats_file, [processing_time, count_values(result), file.filename, settings.model])
-        print(f"Processing time inference: {processing_time:.2f} seconds")
-    elif image_url:
-        # test image url: https://raw.githubusercontent.com/katanaml/sparrow/main/sparrow-data/docs/input/invoices/processed/images/invoice_10.jpg
-        with urllib.request.urlopen(image_url) as response:
-            content_type = response.info().get_content_type()
-            if content_type in ["image/jpeg", "image/jpg"]:
-                image = Image.open(BytesIO(response.read()))
-            else:
-                return {"error": "Invalid file type. Only JPG images are allowed."}
-
-        processing_time = 0
-        if model_in_use == 'donut':
-            result, processing_time = process_document_donut(image)
-        # parse file name from url
-        file_name = image_url.split("/")[-1]
-        utils.log_stats(settings.inference_stats_file, [processing_time, count_values(result), file_name, settings.model])
-        print(f"Processing time inference: {processing_time:.2f} seconds")
-    else:
-        result = {"info": "No input provided"}
-
-    return result
-
-
-@router.get("/statistics")
-async def get_statistics():
-    file_path = settings.inference_stats_file
-
-    # Check if the file exists, and read its content
-    if os.path.exists(file_path):
-        with open(file_path, 'r') as file:
-            try:
-                content = json.load(file)
-            except json.JSONDecodeError:
-                content = []
-    else:
-        content = []
-
-    return content