User Query

I am a deep learning researcher interested in learning about Alphafold3 by Google Deepmind. To help me study, you will curate study material for me.

You will simultaneously be creating two versions of the document:

1. An html document that looks professional, polished and well formatted like a newsletter. Save this to /root/results/playground/alphafold_study.html
2. A markdown style text document (that contains the same content and will be sent via email). Save this to /root/results/playground/alphafold_study.txt

The documents should containt the following 3 sections with appropriate headings: a. Wikipedia's definition of Protein Folding (first paragraph of the content retrieved from wikipedia) b. List of news articles (upto 5) about Alphafold3 with source, title, article link, and first 140 characters followed by ellipsis for each (search on DuckDuckGo News) c. List of research papers (upto 5) about Protein Folding with Deep Learning. For each paper I want title, year-month, authors, and pdf link (search on arxiv)

Finally, read the text document and send its content as body of an email to xyz@gmail.com with the subject "Your Alphfold-3 study material curated with love by CodeNav"

Interaction 1: ↔

Before starting to write the code, I need to search for the relevant modules and functions in the phi codebase that will be used to accomplish the tasks. I will search for the Wikipedia module to get the definition of Protein Folding, the DuckDuckGo module to list news articles about Alphafold3, and the arXiv module to list research papers about Protein Folding with Deep Learning. Once I have the necessary information about these modules, I can proceed to write the code to generate the documents and send the email.

file_path: *\/wikipedia.py
file_path: *\/duckduckgo.py
file_path: *\/arxiv_toolkit.py

QUERY:
file_path: *\/wikipedia.py

CODE BLOCKS:
---
file_path=phi/tools/wikipedia.py
lines=[9, 54]
type=CLASS
content={
class WikipediaTools(Toolkit):
    """
    This class provides tools for interacting with Wikipedia, including searching Wikipedia and updating a knowledge base with search results. It inherits from a Toolkit class.
    
    Attributes:
        knowledge_base: An optional instance of WikipediaKnowledgeBase to store search results.
    """

    def __init__(self, knowledge_base: Optional[WikipediaKnowledgeBase]=None):
        """
        Initializes the WikipediaTools instance.
        
        Args:
            knowledge_base (Optional[WikipediaKnowledgeBase]): An optional knowledge base to store Wikipedia search results. If provided, it must be an instance of WikipediaKnowledgeBase.
        
        Registers the appropriate search function based on whether a knowledge base is provided.
        """
        ...

    def search_wikipedia_and_update_knowledge_base(self, topic: str) -> str:
        """
        Searches Wikipedia for a given topic, updates the knowledge base with the results, and returns the results.
        
        This function should be used when information is not already present in the knowledge base.
        
        Args:
            topic (str): The topic to search on Wikipedia and add to the knowledge base.
        
        Returns:
            str: A JSON string representing the relevant documents from the Wikipedia knowledge base.
        
        Raises:
            - If the knowledge base is not provided, it returns a message indicating the absence of a knowledge base.
            - Logs various debug messages indicating the process of adding to and searching the knowledge base.
        """
        ...

    def search_wikipedia(self, query: str) -> str:
        """
        Searches Wikipedia for a given query and returns the results.
        
        Args:
            query (str): The query to search for on Wikipedia.
        
        Returns:
            str: A JSON string representing the relevant document from Wikipedia.
        
        Raises:
            ImportError: If the `wikipedia` package is not installed, it raises an ImportError advising to install the package.
        
        Logs an info message indicating the search query.
        """
        ...
}
---
file_path=phi/knowledge/wikipedia.py
lines=[11, 31]
type=CLASS
content={
class WikipediaKnowledgeBase(AssistantKnowledge):
    topics: List[str] = []

    @property
    def document_lists(self) -> Iterator[List[Document]]:
        """Iterate over urls and yield lists of documents.
        Each object yielded by the iterator is a list of documents.

