Create app.py

app.py

@@ -0,0 +1,43 @@
+import os
+import subprocess
+import logging
+
+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)