Merge pull request #5 from doccano/release/v0.0.3

Release/v0.0.3

Author: Hironsan

README.md

@@ -2,8 +2,6 @@
 
 doccano-mini is a few-shot annotation tool to assist the development of applications with Large language models (LLMs). Once you annotate a few text, you can test your task (e.g. text classification) with LLMs, then download the [LangChain](https://github.com/hwchase17/langchain)'s config.
 
-![Demo](./docs/images/demo.jpg)
-
 Note: This is an experimental project.
 
 ## Installation
@@ -28,6 +26,24 @@ doccano-mini
 
 Now, we can open the browser and go to `http://localhost:8501/` to see the interface.
 
+### Step1: Annotate a few text
+
+In this step, we will annotate a few text. We can add a new text by clicking the `+` button. Try it out by double-clicking on any cell. You'll notice you can edit all cell values.
+
+![Step1](./docs/images/annotation.gif)
+
+### Step2: Test your task
+
+In this step, we will test your task. We can enter a new test to the text box and click the `Predict` button. Then, we can see the result of the test.
+
+![Step2](./docs/images/test_new_example.jpg)
+
+### Step3: Download the config
+
+In this step, we will download the [LangChain](https://github.com/hwchase17/langchain)'s config. We can click the `Download` button to download it.
+
+![Step3](./docs/images/download_config.jpg)
+
 ## Development
 
 ```bash

doccano_mini/app.py

@@ -42,9 +42,8 @@ def main():
         prompt.prefix = instruction
 
     st.header("Test")
-    text = st.text_area(label="Please enter your text.", value="")
-
-    st.header("Model Options")
+    col1, col2 = st.columns([3, 1])
+    text = col1.text_area(label="Please enter your text.", value="", height=300)
 
     # https://platform.openai.com/docs/models/gpt-3-5
     available_models = (
@@ -54,13 +53,10 @@ def main():
         "text-davinci-002",
         "code-davinci-002",
     )
-
     # Use text-davinci-003 by default.
-    model_name = st.selectbox("Select an OpenAI model to use.", available_models, index=2)
-
-    temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.01)
-
-    top_p = st.slider("Top-p", min_value=0.0, max_value=1.0, value=1.0, step=0.01)
+    model_name = col2.selectbox("Model", available_models, index=2)
+    temperature = col2.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.01)
+    top_p = col2.slider("Top-p", min_value=0.0, max_value=1.0, value=1.0, step=0.01)
 
     if st.button("Predict"):
         llm = OpenAI(model_name=model_name, temperature=temperature, top_p=top_p)

docs/images/annotation.gif

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "doccano-mini"
-version = "0.0.2"
+version = "0.0.3"
 description = "Generate LangChain config quickly"
 authors = ["Hironsan <[email protected]>"]
 license = "MIT"