Update tools.py

tools.py

@@ -32,7 +32,13 @@ import wikipedia
 @tool
 def wiki_search(question: str) -> str:
     """
-    Search Wikipedia and return a short summary based on the input question.
+    Perform a Wikipedia search using the input question and return a summary.
+
+    Args:
+        question (str): The user's query to search on Wikipedia.
+
+    Returns:
+        str: A brief summary of the top Wikipedia result.
     """
     wikipedia.set_lang("en")
     try:
@@ -51,7 +57,13 @@ from youtube_transcript_api import YouTubeTranscriptApi
 @tool
 def youtube_transcript(question: str) -> str:
     """
-    Get the video fromyoutube and return a ttranscription of it.
+    Extract and return the transcript of a YouTube video based on the input URL or query.
+
+    Args:
+        question (str): A YouTube video link or video ID.
+
+    Returns:
+        str: The extracted transcript text from the video.
     """
     match = re.search(r"(?:v=|youtu\.be/)([\w\-]{11})", question)
     if not match:
@@ -68,7 +80,13 @@ def youtube_transcript(question: str) -> str:
 @tool
 def reverse_string(question: str) -> str:
     """
-    Reverse the input question and return it.
+    Reverse the input string.
+
+    Args:
+        question (str): Any input string.
+
+    Returns:
+        str: The reversed string.
     """
     try:
         reversed_part = question.split('"')[1]
@@ -80,7 +98,13 @@ def reverse_string(question: str) -> str:
 @tool
 def python_repl(code: str) -> str:
     """
-    Execute a python code and return local variables.
+    Execute a Python expression or snippet and return the output.
+
+    Args:
+        question (str): A Python code snippet to evaluate.
+
+    Returns:
+        str: The output or result of the executed code.
     """
     try:
         local_vars = {}
@@ -97,7 +121,13 @@ asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-ti
 @tool
 def speech_recognition(audio_path: str) -> str:
     """
-    Extract a text from the input audio file and return a text.
+    Transcribe audio from a file path or URL to text.
+
+    Args:
+        question (str): Path or URL to an audio file.
+
+    Returns:
+        str: Transcribed text from the audio.
     """
     try:
         result = asr_pipeline(audio_path)
@@ -111,7 +141,13 @@ import pandas as pd
 @tool
 def excel_parser_tool(file_path: str) -> str:
     """
-    Sum sales of food category and return it.
+    Load an Excel file and compute the sum of values in a specified column.
+
+    Args:
+        question (str): JSON string containing the file path and column name.
+
+    Returns:
+        str: Sum of the values in the specified Excel column.
     """
     try:
         df = pd.read_excel(file_path)  # Ensure file has a 'Category' and 'Sales' column
@@ -130,7 +166,13 @@ blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image
 @tool
 def chess_image_tool(image_path: str) -> str:
     """
-    Analyze chess position on the image and return the best move.
+    Analyze a chessboard image and return the board's current state in FEN format.
+
+    Args:
+        question (str): Path or URL to the chessboard image.
+
+    Returns:
+        str: The FEN string representing the board state.
     """
     try:
         raw_image = Image.open(image_path).convert("RGB")
@@ -150,7 +192,13 @@ from bs4 import BeautifulSoup
 @tool
 def stat_api(question: str) -> str:
     """
-    Analyze statistics on the link https://www.baseball-reference.com/teams/NYY/1977.shtml and return an answer to the input question.
+    Query a public statistics API and return the requested data.
+
+    Args:
+        question (str): The query string or parameters for the API request.
+
+    Returns:
+        str: API response with the requested statistics.
     """
     try:
         # Hardcoded example for the 1977 Yankees walks leader