Update endpoints.py

endpoints.py

@@ -1,18 +1,10 @@
-from fastapi import FastAPI, APIRouter, File, UploadFile, Form
+from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from typing import Optional
-from PIL import Image
-import urllib.request
-from io import BytesIO
-import json
-from config import settings  
-import utils
 from routers import inference, training
-from routers.donut_inference import process_document_donut
 from huggingface_hub import login
+from config import settings
 import os
-
-# Login using Hugging Face token from environment
+# login(settings.huggingface_key)
 login(os.getenv("HUGGINGFACE_KEY"))
 
 app = FastAPI(openapi_url="/api/v1/sparrow-ml/openapi.json", docs_url="/api/v1/sparrow-ml/docs")
@@ -28,66 +20,6 @@ app.add_middleware(
 app.include_router(inference.router, prefix="/api-inference/v1/sparrow-ml", tags=["Inference"])
 app.include_router(training.router, prefix="/api-training/v1/sparrow-ml", tags=["Training"])
 
-router = APIRouter()
-
-def count_values(obj):
-    if isinstance(obj, dict):
-        return sum(count_values(v) for v in obj.values())
-    elif isinstance(obj, list):
-        return sum(count_values(i) for i in obj)
-    else:
-        return 1
-
-@router.post("/inference")
-async def run_inference(
-    file: Optional[UploadFile] = File(None), 
-    image_url: Optional[str] = Form(None),
-    shipper_id: int = Form(...), 
-    model_in_use: str = Form('donut')
-):
-    result = []
-
-    # Dynamically select model
-    model_url = settings.get_model_url(shipper_id)
-    model_name = model_url.replace("https://huggingface.co/spaces/", "")
-    print(f"[DEBUG] Using model: {model_name}")
-
-    if file:
-        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()))
-        result, processing_time = process_document_donut(image, model_url)
-        utils.log_stats(settings.inference_stats_file, [processing_time, count_values(result), file.filename, model_name])
-        print(f"Processing time: {processing_time:.2f} seconds")
-    
-    elif image_url:
-        with urllib.request.urlopen(image_url) as url:
-            image = Image.open(BytesIO(url.read()))
-        
-        result, processing_time = process_document_donut(image, model_url)
-        file_name = image_url.split("/")[-1]
-        utils.log_stats(settings.inference_stats_file, [processing_time, count_values(result), file_name, model_name])
-        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
-    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
-
 @app.get("/")
 async def root():
     return {"message": "Senga delivery notes inferencing"}