Add files

app.py

@@ -1,32 +1,32 @@
+import warnings
+warnings.filterwarnings("ignore", message=".*pin_memory.*")
+
+
 import os
 import io
-import warnings
 import easyocr
 import numpy as np
 from fastapi import FastAPI, UploadFile, File
 from fastapi.responses import JSONResponse
 from PIL import Image
 from pdf2image import convert_from_bytes
-from concurrent.futures import ProcessPoolExecutor
+from concurrent.futures import ThreadPoolExecutor
 
 # =========================
 # EasyOCR config
 # =========================
-warnings.filterwarnings("ignore", message=".*pin_memory.*")
-
 MODEL_DIR = "/app/.EasyOCR"
 USER_NET_DIR = os.path.join(MODEL_DIR, "user_network")
 
 os.makedirs(MODEL_DIR, exist_ok=True)
 os.makedirs(USER_NET_DIR, exist_ok=True)
 
-# ✅ preload reader (CPU only for Hugging Face)
+# ✅ preload reader
 reader = easyocr.Reader(
     ['en', 'hi'],
     model_storage_directory=MODEL_DIR,
     user_network_directory=USER_NET_DIR,
-    download_enabled=False,
-    gpu=False
+    download_enabled=False
 )
 
 # =========================
@@ -34,18 +34,19 @@ reader = easyocr.Reader(
 # =========================
 app = FastAPI()
 
-
 @app.get("/")
 async def root():
     return {"message": "OCR API is running on Hugging Face 🚀"}
 
 
-def run_ocr_on_image(image_bytes: bytes) -> str:
-    """OCR worker: takes image bytes, runs OCR, returns text"""
-    image = Image.open(io.BytesIO(image_bytes))
+def run_ocr_on_image(image: Image.Image) -> str:
+    """Convert PIL → numpy, run OCR, return plain text"""
     image_np = np.array(image)
     results = reader.readtext(image_np)
-    return " ".join([str(text) for _, text, _ in results]).strip()
+
+    # ✅ only text, join all detections
+    extracted_text = " ".join([str(text) for _, text, _ in results])
+    return extracted_text.strip()
 
 
 @app.post("/ocr")
@@ -54,29 +55,25 @@ async def ocr(file: UploadFile = File(...)):
         contents = await file.read()
 
         if file.filename.lower().endswith(".pdf"):
-            # ✅ Convert PDF → images (lower DPI for speed)
-            pages = convert_from_bytes(contents, dpi=150)
-
-            # Convert each page to raw bytes (so workers don’t share PIL objects)
-            page_bytes = []
-            for p in pages:
-                buf = io.BytesIO()
-                p.save(buf, format="PNG")
-                page_bytes.append(buf.getvalue())
+            # ✅ Convert PDF → images
+            pages = convert_from_bytes(contents)
 
+            # ✅ Run OCR in parallel
             text_results = []
-            # ✅ Multiprocessing (parallel CPU workers)
-            with ProcessPoolExecutor(max_workers=4) as executor:  # adjust workers
-                page_texts = list(executor.map(run_ocr_on_image, page_bytes))
-
-            for i, text in enumerate(page_texts, start=1):
-                text_results.append({"page": i, "text": text})
+            with ThreadPoolExecutor() as executor:
+                page_texts = list(executor.map(run_ocr_on_image, pages))
+                for i, text in enumerate(page_texts, start=1):
+                    text_results.append({
+                        "page": i,
+                        "text": text
+                    })
 
             return JSONResponse(content={"pdf_results": text_results})
 
         else:
             # ✅ Single image case
-            text = run_ocr_on_image(contents)
+            image = Image.open(io.BytesIO(contents))
+            text = run_ocr_on_image(image)
             return JSONResponse(content={"text": text})
 
     except Exception as e: