basic implementation of a chat app with message flagging

.python-version

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+3.11

.vscode/launch.json

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+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

app.py

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+import gradio as gr
+from datasets import Dataset
+from pandas import DataFrame
+
+DATASET_REPO_ID = "username/dataset-name"
+
+
+def add_user_message(history, message):
+    """Add a user message to the chat history"""
+    if message["text"] is not None:
+        content = message["text"]
+        history.append(gr.ChatMessage(role="user", content=content))
+    return history, gr.Textbox(value=None, interactive=False)
+
+
+def respond_system_message(history: list):
+    """Respond to the user message with a system message"""
+
+    ##############################
+    # FAKE RESPONSE
+    response = "**That's cool!**"
+    ##############################
+
+    # TODO: Add a response to the user message
+
+    message = gr.ChatMessage(role="assistant", content=response)
+    history.append(message)
+    return history
+
+
+def wrangle_like_data(x: gr.LikeData, history) -> DataFrame:
+    """Wrangle conversations and liked data into a DataFrame"""
+
+    liked_index = x.index[0]
+
+    output_data = []
+
+    for idx, message in enumerate(history):
+        if idx == liked_index:
+            message["liked"] = x.liked
+        else:
+            message["liked"] = False
+
+        output_data.append(dict(message))
+
+        del message["metadata"]
+
+    return DataFrame(data=output_data)
+
+
+def submit_conversation(dataframe):
+    """ "Submit the conversation to dataset repo"""
+    print(dataframe)
+    # TODO: Submit the conversation to the dataset repo
+    # Dataset.from_pandas(dataframe).push_to_hub(repo_id=DATASET_REPO_ID)
+
+
+with gr.Blocks(
+    css="""
+    button[aria-label="dislike"] {
+        display: none;
+    }
+    button[aria-label="like"] {
+        width: auto;
+    }
+    button[aria-label="like"] svg {
+        display: none;
+    }
+    button[aria-label="like"]::before {
+        content: "⛔️";
+        font-size: 1.5em;
+        display: inline-block;
+    }
+    """
+) as demo:
+
+    ##############################
+    # Chatbot
+    ##############################
+
+    chatbot = gr.Chatbot(
+        elem_id="chatbot",
+        bubble_full_width=False,
+        type="messages",
+    )
+
+    chat_input = gr.Textbox(
+        interactive=True,
+        placeholder="Enter a message...",
+        show_label=True,
+    )
+
+    chat_msg = chat_input.submit(
+        fn=add_user_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input]
+    )
+
+    bot_msg = chat_msg.then(
+        respond_system_message, chatbot, chatbot, api_name="bot_response"
+    )
+
+    bot_msg.then(lambda: gr.Textbox(interactive=True), None, [chat_input])
+
+    ##############################
+    # Deal with feedback
+    ##############################
+
+    dataframe = gr.DataFrame()
+
+    chatbot.like(
+        fn=wrangle_like_data,
+        inputs=[chatbot],
+        outputs=[dataframe],
+        like_user_message=False,
+    )
+
+    gr.Button().click(fn=submit_conversation, inputs=[dataframe], outputs=None)
+
+demo.launch()

pyproject.toml

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+[project]
+name = "ohp"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "datasets>=3.1.0",
+    "gradio>=5.6.0",
+]