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+FROM python:3.11-slim-bullseye as builder
+
+# Build dummy packages to skip installing them and their dependencies
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends equivs \
+    && equivs-control libgl1-mesa-dri \
+    && printf 'Section: misc\nPriority: optional\nStandards-Version: 3.9.2\nPackage: libgl1-mesa-dri\nVersion: 99.0.0\nDescription: Dummy package for libgl1-mesa-dri\n' >> libgl1-mesa-dri \
+    && equivs-build libgl1-mesa-dri \
+    && mv libgl1-mesa-dri_*.deb /libgl1-mesa-dri.deb \
+    && equivs-control adwaita-icon-theme \
+    && printf 'Section: misc\nPriority: optional\nStandards-Version: 3.9.2\nPackage: adwaita-icon-theme\nVersion: 99.0.0\nDescription: Dummy package for adwaita-icon-theme\n' >> adwaita-icon-theme \
+    && equivs-build adwaita-icon-theme \
+    && mv adwaita-icon-theme_*.deb /adwaita-icon-theme.deb
+
+FROM python:3.11-slim-bullseye
+COPY --from=builder /*.deb /
+WORKDIR /app
+RUN echo "deb http://deb.debian.org/debian/ unstable main contrib non-free" >> /etc/apt/sources.list
+RUN apt update
+RUN apt install -y python3 python3-pip libgl1-mesa-glx wget libglib2.0-dev
+RUN pip install playwright hcaptcha_challenger
+RUN playwright install firefox --with-deps
+
+RUN dpkg -i /libgl1-mesa-dri.deb \
+    && dpkg -i /adwaita-icon-theme.deb \
+    # Install dependencies
+    && apt-get update \
+    && apt-get install -y --no-install-recommends xvfb dumb-init \
+        procps curl vim xauth \
+    # Remove temporary files and hardware decoding libraries
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -f /usr/lib/x86_64-linux-gnu/libmfxhw* \
+    && rm -f /usr/lib/x86_64-linux-gnu/mfx/* \
+    && useradd --home-dir /app --shell /bin/sh foxer \
+    && chown -R foxer:foxer .
+
+RUN rm -rf /root/.cache
+USER foxer
+
+RUN mkdir -p "/app/.config/Ultralytics"
+
+COPY main.py .
+EXPOSE 7860
+
+ENTRYPOINT ["/usr/bin/dumb-init", "--"]
+CMD ["/usr/local/bin/python","-u", "/app/main.py"]
+

api.py

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+import os
+import model_process
+from flask import Flask, jsonify, request, logging as flog
+from flask_limiter.util import get_remote_address
+
+app = Flask(__name__)
+
+
+def get_ipaddr():
+    if request.access_route:
+        print(request.access_route[0])
+        return request.access_route[0]
+    else:
+        return request.remote_addr or '127.0.0.1'
+
+
+handler = flog.default_handler
+
+
+def get_token():
+    default_token = "init_token"
+    if os.path.exists("token"):
+        return open("token", "r").read().strip()
+    return default_token
+
+
+def check_request(required_data, data):
+    token = get_token()
+    if not data or any(key not in data for key in required_data):
+        print("Error:Invalid Request Data\n" + str(data))
+        return False
+    if data["token"] != token:
+        print("Error:Invalid Token\n" + str(data))
+        return False
+    return True
+
+
+@app.errorhandler(429)
+def rate_limit_exceeded(e):
+    print(get_remote_address())
+    return jsonify(msg="Too many request"), 429
+
+
+@app.errorhandler(405)
+def method_not_allowed(e):
+    print(get_remote_address())
+    return jsonify(msg="Unauthorized Request"), 405
+
+
+@app.route("/", methods=["GET"])
+def index():
+    return jsonify(status_code=200, ip=get_ipaddr())
+
+
+@app.route("/update/token", methods=["POST"])
+def update_token():
+    require_data = ["token", "new_token"]
+    data = request.get_json(force=True, silent=True)
+    if not check_request(require_data, data):
+        return jsonify(msg="Unauthorized Request"), 403
+    token = open("token", "w+")
+    token.write(data["new_token"])
+    token.close()
+    return jsonify(msg="Token updated successfully", success=True)
+
+
+@app.route("/api/solve", methods=["POST"])
+def solver_captcha():
+    require_data = ["token", "data", "question"]
+    data = request.get_json(force=True, silent=True)
+    if not check_request(require_data, data):
+        return jsonify(msg="Unauthorized Request"), 403
+    try:
+        return model_process.get_result(data["question"], data["data"], data.get("example"))
+    except Exception as e:
+        print(e)
+        return "error", 500
+
+
+app.run(host="0.0.0.0", port=8081)

model_process.py

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+import hashlib
+import os
+import random
+import string
+import open_clip
+import requests
+import torch
+import shutil
+from PIL import Image
+
+model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
+tokenizer = open_clip.get_tokenizer('ViT-B-32')
+
+
+def generate_random_string_and_hash(length=8):
+    # 生成随机字符串
+    letters = string.ascii_letters
+    random_string = ''.join(random.choice(letters) for i in range(length))
+
+    # 生成哈希值
+    hash_value = hashlib.sha256(random_string.encode()).hexdigest()
+
+    return hash_value
+
+
+def process_img(image_input, text_inputs, classes):
+    with torch.no_grad():
+        image_features = model.encode_image(image_input)
+        text_features = model.encode_text(text_inputs)
+
+    # Pick the top 5 most similar labels for the image
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+    similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+    value, index = similarity[0].topk(1)
+    class_name = classes[index]
+    return class_name
+
+
+def get_result(question, data, example=None):
+    global model, preprocess, tokenizer
+    model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
+    tokenizer = open_clip.get_tokenizer('ViT-B-32')
+    sess = requests.session()
+    result = []
+    dir_path = generate_random_string_and_hash()
+    os.makedirs(f"temp/{dir_path}", exist_ok=True)
+    raw_answer = sess.get("https://yundisk.de/d/OneDrive_5G/Pic/data.json").json()
+    if question in raw_answer:
+        classes = raw_answer["classes"]
+        text_inputs = torch.cat([tokenizer(f"a photo of {c}") for c in classes])
+        if raw_answer["need_example"]:
+            if example:
+                example_file_path = f"{generate_random_string_and_hash()}.png"
+                with open(f"temp/{dir_path}/{example_file_path}", "wb+") as f:
+                    f.write(sess.get(example).content)
+                example = preprocess(Image.open(f"temp/{dir_path}/{example_file_path}")).unsqueeze(0)
+                answer = [process_img(example, text_inputs, classes)]
+            else:
+                print(question)
+                return None
+        else:
+            answer = raw_answer["answer"]
+        for img in data:
+            img_path = f"{generate_random_string_and_hash()}.png"
+            with open(f"temp/{dir_path}/{img_path}", "wb+") as f:
+                f.write(sess.get(img).content)
+            img = preprocess(Image.open(f"temp/{dir_path}/{img_path}")).unsqueeze(0)
+            class_name = process_img(img, text_inputs, classes)
+            result.append(class_name in answer)
+    shutil.rmtree(f"temp/{dir_path}")
+    return result