Update app.py

app.py

@@ -1,23 +1,36 @@
+import requests
 import gradio as gr
-import pytesseract
-import cv2
-import numpy as np
-from PIL import Image
-from io import BytesIO
 
-def extract_text(image):
-    image = Image.open(image.name)
-    image = image.convert('L')
-    image = np.array(image)
-    text = pytesseract.image_to_string(image)
-    print(text)
+
+def infer(im):
+    im.save("converted.png")
+    url = "https://ajax.thehive.ai/api/demo/classify?endpoint=text_recognition"
+    files = {
+        "image": ("converted.png", open("converted.png", "rb"), "image/png"),
+        "model_type": (None, "detection"),
+        "media_type": (None, "photo"),
+    }
+    headers = {"referer": "https://thehive.ai/"}
+
+    res = requests.post(url, headers=headers, files=files)
+
+    text = ""
+    blocks = []
+    for output in res.json()["response"]["output"]:
+        text += output["block_text"]
+        for poly in output["bounding_poly"]:
+            blocks.append({
+                "text": "".join([c["class"] for c in poly["classes"]]),
+                "rect": poly["dimensions"]
+            })
+
     return text
 
-# Définir l'interface utilisateur 
-inputs = gr.Image(label='Upload an image',image_mode='L',tool='select',type='pil')
-outputs = gr.outputs.Textbox()
-interface = gr.Interface(fn=extract_text, inputs=inputs, outputs=outputs, 
-                         title="Extraction de texte à partir d'une image", 
-                         description="Téléchargez une image contenant du texte et cliquez sur 'Predict' pour extraire le texte.")
-                         
-interface.launch()
+
+iface = gr.Interface(
+    fn=infer,
+    title="Mariam - Beta",
+    description=" La maj du siècle. ",
+    inputs=[gr.Image(type="pil")],
+    outputs=["text"],
+).launch()