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README.md

@@ -1,12 +1,12 @@
----
-title: Ppf Test
-emoji: 💻
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 6.3.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: ppf-captcha
+emoji: 🔥
+colorFrom: gray
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.6.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,152 @@
+import io
+import time
+import os
+import re
+import numpy as np
+from PIL import Image, ImageFilter
+from cairosvg import svg2png
+from transformers import VisionEncoderDecoderModel, TrOCRProcessor
+import gradio as gr
+
+processor = TrOCRProcessor.from_pretrained("anuashok/ocr-captcha-v3")
+model = VisionEncoderDecoderModel.from_pretrained("anuashok/ocr-captcha-v3")
+os.makedirs("outputs", exist_ok=True)
+
+
+def _single_ocr_from_image(image: Image.Image) -> str:
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+    generated_ids = model.generate(pixel_values)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    sanitized = re.sub(r'[^A-Z0-9]', '', generated_text).upper()
+    return sanitized[:4]
+
+
+def solve_svg_captcha(svg_data: str) -> str:
+    svg = svg_data or ""
+    svg = re.sub(r'<style>.*?</style>', '', svg, flags=re.DOTALL)
+    svg = svg.replace('file:///', '')
+    svg = svg.replace('/app/', '')
+    svg = re.sub(r'url\(["\']?\/?app\/[^)"\']*["\']?\)', 'url()', svg)
+
+    svg_static = re.sub(r'<animateTransform\b[^>]*>(?:.*?</animateTransform>)?', '', svg, flags=re.DOTALL)
+
+    rotate_re = re.compile(r'rotate\(\s*([+-]?\d+)\s*,\s*([0-9.]+)\s*,\s*([0-9.]+)\s*\)')
+    matches = rotate_re.findall(svg_static)
+    centers = []
+    seen = set()
+    for _, cx, cy in matches:
+        key = f"{cx},{cy}"
+        if key not in seen:
+            seen.add(key)
+            centers.append((cx, cy))
+
+    if not centers:
+        try:
+            png_bytes = svg2png(bytestring=svg_static.encode('utf-8'))
+            image = Image.open(io.BytesIO(png_bytes)).convert("RGBA")
+            image = image.resize((600, 400))
+            background = Image.new("RGBA", image.size, (255, 255, 255))
+            combined = Image.alpha_composite(background, image).convert("RGB")
+            return _single_ocr_from_image(combined)
+        except Exception as e:
+            print("OCR fallback error:", e)
+            return ""
+
+    centers = centers[:2]
+    angle_step = 15
+    top_k = 2
+    best_angles = {}
+
+    for cx, cy in centers:
+        metrics = []
+        for angle in range(0, 360, angle_step):
+            try:
+                tmp = re.sub(rf'rotate\(\s*1\s*,\s*{re.escape(cx)}\s*,\s*{re.escape(cy)}\s*\)', f'rotate({angle}, {cx}, {cy})', svg_static)
+                tmp = re.sub(rf'rotate\(\s*-1\s*,\s*{re.escape(cx)}\s*,\s*{re.escape(cy)}\s*\)', f'rotate(-{angle}, {cx}, {cy})', tmp)
+                png_bytes = svg2png(bytestring=tmp.encode('utf-8'))
+                img = Image.open(io.BytesIO(png_bytes)).convert('L')
+                img = img.resize((600, 400))
+                img = img.filter(ImageFilter.GaussianBlur(radius=1))
+                edges = img.filter(ImageFilter.FIND_EDGES)
+                arr = np.array(edges)
+                edge_count = int((arr > 10).sum())
+                metrics.append((edge_count, angle))
+            except Exception:
+                continue
+        metrics.sort(key=lambda x: x[0])
+        picked = [m[1] for m in metrics[:top_k]] if metrics else [0] * top_k
+        if len(picked) < top_k:
+            picked += [picked[0]] * (top_k - len(picked))
+        best_angles[f"{cx},{cy}"] = picked
+
+    combos = []
+    if len(centers) == 1:
+        k = f"{centers[0][0]},{centers[0][1]}"
+        a1, a2 = best_angles[k][:2]
+        combos = [{k: a1}, {k: a2}, {k: a1}, {k: a2}]
+    else:
+        k0 = f"{centers[0][0]},{centers[0][1]}"
+        k1 = f"{centers[1][0]},{centers[1][1]}"
+        a1, a2 = best_angles[k0][:2]
+        b1, b2 = best_angles[k1][:2]
+        combos = [
+            {k0: a1, k1: b1},
+            {k0: a2, k1: b1},
+            {k0: a1, k1: b2},
+            {k0: a2, k1: b2},
+        ]
+
+    images = []
+    for combo in combos:
+        tmp = svg_static
+        for key, angle in combo.items():
+            cx, cy = key.split(',')
+            tmp = re.sub(rf'rotate\(\s*1\s*,\s*{re.escape(cx)}\s*,\s*{re.escape(cy)}\s*\)', f'rotate({angle}, {cx}, {cy})', tmp)
+            tmp = re.sub(rf'rotate\(\s*-1\s*,\s*{re.escape(cx)}\s*,\s*{re.escape(cy)}\s*\)', f'rotate(-{angle}, {cx}, {cy})', tmp)
+        try:
+            png_bytes = svg2png(bytestring=tmp.encode('utf-8'))
+            img = Image.open(io.BytesIO(png_bytes)).convert("RGBA")
+            img = img.resize((600, 400))
+            background = Image.new("RGBA", img.size, (255, 255, 255))
+            combined = Image.alpha_composite(background, img).convert("RGB")
+            images.append(combined)
+        except Exception:
+            continue
+
+    ocr_results = []
+    for img in images:
+        try:
+            txt = _single_ocr_from_image(img)
+            ocr_results.append(txt)
+        except Exception:
+            ocr_results.append("")
+
+    for r in ocr_results:
+        if len(r) == 4:
+            return r
+    if ocr_results:
+        best = max(ocr_results, key=lambda x: len(x or ""))
+        return best or ""
+    return ""
+
+def predict(svgdata):
+    if not svgdata:
+        return "No SVG provided"
+    if len(svgdata) > 50000:
+        return "SVG too large"
+    try:
+        model_answer = solve_svg_captcha(svgdata)
+    except Exception as e:
+        print(f"Error in predict: {e}")
+        return "Model could not predict"
+    return model_answer or "Model could not predict"
+
+with gr.Blocks() as demo:
+    gr.Markdown("Enter SVG data and receive model answer")
+    svg_input = gr.Textbox(label="SVG Data", lines=10)
+    predict_btn = gr.Button("Get Model Answer")
+    model_answer = gr.Textbox(label="Model Answer", interactive=False)
+    predict_btn.click(predict, inputs=[svg_input], outputs=[model_answer])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+cairosvg==2.7.1
+transformers>=4.30.0
+torch>=2.0.0
+Pillow>=9.0.0
+gradio>=3.0.0
+hf_xet