metadata

title: 🧠Deep🐍Research🌐Evaluator
emoji: 🧠🐍🌐
colorFrom: red
colorTo: purple
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: true
license: mit
short_description: Deep Research Evaluator for Long Horizon Learning Tasks

🎵', '🎶', '🎸', '🎹', '🎺', '🎷', '🥁', '🎻

Deep Research Evaluator is a conceptual AI system designed to analyze and synthesize information from extensive research literature, such as arXiv papers, to learn about specific topics and generate code applicable to long-horizon tasks in AI. This involves understanding complex subjects, identifying relevant methodologies, and implementing solutions that require planning and execution over extended sequences.

Project Architecture

📂 Root Folder
- app.py (🤖 Streamlit App)
  - Main entry point for your Streamlit application.
- requirements.txt (📋 Dependencies)
  - Lists all the Python packages needed to run the app.
- 📂 mycomponent (🔧 HTML Component)
  - A subdirectory containing your custom Streamlit component code.
  - __init__.py (🐍 Python Init)
    - Tells Python this folder is a module/package.
  - index.html (🌐 Custom HTML)
    - Front-end HTML/JS/CSS for the custom component.

flowchart TB
    A[📂 Root Folder] --> B[app.py 🤖<br>(Streamlit App)]
    A --> C[requirements.txt 📋<br>(Dependencies)]
    A --> D[📂 mycomponent 🔧<br>(HTML Component)]
    D --> E[__init__.py 🐍<br>(Python Init)]
    D --> F[index.html 🌐<br>(Custom HTML)]

Usage Flow:

You run streamlit run app.py.
app.py imports mycomponent to load the HTML from index.html.
requirements.txt ensures needed dependencies are installed.
The __init__.py file ensures the custom component folder is recognized as a Python package.

Notes:

app.py hosts your Streamlit logic and references the mycomponent.
index.html supplies the interface for any front-end custom elements.
requirements.txt keeps the environment consistent.

Features 🎯 Core Configuration & Setup Configures the Streamlit page with title “🚲TalkingAIResearcher🏆”, sets layout, sidebar states, and environment variables.

🔑 API Setup & Clients Loads and initializes OpenAI, Anthropic, and HuggingFace clients from environment variables and secrets.

📝 Session State Management Manages conversation history, transcripts, file editing states, and model selections.

🧠 get_high_info_terms() Extracts top words/bigrams from a text by counting frequency and filtering out stop words.

🏷️ clean_text_for_filename() Sanitizes text for valid filenames by removing special characters, short/unhelpful words, and truncating length.

📄 generate_filename() Creates an intelligent filename based on timestamps, high-info terms, and a snippet of the content (removing duplicates).

💾 create_file() Saves prompt + response content to a file, using generate_filename().

🔗 get_download_link() Generates base64-encoded download links for .md, audio, or zip files for inline downloading.

🎤 clean_for_speech() Strips out line breaks, URLs, and symbols to create more readable text for TTS.

🎙️ edge_tts_generate_audio() Asynchronously generates audio files (e.g., .mp3) using Edge TTS.

🔊 speak_with_edge_tts() A wrapper function for the async TTS call, allowing direct usage in synchronous code.

🎵 play_and_download_audio() Embeds an audio player in Streamlit and provides a download link for that audio file.

💿 save_qa_with_audio() Stores Q&A content in a markdown file and generates TTS audio for the question + answer.

📰 parse_arxiv_refs() Parses the multi-line markdown references returned by the ArXiv RAG pipeline into structured paper objects.

🔗 create_paper_links_md() Builds a minimal markdown page with numbered links to each paper’s ArXiv URL.

📑 create_paper_audio_files() Processes each parsed paper, generating TTS audio and embedding base64 download links.

📚 display_papers() Shows papers in the main area with a scrolling marquee (via streamlit_marquee), plus expanders for details and audio.

🗂 display_papers_in_sidebar() Mirrors the paper listing in the sidebar with expanders, letting users quickly play or download paper audio.

📂 display_file_history_in_sidebar() Enumerates all local .md, .mp3, .wav files in descending modification time, letting users preview and download them.

📦 create_zip_of_files() Bundles multiple files (markdown + audio) into a zip with an automatically shortened filename.

🔍 perform_ai_lookup() The main function to:

Query Anthropic (Claude) Call an ArXiv RAG pipeline Generate Q&A audio Parse and render the resulting papers 🎧 process_voice_input() Receives user text/voice input, then calls perform_ai_lookup() to produce an audio summary and final Q&A file.

🎬 main() Orchestrates the entire application flow:

Renders tabs for Voice Input, Media Gallery, ArXiv search, and Editor Shows file history in the sidebar Manages marquee settings and final UI layout