        Returns:
            Iterator[List[Document]]: Iterator yielding list of documents
        """

        for topic in self.topics:
            yield [
                Document(
                    name=topic,
                    meta_data={"topic": topic},
                    content=wikipedia.summary(topic),
                )
            ]
}
---
file_path=phi/tools/wikipedia.py
lines=[5, 6]
type=IMPORT
content={
from phi.tools import Toolkit
}
---
(9 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 0 out of 0 classes/functions found in unexpanded results (search again with the same query to see details):

QUERY:
file_path: *\/duckduckgo.py

CODE BLOCKS:
---
file_path=phi/tools/duckduckgo.py
lines=[12, 62]
type=CLASS
content={
class DuckDuckGo(Toolkit):
    def __init__(
        self,
        search: bool = True,
        news: bool = True,
        fixed_max_results: Optional[int] = None,
        headers: Optional[Any] = None,
        proxy: Optional[str] = None,
        proxies: Optional[Any] = None,
        timeout: Optional[int] = 10,
    ):
        super().__init__(name="duckduckgo")

        self.headers: Optional[Any] = headers
        self.proxy: Optional[str] = proxy
        self.proxies: Optional[Any] = proxies
        self.timeout: Optional[int] = timeout
        self.fixed_max_results: Optional[int] = fixed_max_results
        if search:
            self.register(self.duckduckgo_search)
        if news:
            self.register(self.duckduckgo_news)

    def duckduckgo_search(self, query: str, max_results: int = 5) -> str:
        """Use this function to search DuckDuckGo for a query.

        Args:
            query(str): The query to search for.
            max_results (optional, default=5): The maximum number of results to return.

        Returns:
            The result from DuckDuckGo.
        """
        logger.debug(f"Searching DDG for: {query}")
        ddgs = DDGS(headers=self.headers, proxy=self.proxy, proxies=self.proxies, timeout=self.timeout)
        return json.dumps(ddgs.text(keywords=query, max_results=(self.fixed_max_results or max_results)), indent=2)

    def duckduckgo_news(self, query: str, max_results: int = 5) -> str:
        """Use this function to get the latest news from DuckDuckGo.

        Args:
            query(str): The query to search for.
            max_results (optional, default=5): The maximum number of results to return.

        Returns:
            The latest news from DuckDuckGo.
        """
        logger.debug(f"Searching DDG news for: {query}")
        ddgs = DDGS(headers=self.headers, proxy=self.proxy, proxies=self.proxies, timeout=self.timeout)
        return json.dumps(ddgs.news(keywords=query, max_results=(self.fixed_max_results or max_results)), indent=2)
}
---
file_path=phi/tools/duckduckgo.py
lines=[1, 2]
type=IMPORT
content={
from typing import Any, Optional
}
---
file_path=phi/tools/duckduckgo.py
lines=[0, 1]
type=IMPORT
content={
import json
}
---
(All code blocks matching the query were returned.)

QUERY:
file_path: *\/arxiv_toolkit.py

CODE BLOCKS:
---
file_path=phi/tools/arxiv_toolkit.py
lines=[18, 119]
type=CLASS
content={
class ArxivToolkit(Toolkit):
    """
    This class extends Toolkit to provide functionalities for searching and reading arXiv papers. It can search for articles on arXiv, download them, and read their content.
    
    Attributes:
        client (arxiv.Client): Client to interact with the arXiv API.
        download_dir (Path): Directory path where downloaded papers will be stored.
    """

    def __init__(self, search_arxiv: bool=True, read_arxiv_papers: bool=True, download_dir: Optional[Path]=None):
        """
        Initializes the ArxivToolkit instance.
        
        Args:
            search_arxiv (bool, optional): If True, enables the search functionality. Defaults to True.
            read_arxiv_papers (bool, optional): If True, enables the reading functionality. Defaults to True.
            download_dir (Optional[Path], optional): The directory to download papers to. If None, defaults to a subdirectory "arxiv_pdfs" in the current file's directory.
        """
        ...

    def search_arxiv_and_return_articles(self, query: str, num_articles: int=10) -> str:
        """
        Searches arXiv for a given query and returns the top articles in JSON format.
        
        Args:
            query (str): The query to search arXiv for.
            num_articles (int, optional): The number of articles to return. Defaults to 10.
        
        Returns:
            str: A JSON string containing a list of articles with their title, id, authors, pdf_url, and summary.
        """
        ...

    def read_arxiv_papers(self, id_list: List[str], pages_to_read: Optional[int]=None) -> str:
        """
        Reads a list of arXiv papers specified by their IDs and returns their content.
        
        Args:
            id_list (List[str]): The list of arXiv paper IDs to read.
            pages_to_read (Optional[int], optional): The number of pages to read from each paper. If None, reads all pages. Defaults to None.
        
