first commit

Dockerfile

@@ -0,0 +1,30 @@
+FROM docker.io/library/python:3.10
+
+RUN apt-get update && apt-get install -y git git-lfs ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx && rm -rf /var/lib/apt/lists/* && git lfs install
+
+RUN pip install --no-cache-dir pip==22.3.1 && pip install --no-cache-dir datasets "huggingface-hub>=0.12.1" "protobuf<4" "click<8.1" "pydantic~=1.0" gradio[oauth]==3.44.1 uvicorn>=0.14.0 spaces==0.18.0
+
+WORKDIR /home/user/app
+
+RUN useradd -m -u 1000 user
+
+COPY --chown=1000 ./ /home/user/app
+
+RUN if [ -f ./packages.txt ]; then \
+    apt-get update && xargs -r -a ./packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*; \
+else \
+    echo "packages.txt not found!"; \
+fi
+
+RUN if [ -f ./requirements.txt ]; then \
+    pip install --no-cache-dir -r ./requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple; \
+else \
+    echo "requirements.txt not found!"; \
+fi
+
+ENV PYTHONUNBUFFERED=1
+
+EXPOSE 7860
+
+# ENTRYPOINT ["python", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+

app-download-model.py

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+import os
+import subprocess
+import gradio as gr
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# 下载模型
+base_dir = "/root/.cache/huggingface/hub"
+if not os.path.isdir(base_dir):
+    os.makedirs(base_dir)
+
+cmd_list = ["cd", base_dir, "&&", "git lfs install", "&&", "git clone", "https://gitee.com/lanzhiwang/gpt2.git", "models"]
+cmd_str = " ".join(cmd_list)
+print("cmd_str:", cmd_str)
+ret, out = subprocess.getstatusoutput(cmd_str)
+print("ret:", ret)
+print("out:", out)
+
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path="/root/.cache/huggingface/hub/models")
+model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="/root/.cache/huggingface/hub/models")
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+# generator = pipeline('text-generation', model='gpt2')
+
+def generate(text):
+    result = generator(text, max_length=30, num_return_sequences=1)
+    return result[0]["generated_text"]
+
+examples = [
+    ["The Moon's orbit around Earth has"],
+    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
+]
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Generated Text"),
+    examples=examples
+)
+
+demo.launch(server_name="0.0.0.0", server_port=7860)

app.py

@@ -1,22 +1,13 @@
-import os
-import subprocess
-import logging
-
 import gradio as gr
 from transformers import pipeline
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# 下载模型
-
-
-
 # Load model directly
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = AutoModelForCausalLM.from_pretrained("gpt2")
 
-
 generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
 
 # generator = pipeline('text-generation', model='gpt2')

learn/app1.py

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+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!"
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)