Create app.py

app.py

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+# --- Setup ---
+import gradio as gr
+import numpy as np
+from PIL import Image
+import torch
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import cv2
+from paddleocr import TextDetection
+from huggingface_hub import spaces
+import time
+
+# Request H200 GPU
+spaces.GPU.require("H200")
+
+# --- Model Load ---
+MODEL_HUB_ID = "imperiusrex/Handwritten_model"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+processor = TrOCRProcessor.from_pretrained(MODEL_HUB_ID)
+model = VisionEncoderDecoderModel.from_pretrained(MODEL_HUB_ID)
+model.to(device)
+model.eval()
+ocr_det_model = TextDetection(model_name="PP-OCRv5_server_det")
+
+# --- Core OCR Function ---
+def recognize_handwritten_text_from_npimg(np_img):
+    pil_img = Image.fromarray(np_img.astype(np.uint8)).convert("RGB")
+    image_np = np.array(pil_img)
+    detection_results = ocr_det_model.predict(image_np, batch_size=1)
+
+    detected_polys = []
+    for res in detection_results:
+        polys = res.get('dt_polys', [])
+        if polys is not None:
+            detected_polys.extend(polys.tolist())
+
+    cropped_images = []
+    if detected_polys:
+        for box in detected_polys:
+            box = np.array(box, dtype=np.float32)
+            width = int(max(np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[2] - box[3])))
+            height = int(max(np.linalg.norm(box[0] - box[3]), np.linalg.norm(box[1] - box[2])))
+            dst_rect = np.array([
+                [0, 0],
+                [width - 1, 0],
+                [width - 1, height - 1],
+                [0, height - 1]
+            ], dtype=np.float32)
+            M = cv2.getPerspectiveTransform(box, dst_rect)
+            warped = cv2.warpPerspective(image_np, M, (width, height))
+            cropped_images.append(Image.fromarray(warped).convert("RGB"))
+        cropped_images.reverse()
+
+    recognized_texts = []
+    if cropped_images:
+        for crop_img in cropped_images:
+            pixel_values = processor(images=crop_img, return_tensors="pt").pixel_values.to(device)
+            with torch.no_grad():
+                generated_ids = model.generate(pixel_values, max_new_tokens=64)
+                generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+                recognized_texts.append(generated_text)
+    else:
+        pixel_values = processor(images=pil_img, return_tensors="pt").pixel_values.to(device)
+        with torch.no_grad():
+            generated_ids = model.generate(pixel_values, max_new_tokens=64)
+            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+            recognized_texts.append("No text boxes detected. Full image OCR:\n" + generated_text)
+
+    return "\n".join(recognized_texts)
+
+
+# --- Interface Function ---
+def ocr_from_canvas(img):
+    if img is None:
+        return "Draw something to see OCR output."
+    np_img = np.array(img)
+    try:
+        result = recognize_handwritten_text_from_npimg(np_img)
+    except Exception as e:
+        result = f"[OCR error: {e}]"
+    return result
+
+
+# --- UI Layout ---
+with gr.Blocks(css=".gr-textbox textarea { font-family: monospace; font-size: 16px; }") as demo:
+    gr.Markdown("<h1>📝 Real-Time Handwriting OCR Canvas</h1>")
+    
+    with gr.Row():
+        with gr.Column():
+            canvas = gr.ImageEditor(
+                label="Draw here (freehand, line, shapes)",
+                type="numpy",
+                tool="freedraw",
+                width=600,
+                height=400,
+                brush=gr.Brush(color="#000000", size=3),
+                background="#FFFFFF"
+            )
+            gr.Markdown(
+                """
+                - Use the canvas tools to draw freely, lines, rectangles, etc.
+                - You can adjust stroke width, brush color, and background color.
+                - The OCR will trigger every 4 seconds or when you draw.
+                """
+            )
+
+        with gr.Column():
+            output_text = gr.Textbox(
+                label="🧠 OCR Output",
+                lines=12,
+                max_lines=20,
+                interactive=False,
+            )
+
+    # Trigger OCR on change
+    canvas.change(fn=ocr_from_canvas, inputs=canvas, outputs=output_text)
+
+demo.launch()