feat: Add two sample gradio tools

app.py

@@ -1,64 +1,15 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+from tdagent.tools.get_url_content import gr_get_url_http_content
+from tdagent.tools.letter_counter import gr_letter_counter
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+gr_app = gr.TabbedInterface(
+    [
+        gr_get_url_http_content,
+        gr_letter_counter,
     ],
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    gr_app.launch(mcp_server=True)

tdagent/tools/get_url_content.py

@@ -0,0 +1,65 @@
+import enum
+from collections.abc import Sequence
+
+import gradio as gr
+import requests
+
+
+class HttpContentType(str, enum.Enum):
+    """Http content type values."""
+
+    HTML = "text/html"
+    JSON = "application/json"
+
+
+def get_url_http_content(
+    url: str,
+    content_type: Sequence[HttpContentType] | None = None,
+    timeout: int = 30,
+) -> tuple[str, str]:
+    """Get the content of a URL using an HTTP GET request.
+
+    Args:
+        url: The URL to fetch the content from.
+        content_type: If given it should contain the expected
+            content types in the response body. The server may
+            not honor the requested content types.
+        timeout: Request timeout in seconds. Defaults to 30.
+
+    Returns:
+        A pair of strings (content, error_message). If there is an
+        error getting content from the URL the `content` will be
+        empty and `error_message` will, usually, contain the error
+        cause. Otherwise, `error_message` will be empty and the
+        content will be filled with data fetched from the URL.
+    """
+    headers = {}
+
+    if content_type:
+        headers["Accept"] = ",".join(content_type)
+    response = requests.get(
+        url,
+        headers=headers,
+        timeout=timeout,
+    )
+
+    try:
+        response.raise_for_status()
+    except requests.HTTPError as err:
+        return "", str(err)
+
+    return response.text, ""
+
+
+gr_get_url_http_content = gr.Interface(
+    fn=get_url_http_content,
+    inputs=["text", "text"],
+    outputs="text",
+    title="Get the content of a URL using an HTTP GET request.",
+    description=(
+        "Get the content of a URL in one of the specified content types."
+        " The server may not honor the content type and if it fails the"
+        " reason should also be returned with the corresponding HTTP"
+        " error code."
+    ),
+)

tdagent/tools/letter_counter.py

@@ -0,0 +1,23 @@
+import gradio as gr
+
+
+def letter_counter(word: str, letter: str) -> int:
+    """Count the occurrences of a specific letter in a word.
+
+    Args:
+        word: The word or phrase to analyze
+        letter: The letter to count occurrences of
+
+    Returns:
+        The number of times the letter appears in the word
+    """
+    return word.lower().count(letter.lower())
+
+
+gr_letter_counter = gr.Interface(
+    fn=letter_counter,
+    inputs=["text", "text"],
+    outputs="number",
+    title="Letter Counter",
+    description="Count how many times a letter appears in a word",
+)