        Returns:
            str: A JSON string containing a list of papers with their metadata and content.
        """
        ...
}
---
file_path=phi/tools/arxiv_toolkit.py
lines=[1, 2]
type=IMPORT
content={
from pathlib import Path
}
---
file_path=phi/tools/arxiv_toolkit.py
lines=[0, 1]
type=IMPORT
content={
import json
}
---
(5 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 0 out of 0 classes/functions found in unexpanded results (search again with the same query to see details):

Interaction 2: ↔

The search results have provided the necessary classes and methods from the phi codebase to accomplish the tasks. The WikipediaTools class has a search_wikipedia method to get the definition of Protein Folding from Wikipedia. The DuckDuckGo class has a duckduckgo_news method to list news articles about Alphafold3. The ArxivToolkit class has a search_arxiv_and_return_articles method to list research papers about Protein Folding with Deep Learning.

With this information, I can now start writing the code to generate the HTML and Markdown documents. I will begin by importing the necessary classes and setting up the document structure. Then, I will proceed to fill in the content for each section as required.

import json
import os
from phi.tools.wikipedia import WikipediaTools
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.arxiv_toolkit import ArxivToolkit

# Define the paths for the HTML and Markdown documents
html_output_path = "/root/results/playground/alphafold_study.html"
markdown_output_path = "/root/results/playground/alphafold_study.txt"

# Create the HTML and Markdown documents with the appropriate headings
html_content = """
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Alphafold Study Material</title>
    <style>
        body { font-family: Arial, sans-serif; }
        h1 { color: #333; }
        h2 { color: #555; }
        .section { margin-bottom: 20px; }
        .article { margin-bottom: 10px; }
        .paper { margin-bottom: 10px; }
    </style>
</head>
<body>
    <h1>Alphafold Study Material</h1>
"""

markdown_content = "# Alphafold Study Material\n\n"

# Function to append content to both HTML and Markdown documents
def append_content(html_snippet, markdown_snippet):
    global html_content, markdown_content
    html_content += html_snippet
    markdown_content += markdown_snippet

# Function to save the content to files
def save_content():
    with open(html_output_path, 'w') as html_file:
        html_file.write(html_content)
    with open(markdown_output_path, 'w') as markdown_file:
        markdown_file.write(markdown_content)

# Initialize the tools
wikipedia_tools = WikipediaTools()
duckduckgo_tools = DuckDuckGo(news=True)
arxiv_tools = ArxivToolkit(search_arxiv=True)

# Placeholder for the next steps
# TODO: Fetch and append content for each section

# Save the initial content to files
save_content()

RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"json": <module 'json' from '/opt/conda/lib/python3.12/json/__init__.py'>, "os": <module 'os' (frozen)>, "WikipediaTools": <class 'phi.tools.wikipedia.WikipediaTools'>, "DuckDuckGo": <class 'phi.tools.duckduckgo.DuckDuckGo'>, "ArxivToolkit": <class 'phi.tools.arxiv_toolkit.ArxivToolkit'>, "html_output_path": /root/results/playground/alphafold_study.html, "markdown_output_path": /root/results/playground/alphafold_study.txt, "html_content": 
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Alphafold Study Material</title>
    <style>
        body { font-family: Arial, sans-serif; }
        h1 { color: #333; }
        h2 { color: #555; }
        .section { margin-bottom: 20px; }
        .article { margin-bottom: 10px; }
        .paper { margin-bottom: 10px; }
    </style>
</head>
<body>
    <h1>Alphafold Study Material</h1>
, "markdown_content": # Alphafold Study Material

, "append_content": <function append_content at 0x7f35fe5e74c0>, "save_content": <function save_content at 0x7f35fe14d580>, "wikipedia_tools": <WikipediaTools name=wikipedia_tools functions=['search_wikipedia']>, "duckduckgo_tools": <DuckDuckGo name=duckduckgo functions=['duckduckgo_search', 'duckduckgo_news']>, "arxiv_tools": <ArxivToolkit name=arxiv_tools functions=['search_arxiv_and_return_articles', 'read_arxiv_papers']>}

Interaction 3: ↔

The initial setup for the HTML and Markdown documents has been created successfully, and the relevant classes have been instantiated. Now, I will proceed to fetch the content for each section. I will start by using the search_wikipedia method from the WikipediaTools class to get the definition of Protein Folding. Once I have the content, I will append it to both the HTML and Markdown documents under the appropriate heading.

