First Commit

README.md

@@ -1,10 +1,10 @@
----
-title: Invoice Ocr Api
-emoji: 😻
-colorFrom: gray
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Invoice Ocr Api
+emoji: 😻
+colorFrom: gray
+colorTo: purple
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

@@ -0,0 +1,25 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+import shutil
+import os
+from model_utils import extract_invoice_data_from_image
+
+app = FastAPI()
+
+UPLOAD_DIR = "uploads"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+@app.post("/extract-invoice")
+async def extract_invoice(file: UploadFile = File(...)):
+    # Save the uploaded image temporarily
+    file_location = os.path.join(UPLOAD_DIR, file.filename)
+    with open(file_location, "wb") as f:
+        shutil.copyfileobj(file.file, f)
+
+    # Run OCR + detection
+    extracted_data = extract_invoice_data_from_image(file_location)
+
+    # Optionally, clean up the uploaded file
+    os.remove(file_location)
+
+    return JSONResponse(content=extracted_data)

model_utils.py

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+import cv2
+import pytesseract
+import re
+from PIL import Image
+from ultralytics import YOLO
+
+# Path to your trained YOLO model
+MODEL_PATH = "yolov8m_invoiceOCR.pt"
+
+# YOLO class names (order matters)
+class_names = [
+    "Discount_Percentage", "Due_Date", "Email_Client", "Name_Client", "Products",
+    "Remise", "Subtotal", "Tax", "Tax_Precentage", "Tel_Client", "billing address",
+    "header", "invoice date", "invoice number", "shipping address", "total"
+]
+
+# Load YOLOv8 model
+model = YOLO(MODEL_PATH)
+
+def initialize_data_dict():
+    return {label: [] if label == "Products" else "" for label in class_names}
+
+def parse_products(raw_text):
+    structured = []
+    lines = raw_text.split('\n')
+    for line in lines:
+        match = re.match(r"(\d+)\s+(.*)\s+([\d,]+\.\d{2})\s+([\d,]+\.\d{2})", line)
+        if match:
+            qty, desc, unit_price, amount = match.groups()
+            structured.append({
+                "qty": qty,
+                "description": desc.strip(),
+                "unit_price": unit_price,
+                "amount": amount
+            })
+        elif line.strip():
+            structured.append({"raw": line.strip()})
+    return structured
+
+def extract_invoice_data_from_image(image_path: str):
+    image_bgr = cv2.imread(image_path)
+    image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+    pil_img = Image.fromarray(image_rgb)
+
+    results = model(image_path)[0]
+    data = initialize_data_dict()
+
+    for box in results.boxes:
+        x1, y1, x2, y2 = map(int, box.xyxy[0])
+        cls_id = int(box.cls[0])
+        label = class_names[cls_id]
+
+        cropped_img = pil_img.crop((x1, y1, x2, y2))
+        extracted_text = pytesseract.image_to_string(cropped_img, config='--psm 6').strip()
+
+        if label == "Products" and extracted_text:
+            structured_products = parse_products(extracted_text)
+            data["Products"].extend(structured_products)
+        elif extracted_text:
+            data[label] = extracted_text
+
+    return data

requirements.txt

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+fastapi
+uvicorn
+opencv-python
+pytesseract
+torch
+Pillow
+ultralytics