Update app.py

app.py

@@ -178,80 +178,8 @@ custom_role_conversions=None,
 # Import tool from Hub
 search_tool = DuckDuckGoSearchTool()
 
-# with open("prompts.yaml", 'r') as stream:
-#    prompt_templates = yaml.safe_load(stream)
-
-prompt_templates = """You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
-To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
-To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
-
-At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.
-Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '<end_code>' sequence.
-During each intermediate step, you can use 'print()' to save whatever important information you will then need.
-These print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.
-In the end you have to return a final answer using the `final_answer` tool.
-
-On top of performing computations in the Python code snippets that you create, you only have access to these tools:
-
-def search_articles(search_term: str, start: int, end: int, output_md: str = "arxiv_articles.md") -> str:
-    \"\"\"
-    Searches for academic articles on arxiv.org using a given search term, retrieves 
-    results within a given range, and saves the output as a Markdown file.
-
-    Args:
-        search_term: The keyword or phrase to search for in ArXiv articles.
-        start: The starting index of the search results (used for pagination).
-        end: The number of articles to retrieve from the API.
-        output_md: The name of the output Markdown file. Defaults to "arxiv_articles.md".
-
-    Returns:
-        str: The absolute path of the generated Markdown file, or an error message if the request fails.
-
-    Example:
-        >>> search_articles("deep learning", 0, 10)
-        "Markdown file created: /absolute/path/to/arxiv_articles.md"
-    \"\"\"
-	
-def download_articles(article_links: list, save_folder: str = "downloads") -> str:
-    \"\"\"
-    A tool that downloads articles from arxiv.org given a list of links.
-
-    Args:
-        article_links: List of article links from ArXiv.
-        save_folder: Folder to save downloaded articles. Default is 'downloads'.
-
-    Returns:
-        str: Success or error message.
-    \"\"\"
-	
-def create_zip_package(articles_folder: str, output_zip: str) -> str:
-    \"\"\"
-    A tool that packages all downloaded articles (PDFs) from a folder into a zip file.
-
-    Args:
-        articles_folder: Folder containing downloaded articles.
-        output_zip: The name of the output zip file.
-
-    Returns:
-        str: Path to the created zip file.
-    \"\"\"
-
-Here are the rules you should always follow to solve your task:
-1. Always provide a 'Thought:' sequence, and a 'Code:\\n```py' sequence ending with '```<end_code>' sequence, else you will fail.
-2. Use only variables that you have defined!
-3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wiki({'query': "What is the place where James Bond lives?"})', 
-but use the arguments directly as in 'answer = wiki(query="What is the place where James Bond lives?")'.
-4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. 
-For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the 
-same block: rather output results with print() to use them in the next block.
-5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.
-6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.
-7. Never create any notional variables in our code, as having these in your logs might derail you from the true variables.
-8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
-9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
-10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
-
-Now Begin! If you solve the task correctly, you will receive a reward of $1,000,000."""
+with open("prompts.yaml", 'r') as stream:
+    prompt_templates = yaml.safe_load(stream)
     
 agent = CodeAgent(
     model=model,