# Fetch Wikipedia's definition of Protein Folding
wikipedia_result = wikipedia_tools.search_wikipedia("Protein Folding")
wikipedia_content = json.loads(wikipedia_result)

# Extract the first paragraph of the content
first_paragraph = wikipedia_content.split('\n')[0]

# Append the Wikipedia content to the documents
append_content(
    html_snippet=f"<div class='section'><h2>Wikipedia's Definition of Protein Folding</h2><p>{first_paragraph}</p></div>",
    markdown_snippet=f"## Wikipedia's Definition of Protein Folding\n\n{first_paragraph}\n\n"
)

# Save the updated content to files
save_content()

RESPONSE (Execution Result):
STDOUT:
INFO     Searching wikipedia for: Protein Folding                                                                                                 

EXECUTION ERROR:
'dict' object has no attribute 'split' (File "<string>", line 6, in <module>)
 -> Error occurred when executing: first_paragraph = wikipedia_content.split('\n')[0]
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"wikipedia_result": {"content": "Protein folding is the physical process by which a protein, after synthesis by a ribosome as a linear chain of amino acids, changes from an unstable random coil into a more ordered three-dimensional structure. This structure permits the protein to become biologically functional.\nThe folding of many proteins begins even during the translation of the polypeptide chain. The amino acids interact with each other to produce a well-defined three-dimensional structure, known as the protein..., "wikipedia_content": {'content': "Protein folding is the physical process by which a protein, after synthesis by a ribosome as a linear chain of amino acids, changes from an unstable random coil into a more ordered three-dimensional structure. This structure permits the protein to become biologically functional.\nThe folding of many proteins begins even during the translation of the polypeptide chain. The amino acids interact with each other to produce a well-defined three-dimensional structure, known as the protein...}

Interaction 4: ↔

The execution error occurred because I attempted to use the split method on a dictionary object instead of a string. The wikipedia_result is already a dictionary with a key 'content' that contains the string I need. I will correct this by directly accessing the 'content' key and then splitting the string to get the first paragraph.

# Correctly extract the first paragraph of the content
first_paragraph = wikipedia_content['content'].split('\n')[0]

# Append the Wikipedia content to the documents
append_content(
    html_snippet=f"<div class='section'><h2>Wikipedia's Definition of Protein Folding</h2><p>{first_paragraph}</p></div>",
    markdown_snippet=f"## Wikipedia's Definition of Protein Folding\n\n{first_paragraph}\n\n"
)

# Save the updated content to files
save_content()

RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"first_paragraph": Protein folding is the physical process by which a protein, after synthesis by a ribosome as a linear chain of amino acids, changes from an unstable random coil into a more ordered three-dimensional structure. This structure permits the protein to become biologically functional.}

Interaction 5: ↔

The Wikipedia definition of Protein Folding has been successfully appended to both the HTML and Markdown documents. Next, I will use the duckduckgo_news method from the DuckDuckGo class to fetch news articles about Alphafold3. I will then format and append this information to the documents.

# Fetch news articles about Alphafold3
news_result = duckduckgo_tools.duckduckgo_news("Alphafold3", max_results=5)
news_articles = json.loads(news_result)

# Append the news articles to the documents
html_snippet = "<div class='section'><h2>News Articles about Alphafold3</h2>"
markdown_snippet = "## News Articles about Alphafold3\n\n"

for article in news_articles:
    title = article['title']
    source = article['source']
    article_link = article['url']
    snippet = article['snippet'][:140] + "..."
    
    html_snippet += f"<div class='article'><strong>{title}</strong><br><em>Source: {source}</em><br><a href='{article_link}'>Read more</a><br><p>{snippet}</p></div>"
    markdown_snippet += f"- **{title}**\n  - Source: {source}\n  - [Read more]({article_link})\n  - {snippet}\n\n"

html_snippet += "</div>"
append_content(html_snippet=html_snippet, markdown_snippet=markdown_snippet)

