updated tools description

agent.py

@@ -161,6 +161,7 @@ def get_agent():
             model_id="Qwen/Qwen2.5-VL-32B-Instruct",  # provider="together"
         ),
         max_steps=3,
+        additional_authorized_imports=["pandas", "numpy", "openpyxl"],
         name="Multimedia_Agent",
         description="An agent that can process and analyze images, audio, video, and other files. It needs to be provided with a valid URL to fetch the file.",
         verbosity_level=1,
@@ -192,7 +193,7 @@ def get_agent():
         model=OpenAIServerModel(model_id="gpt-4.1", temperature=0.1),
         max_steps=5,
         planning_interval=10,
-        additional_authorized_imports=["pandas", "numpy"],
+        additional_authorized_imports=["pandas", "numpy", "openpyxl"],
         verbosity_level=2,
     )
 

app.py

@@ -109,7 +109,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data[3:4]:
+    for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:

tools.py

@@ -551,13 +551,35 @@ def process_binary(response, filename, content_type):
 @tool
 def fetch_task_files(url: str) -> Dict[str, Any]:
     """
-    Download files from a given URL.
+    Download and process files from a given URL. This tool detects the file type and returns the data in a suitable format for further processing.
+
+    For different file types, this tool returns:
+
+    - Images: Returns a data_url that can be directly used with the read_image tool
+      Example: result = fetch_task_files(url); then use read_image(question, result["data_url"])
+
+    - Audio: Returns audio data that can be used with the transcribe_audio tool
+      Example: result = fetch_task_files(url); then use transcribe_audio(result["data_url"], result["filename"])
+
+    - Video: Returns frame extractions and a video buffer for processing with run_video
+      Example: result = fetch_task_files(url); you can access frames via result["frame_urls"]
+
+    - Tabular data (Excel/CSV): Returns parsed data as records, column names, and dimensions
+      Example: result = fetch_task_files(url); then analyze data with result["data"] and result["columns"]
+
+    - Text/Code: Returns the content as text for analysis
+      Example: result = fetch_task_files(url); then access text via result["content"]
+
+    - PDFs & other files: Returns encoded file data for processing
+
+    All responses include metadata like file_type, filename, and content_type to help determine how to handle the file.
 
     Args:
-        url (str): Direct URL to the file to download.
+        url (str): Direct URL to the file to download. For task files, construct using the API base URL
+                  and the task ID (e.g., "https://agents-course-unit4-scoring.hf.space/files/{task_id}")
 
     Returns:
-        dict: A dictionary containing file information and data in appropriate format for the file type
+        dict: A dictionary with file data and metadata structured for the specific file type
     """
     files_url = url
     print(f"Fetching file from: {files_url}")