Create app.py

app.py

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+# This file is .....
+# Author: Hanbin Wang
+# Date: 2023/4/18
+import transformers
+import streamlit as st
+from PIL import Image
+
+from transformers import RobertaTokenizer, T5ForConditionalGeneration
+from transformers import pipeline
+
+@st.cache_resource
+def get_model(model_path):
+    tokenizer = RobertaTokenizer.from_pretrained(model_path)
+    model = T5ForConditionalGeneration.from_pretrained(model_path)
+    model.eval()
+    return tokenizer, model
+
+
+def main():
+    # `st.set_page_config` is used to display the default layout width, the title of the app, and the emoticon in the browser tab.
+
+    st.set_page_config(
+        layout="centered", page_title="MaMaL-Gen Demo(代码生成)", page_icon="❄️"
+    )
+
+    c1, c2 = st.columns([0.32, 2])
+
+    # The snowflake logo will be displayed in the first column, on the left.
+
+    with c1:
+        st.image(
+            "./panda23.png",
+            width=100,
+        )
+
+    # The heading will be on the right.
+
+    with c2:
+        st.caption("")
+        st.title("MaMaL-Gen(代码生成)")
+
+
+    ############ SIDEBAR CONTENT ############
+
+    st.sidebar.image("./panda23.png",width=270)
+
+    st.sidebar.markdown("---")
+
+    st.sidebar.write(
+    """
+    ## 使用方法：
+    在【输入】文本框输入自然语言，点击【生成】按钮,即会生成想要的代码。
+    """
+    )
+
+    st.sidebar.write(
+    """
+    ## 注意事项：
+    1）APP托管在外网上，请确保您可以全局科学上网。
+    
+    2）您可以下载[MaMaL-Gen](https://huggingface.co/hanbin/MaMaL-Gen)模型，本地测试。（无需科学上网）
+    """
+    )
+    # For elements to be displayed in the sidebar, we need to add the sidebar element in the widget.
+
+    # We create a text input field for users to enter their API key.
+
+    # API_KEY = st.sidebar.text_input(
+    #     "Enter your HuggingFace API key",
+    #     help="Once you created you HuggingFace account, you can get your free API token in your settings page: https://huggingface.co/settings/tokens",
+    #     type="password",
+    # )
+    #
+    # # Adding the HuggingFace API inference URL.
+    # API_URL = "https://api-inference.huggingface.co/models/valhalla/distilbart-mnli-12-3"
+    #
+    # # Now, let's create a Python dictionary to store the API headers.
+    # headers = {"Authorization": f"Bearer {API_KEY}"}
+
+
+    st.sidebar.markdown("---")
+
+    st.write(
+        "> **Tip：** 首次运行需要加载模型，可能需要一定的时间！"
+    )
+
+    st.write(
+        "> **Tip：** 左侧栏给出了一些good case 和 bad case，you can try it！"
+    )
+    st.write(
+        "> **Tip：** 只支持英文输入，输入过长，效果会变差。只支持Python语言"
+    )
+
+    st.sidebar.write(
+        "> **Good case：**"
+    )
+    code_good = """1)Convert a SVG string to a QImage
+2)Try to seek to given offset"""
+    st.sidebar.code(code_good, language='python')
+
+    st.sidebar.write(
+        "> **Bad cases：**"
+    )
+    code_bad = """Read an OpenAPI binary file ."""
+    st.sidebar.code(code_bad, language='python')
+
+    # Let's add some info about the app to the sidebar.
+
+    st.sidebar.write(
+    """
+    App 由 东北大学NLP课小组成员创建， 使用 [Streamlit](https://streamlit.io/)🎈 和 [HuggingFace](https://huggingface.co/inference-api)'s [MaMaL-Gen](https://huggingface.co/hanbin/MaMaL-Gen) 模型.
+    """
+    )
+
+    # model, tokenizer = load_model("hanbin/MaMaL-Gen")
+    st.write("### 输入：")
+    input = st.text_area("", height=100)
+    button = st.button('生成')
+
+    tokenizer,model = get_model("hanbin/MaMaL-Gen")
+
+    input_ids = tokenizer(input, return_tensors="pt").input_ids
+    generated_ids = model.generate(input_ids, max_length=100)
+    output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    # generator = pipeline('text-generation', model="E:\DenseRetrievalGroup\CodeT5-base")
+    # output = generator(input)
+    # code = '''def hello():
+    #     print("Hello, Streamlit!")'''
+    if button:
+        st.write("### 输出：")
+        st.code(output, language='python')
+    else:
+        st.write('####         输出位置~~')
+
+
+
+
+
+if __name__ == '__main__':
+
+    main()