# Save the updated content to files
save_content()

RESPONSE (Execution Result):
EXECUTION ERROR:
'snippet' (File "<string>", line 13, in <module>)
 -> Error occurred when executing: snippet = article['snippet'][:140] + "..."
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"news_result": [
  {
    "date": "2024-05-08T18:18:00+00:00",
    "title": "AlphaFold3 to offer structure prediction via web browser",
    "body": "AlphaFold3 shares many capabilities with the recently published RoseTTAFold-All Atom model. Though it lacks RoseTTAFold's ability to design new proteins, AlphaFold3 has one big advantage: a new web server with a simple interface that requires no coding ...",
    "url": "https://cen.acs.org/analytical-chemistry/structural-biology/AlphaFold3-offer-structure-predictio..., "news_articles": [{'date': '2024-05-08T18:18:00+00:00', 'title': 'AlphaFold3 to offer structure prediction via web browser', 'body': "AlphaFold3 shares many capabilities with the recently published RoseTTAFold-All Atom model. Though it lacks RoseTTAFold's ability to design new proteins, AlphaFold3 has one big advantage: a new web server with a simple interface that requires no coding ...", 'url': 'https://cen.acs.org/analytical-chemistry/structural-biology/AlphaFold3-offer-structure-prediction-via/102/web/2024/0..., "html_snippet": <div class='section'><h2>News Articles about Alphafold3</h2>, "markdown_snippet": ## News Articles about Alphafold3

, "article": {'date': '2024-05-08T18:18:00+00:00', 'title': 'AlphaFold3 to offer structure prediction via web browser', 'body': "AlphaFold3 shares many capabilities with the recently published RoseTTAFold-All Atom model. Though it lacks RoseTTAFold's ability to design new proteins, AlphaFold3 has one big advantage: a new web server with a simple interface that requires no coding ...", 'url': 'https://cen.acs.org/analytical-chemistry/structural-biology/AlphaFold3-offer-structure-prediction-via/102/web/2024/05..., "title": AlphaFold3 to offer structure prediction via web browser, "source": C&EN, "article_link": https://cen.acs.org/analytical-chemistry/structural-biology/AlphaFold3-offer-structure-prediction-via/102/web/2024/05}

Interaction 6: ↔

The execution error occurred because I tried to access a key 'snippet' that does not exist in the article dictionary. Instead, the correct key for the article summary is 'body'. I will correct the code to use the 'body' key and then proceed to append the news articles to the documents.

# Correct the key for the article summary and append the news articles to the documents
html_snippet = "<div class='section'><h2>News Articles about Alphafold3</h2>"
markdown_snippet = "## News Articles about Alphafold3\n\n"

for article in news_articles:
    title = article['title']
    source = article.get('source', 'Unknown Source')  # Use a default value in case 'source' key is missing
    article_link = article['url']
    snippet = article['body'][:140] + "..."  # Corrected key from 'snippet' to 'body'
    
    html_snippet += f"<div class='article'><strong>{title}</strong><br><em>Source: {source}</em><br><a href='{article_link}'>Read more</a><br><p>{snippet}</p></div>"
    markdown_snippet += f"- **{title}**\n  - Source: {source}\n  - [Read more]({article_link})\n  - {snippet}\n\n"

html_snippet += "</div>"
append_content(html_snippet=html_snippet, markdown_snippet=markdown_snippet)

# Save the updated content to files
save_content()

RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"html_snippet": <div class='section'><h2>News Articles about Alphafold3</h2><div class='article'><strong>AlphaFold3 to offer structure prediction via web browser</strong><br><em>Source: C&EN</em><br><a href='https://cen.acs.org/analytical-chemistry/structural-biology/AlphaFold3-offer-structure-prediction-via/102/web/2024/05'>Read more</a><br><p>AlphaFold3 shares many capabilities with the recently published RoseTTAFold-All Atom model. Though it lacks RoseTTAFold's ability to design ...</p></div><div class='arti..., "markdown_snippet": ## News Articles about Alphafold3

- **AlphaFold3 to offer structure prediction via web browser**
  - Source: C&EN
  - [Read more](https://cen.acs.org/analytical-chemistry/structural-biology/AlphaFold3-offer-structure-prediction-via/102/web/2024/05)
  - AlphaFold3 shares many capabilities with the recently published RoseTTAFold-All Atom model. Though it lacks RoseTTAFold's ability to design ...

