fix: updated tool list

agent_course_tools.py

@@ -1,18 +1,35 @@
+import requests
 from llama_index.tools.wikipedia import WikipediaToolSpec
 from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
-from llama_index.core.tools.tool_spec.load_and_search import (
-    LoadAndSearchToolSpec,
-)
+from llama_index.tools.code_interpreter import CodeInterpreterToolSpec
+from llama_index.core.tools import FunctionTool
 
+# wikipedia search tool
 wiki_spec = WikipediaToolSpec()
-#wiki_tool = LoadAndSearchToolSpec.from_defaults(wiki_spec.to_tool_list()[1]).to_tool_list()
-wiki_tool = wiki_spec.to_tool_list()[1]
+wiki_tool = wiki_spec.to_tool_list()
 
+# duckduckgo search tool
 ddgo_spec = DuckDuckGoSearchToolSpec()
-#ddgo_tool = LoadAndSearchToolSpec.from_defaults(ddgo_spec.to_tool_list()[1]).to_tool_list()
-ddgo_tool = ddgo_spec.to_tool_list()[1]
+ddgo_tool = ddgo_spec.to_tool_list()
+
+# execute python code and return the stdout and stderr.
+code_spec = CodeInterpreterToolSpec()
+code_tool = code_spec.to_tool_list()
+
+
+def download_file(url: str, save_path: str) -> str:
+    """Download a file from a given URL and save it to the specified path."""
+    response = requests.get(url)
+    with open(save_path, 'wb') as file:
+        file.write(response.content)
+    return f"File downloaded to (save_path)"
+
+download_tool = FunctionTool.from_defaults(
+    fn=download_file,
+    name="download_file_tool",
+    description="A tool to download files from an HTTPS API endpoint. The tool accepts two arguments: The https url path to download the file and the location to save the downloaded file. If no auguments are passed then use the default values. Default values: For 'url' use 'https://agents-course-unit4-scoring.hf.space/files/{task_id}' and for 'save_path' use the current path."
+)
+
 
-# Create the Agent with load/search tools
-#agent = OpenAIAgent.from_tools(
-#    LoadAndSearchToolSpec.from_defaults(wiki_tool).to_tool_list(), verbose=True
-#)
+# agent complete tool list
+agent_tools_list = wiki_tool + ddgo_tool + code_tool + [download_tool]

app.py

@@ -17,7 +17,8 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
-        llm = HuggingFaceLLM(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
+        #llm = HuggingFaceLLM(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
+        llm = HuggingFaceLLM(model_name="Qwen/Qwen2.5-7B")
         self.agent = AgentWorkflow.from_tools_or_functions([
             tools.wiki_tool,
             tools.ddgo_tool
@@ -26,13 +27,13 @@ class BasicAgent:
             system_prompt="You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.",
         )
 
-    def __call__(self, question: str) -> str:
+    async def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = self.agent.run(question)
+        fixed_answer = await self.agent.run(question)
         print(f"Agent returning fixed answer: {fixed_answer}")
         return fixed_answer
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+async def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
@@ -93,7 +94,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            submitted_answer = await agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
@@ -106,7 +107,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    status_update = (
+        f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    )
     print(status_update)
 
     # 5. Submit
@@ -180,7 +183,8 @@ with gr.Blocks() as demo:
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
-        fn=run_and_submit_all,
+        fn=lambda profile: asyncio.run(run_and_submit_all(profile)),
+        inputs=gr.OAuth(),
         outputs=[status_output, results_table]
     )
 
@@ -206,4 +210,4 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)

requirements.txt

@@ -8,4 +8,8 @@ llama-index-tools-wikipedia
 llama-index-llms-litellm
 llama-index-readers-file
 llama-index-tools-duckduckgo
-llama-index-llms-huggingface
+llama-index-llms-huggingface
+llama-index-llms-ollama
+llama-index-tools-code_interpreter
+#
+gradio[oauth]