Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Ocr Model
-emoji: 🐨
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 4.16.0
+title: ocr_model
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.44.4
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,49 @@
+from paddleocr import PaddleOCR
+import requests
+import numpy as np
+from PIL import Image
+from io import BytesIO
+import json
+import gradio as gr
+
+ocr = PaddleOCR(use_angle_cls=True, lang='en', use_pdserving=False, cls_batch_num=8, det_batch_num=8, rec_batch_num=8)
+
+def index(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content))
+    resize_factor = 1
+    new_size = tuple(int(dim * resize_factor) for dim in img.size)
+    img = img.resize(new_size, Image.Resampling.LANCZOS)
+
+    img_array = np.array(img.convert('RGB'))
+
+    result = ocr.ocr(img_array)
+
+    boxes = [line[0] for line in result]
+    txts = [line[1][0] for line in result]
+    scores = [line[1][1] for line in result]
+
+    print(boxes)
+    print(txts)
+
+    output_dict = {"texts": txts, "boxes": boxes, "scores": scores}
+    output_json = json.dumps(output_dict)  # Convert to JSON string
+
+    return output_json
+
+
+inputs_image_url = [
+    gr.Textbox(type="text", label="Image URL"),
+]
+
+outputs_result_json = [
+    gr.Textbox(type="text", label="Result JSON"),
+]
+
+interface_image_url = gr.Interface(
+    fn=index,
+    inputs=inputs_image_url,
+    outputs=outputs_result_json,
+    title="Text Extraction",
+    cache_examples=False,
+).queue().launch()

requirements.txt

@@ -0,0 +1,9 @@
+common
+paddlepaddle
+paddleocr
+opencv-python
+Shapely
+requests
+numpy
+pillow
+gradio