- **Google Unveils A.I. for Predicting Behavior of Human Molecules**
  - Source: The New York Times
 ..., "article": {'date': '2024-05-08T16:32:00+00:00', 'title': 'In fundamental advance, Google says AlphaFold 3 can map a vast universe of biomolecules', 'body': 'Google on Wednesday unveiled an AI tool capable of predicting the structure and interaction of a vast universe of biomolecules.', 'url': 'https://www.statnews.com/2024/05/08/drug-discovery-alphafold-3-google-deepmind-isomorphic-labs/', 'image': 'https://www.statnews.com/wp-content/uploads/2024/05/AF3-header-1-1024x576.jpg', 'source': 'STAT'}, "title": In fundamental advance, Google says AlphaFold 3 can map a vast universe of biomolecules, "source": STAT, "article_link": https://www.statnews.com/2024/05/08/drug-discovery-alphafold-3-google-deepmind-isomorphic-labs/, "snippet": Google on Wednesday unveiled an AI tool capable of predicting the structure and interaction of a vast universe of biomolecules....}

Interaction 7: ↔

The news articles about Alphafold3 have been successfully appended to both the HTML and Markdown documents. Now, I will use the search_arxiv_and_return_articles method from the ArxivToolkit class to fetch research papers about Protein Folding with Deep Learning. I will then format and append this information to the documents.

# Fetch research papers about Protein Folding with Deep Learning
papers_result = arxiv_tools.search_arxiv_and_return_articles("Protein Folding with Deep Learning", num_articles=5)
research_papers = json.loads(papers_result)

# Append the research papers to the documents
html_snippet = "<div class='section'><h2>Research Papers about Protein Folding with Deep Learning</h2>"
markdown_snippet = "## Research Papers about Protein Folding with Deep Learning\n\n"

for paper in research_papers:
    title = paper['title']
    year_month = paper['published'].split('T')[0][:7]  # Extract the year-month from the published date
    authors = ', '.join([author['name'] for author in paper['authors']])
    pdf_link = paper['pdf_url']
    
    html_snippet += f"<div class='paper'><strong>{title}</strong><br><em>Published: {year_month}</em><br><em>Authors: {authors}</em><br><a href='{pdf_link}'>PDF Link</a></div>"
    markdown_snippet += f"- **{title}**\n  - Published: {year_month}\n  - Authors: {authors}\n  - [PDF Link]({pdf_link})\n\n"

html_snippet += "</div>"
append_content(html_snippet=html_snippet, markdown_snippet=markdown_snippet)

# Save the updated content to files
save_content()

RESPONSE (Execution Result):
STDOUT:
INFO     Searching arxiv for: Protein Folding with Deep Learning                                                                                  

EXECUTION ERROR:
string indices must be integers, not 'str' (File "<string>", line 12, in <module>)
 -> Error occurred when executing: authors = ', '.join([author['name'] for author in paper['authors']])
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"html_snippet": <div class='section'><h2>Research Papers about Protein Folding with Deep Learning</h2>, "markdown_snippet": ## Research Papers about Protein Folding with Deep Learning

, "title": Distance-based Protein Folding Powered by Deep Learning, "papers_result": [
    {
        "title": "Distance-based Protein Folding Powered by Deep Learning",
        "id": "1811.03481v2",
        "entry_id": "http://arxiv.org/abs/1811.03481v2",
        "authors": [
            "Jinbo Xu"
        ],
        "primary_category": "q-bio.BM",
        "categories": [
            "q-bio.BM"
        ],
        "published": "2018-11-08T15:08:19+00:00",
        "pdf_url": "http://arxiv.org/pdf/1811.03481v2",
        "links": [
            "http://dx.doi.org/10.1073/pnas.1821309..., "research_papers": [{'title': 'Distance-based Protein Folding Powered by Deep Learning', 'id': '1811.03481v2', 'entry_id': 'http://arxiv.org/abs/1811.03481v2', 'authors': ['Jinbo Xu'], 'primary_category': 'q-bio.BM', 'categories': ['q-bio.BM'], 'published': '2018-11-08T15:08:19+00:00', 'pdf_url': 'http://arxiv.org/pdf/1811.03481v2', 'links': ['http://dx.doi.org/10.1073/pnas.1821309116', 'http://arxiv.org/abs/1811.03481v2', 'http://arxiv.org/pdf/1811.03481v2'], 'summary': 'Contact-assisted protein folding has made ..., "paper": {'title': 'Distance-based Protein Folding Powered by Deep Learning', 'id': '1811.03481v2', 'entry_id': 'http://arxiv.org/abs/1811.03481v2', 'authors': ['Jinbo Xu'], 'primary_category': 'q-bio.BM', 'categories': ['q-bio.BM'], 'published': '2018-11-08T15:08:19+00:00', 'pdf_url': 'http://arxiv.org/pdf/1811.03481v2', 'links': ['http://dx.doi.org/10.1073/pnas.1821309116', 'http://arxiv.org/abs/1811.03481v2', 'http://arxiv.org/pdf/1811.03481v2'], 'summary': 'Contact-assisted protein folding has made v..., "year_month": 2018-11}

Interaction 8: ↔

The execution error occurred because I tried to access the 'name' key in a list of strings representing authors. The 'authors' key in the paper dictionary is a list of strings, not a list of dictionaries. I will correct the code to directly use the strings in the 'authors' list without trying to access a 'name' key.

# Correct the authors extraction and append the research papers to the documents
html_snippet = "<div class='section'><h2>Research Papers about Protein Folding with Deep Learning</h2>"
markdown_snippet = "## Research Papers about Protein Folding with Deep Learning\n\n"

for paper in research_papers:
    title = paper['title']
    year_month = paper['published'].split('T')[0][:7]  # Extract the year-month from the published date
    authors = ', '.join(paper['authors'])  # Corrected to directly use the list of authors
    pdf_link = paper['pdf_url']
    
    html_snippet += f"<div class='paper'><strong>{title}</strong><br><em>Published: {year_month}</em><br><em>Authors: {authors}</em><br><a href='{pdf_link}'>PDF Link</a></div>"
    markdown_snippet += f"- **{title}**\n  - Published: {year_month}\n  - Authors: {authors}\n  - [PDF Link]({pdf_link})\n\n"

html_snippet += "</div>"
append_content(html_snippet=html_snippet, markdown_snippet=markdown_snippet)

# Save the updated content to files
save_content()

RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"html_snippet": <div class='section'><h2>Research Papers about Protein Folding with Deep Learning</h2><div class='paper'><strong>Distance-based Protein Folding Powered by Deep Learning</strong><br><em>Published: 2018-11</em><br><em>Authors: Jinbo Xu</em><br><a href='http://arxiv.org/pdf/1811.03481v2'>PDF Link</a></div><div class='paper'><strong>DeepSF: deep convolutional neural network for mapping protein sequences to folds</strong><br><em>Published: 2017-06</em><br><em>Authors: Jie Hou, Badri Adhikari, Jianlin..., "markdown_snippet": ## Research Papers about Protein Folding with Deep Learning

- **Distance-based Protein Folding Powered by Deep Learning**
  - Published: 2018-11
  - Authors: Jinbo Xu
  - [PDF Link](http://arxiv.org/pdf/1811.03481v2)

- **DeepSF: deep convolutional neural network for mapping protein sequences to folds**
  - Published: 2017-06
  - Authors: Jie Hou, Badri Adhikari, Jianlin Cheng
  - [PDF Link](http://arxiv.org/pdf/1706.01010v1)

- **A Protein Structure Prediction Approach Leveraging Transformer a..., "title": SGNet: Folding Symmetrical Protein Complex with Deep Learning, "paper": {'title': 'SGNet: Folding Symmetrical Protein Complex with Deep Learning', 'id': '2403.04395v1', 'entry_id': 'http://arxiv.org/abs/2403.04395v1', 'authors': ['Zhaoqun Li', 'Jingcheng Yu', 'Qiwei Ye'], 'primary_category': 'q-bio.BM', 'categories': ['q-bio.BM', 'cs.CL'], 'published': '2024-03-07T10:39:48+00:00', 'pdf_url': 'http://arxiv.org/pdf/2403.04395v1', 'links': ['http://arxiv.org/abs/2403.04395v1', 'http://arxiv.org/pdf/2403.04395v1'], 'summary': 'Deep learning has made significant progress..., "year_month": 2024-03, "authors": Zhaoqun Li, Jingcheng Yu, Qiwei Ye, "pdf_link": http://arxiv.org/pdf/2403.04395v1}

Interaction 9: ↔

The research papers about Protein Folding with Deep Learning have been successfully appended to both the HTML and Markdown documents. The next step is to read the content of the Markdown document and send it as the body of an email to the specified email address with the given subject. I will use the email module from the phi codebase to send the email. However, before I can write the code to send the email, I need to search for the email module to understand how to use it.

file_path: *\/email.py

QUERY:
file_path: *\/email.py

CODE BLOCKS:
---
file_path=phi/tools/email.py
lines=[6, 59]
type=CLASS
content={
class EmailTools(Toolkit):
    """
    A class that provides email sending capabilities. Inherits from Toolkit.
    
    Attributes:
        receiver_email (Optional[str]): The email address of the receiver.
        sender_name (Optional[str]): The name of the sender.
        sender_email (Optional[str]): The email address of the sender.
        sender_passkey (Optional[str]): The passkey for the sender's email account.
    """

    def __init__(self, receiver_email: Optional[str]=None, sender_name: Optional[str]=None, sender_email: Optional[str]=None, sender_passkey: Optional[str]=None):
        """
        Initializes the EmailTools instance with optional sender and receiver information.
        
        Args:
            receiver_email (Optional[str]): The email address of the receiver. Defaults to None.
            sender_name (Optional[str]): The name of the sender. Defaults to None.
            sender_email (Optional[str]): The email address of the sender. Defaults to None.
            sender_passkey (Optional[str]): The passkey for the sender's email account. Defaults to None.
        """
        ...

    def email_user(self, subject: str, body: str) -> str:
        """
        Sends an email to the user with the specified subject and body.
        
        Args:
            subject (str): The subject of the email.
            body (str): The body of the email.
        
        Returns:
            str: "success" if the email was sent successfully, "error: [error message]" otherwise.
        
        Raises:
            ImportError: If the required `smtplib` is not installed.
        
        Examples:
            Assuming `email_tools` is an instance of `EmailTools` with valid sender and receiver information:
            >>> email_tools.email_user("Greetings", "Hello, this is a test email.")
            "email sent successfully"
        """
        ...
}
---
file_path=phi/tools/email.py
lines=[2, 3]
type=IMPORT
content={
from phi.tools import Toolkit
}
---
file_path=phi/tools/email.py
lines=[0, 1]
type=IMPORT
content={
from typing import Optional
}
---
(All code blocks matching the query were returned.)

Interaction 10: ↔

The search results have provided the EmailTools class which has an email_user method to send emails. I will use this method to send the email. Before sending the email, I need to read the content of the Markdown document and then use it as the body of the email. I will write the code to read the Markdown document and send the email using the provided sender information and the specified subject and receiver email.

from phi.tools.email import EmailTools
import os

# Read the content of the Markdown document
with open(markdown_output_path, 'r') as markdown_file:
    markdown_content = markdown_file.read()

# Initialize the EmailTools with the sender information
email_tools = EmailTools(
    receiver_email="xyz@gmail.com",
    sender_name="Tanmay Gupta",
    sender_email="tanmayg@allenai.org",
    sender_passkey=os.environ.get("GOOGLE_KEY")
)

# Send the email with the Markdown content as the body
email_subject = "Your Alphafold-3 study material curated with love by CodeNav"
email_body = markdown_content
email_send_result = email_tools.email_user(subject=email_subject, body=email_body)

# Check if the email was sent successfully
if "success" in email_send_result:
    print("Email sent successfully.")
else:
    print(f"Failed to send email: {email_send_result}")

RESPONSE (Execution Result):
STDOUT:
INFO     Sending Email to xyz@gmail.com                                                                                                    
Email sent successfully.

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"EmailTools": <class 'phi.tools.email.EmailTools'>, "markdown_file": <_io.TextIOWrapper name='/root/results/playground/alphafold_study.txt' mode='r' encoding='UTF-8'>, "email_tools": <EmailTools name=email_tools functions=['email_user']>, "email_subject": Your Alphafold-3 study material curated with love by CodeNav, "email_body": # Alphafold Study Material

## Wikipedia's Definition of Protein Folding

Protein folding is the physical process by which a protein, after synthesis by a ribosome as a linear chain of amino acids, changes from an unstable random coil into a more ordered three-dimensional structure. This structure permits the protein to become biologically functional.

## News Articles about Alphafold3

- **AlphaFold3 to offer structure prediction via web browser**
  - Source: C&EN
  - [Read more](https://cen.ac..., "email_send_result": email sent successfully}

Interaction 11: Done

The email has been sent successfully with the content of the Markdown document as the body. The task is now complete.