examples(studypal): use aiohttp instead of requests

CHANGELOG.md

@@ -98,6 +98,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Other
 
+- Added `studypal` example (from to the Cartesia folks!).
+
 - Most examples now use Cartesia.
 
 - Added examples `foundational/19a-tools-anthropic.py`,

examples/README.md

@@ -41,6 +41,7 @@ Next, follow the steps in the README for each demo.
 | [Patient intake](patient-intake)             | A chatbot that can call functions in response to user input.                                                                               | Deepgram, ElevenLabs, OpenAI, Daily, Daily Prebuilt UI            |
 | [Dialin Chatbot](dialin-chatbot)             | A chatbot that connects to an incoming phone call from Daily or Twilio.                                                                    | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [Twilio Chatbot](twilio-chatbot)             | A chatbot that connects to an incoming phone call from Twilio.                                                                             | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
+| [studypal](studypal)                         | A chatbot to have a conversation about any article on the web                                                                              |                                                                   |
 
 > [!IMPORTANT]
 > These example projects use Daily as a WebRTC transport and can be joined using their hosted Prebuilt UI.

examples/studypal/README.md

@@ -1,12 +1,13 @@
 # studypal
+
 ### Have a conversation about any article on the web
 
-studypal is a fast conversational ai built using [Daily](https://www.daily.co/) for real-time media transport and [Cartesia](https://cartesia.ai) for text-to-speech. Everything is orchestrated together (VAD -> STT -> LLM -> TTS) using [Pipecat](https://www.pipecat.ai/). 
+studypal is a fast conversational AI built using [Daily](https://www.daily.co/) for real-time media transport and [Cartesia](https://cartesia.ai) for text-to-speech. Everything is orchestrated together (VAD -> STT -> LLM -> TTS) using [Pipecat](https://www.pipecat.ai/).
 
 ## Setup
 
 1. Clone the repository
-2. Copy `.env.example` to a `.env` file and add API keys 
+2. Copy `env.example` to a `.env` file and add API keys
-3. Install the required packages: `pip install -r requirements.txt` 
+3. Install the required packages: `pip install -r requirements.txt`
-4. Run `python3 studypal.py` from your command line. 
+4. Run `python3 studypal.py` from your command line.
 5. While the app is running, go to the `https://<yourdomain>.daily.co/<room_url>` set in `DAILY_SAMPLE_ROOM_URL` and talk to studypal!

examples/studypal/requirements.txt

@@ -1,16 +1,5 @@
-aiohttp==3.9.5
 beautifulsoup4==4.12.2
 PyPDF2==3.0.1
 tiktoken==0.7.0
-pipecat==0.3.0
+pipecat-ai[daily,cartesia,openai,silero]==0.0.39
-pipecat-ai==0.0.39
 python-dotenv==1.0.1
-loguru==0.7.2
-requests==2.32.3
-pydantic==2.8.2
-httpx==0.27.0
-openai==1.27.0
-websockets==12.0
-daily-python==0.10.1
-torch==2.2.2
-torchaudio==2.2.2

examples/studypal/studypal.py

@@ -2,8 +2,8 @@ import aiohttp
 import asyncio
 import os
 import sys
-import requests
 import io
+
 from bs4 import BeautifulSoup
 from PyPDF2 import PdfReader
 import tiktoken
@@ -26,17 +26,15 @@ from loguru import logger
 from dotenv import load_dotenv
 load_dotenv(override=True)
 
-from openai import OpenAI
+# Run this script directly from your command line.
-client = OpenAI()
+# This project was adapted from
-
+# https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/07d-interruptible-cartesia.py
-# Run this script directly from your command line. 
-# This project was adapted from https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/07d-interruptible-cartesia.py
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-# Count number of tokens used in model and truncate the content 
+# Count number of tokens used in model and truncate the content
 def truncate_content(content, model_name):
     encoding = tiktoken.encoding_for_model(model_name)
     tokens = encoding.encode(content)
@@ -47,50 +45,66 @@ def truncate_content(content, model_name):
         return encoding.decode(truncated_tokens)
     return content
 
-# Main function to extract content from url 
+# Main function to extract content from url
-def get_article_content(url):
+
+
+async def get_article_content(url: str, aiohttp_session: aiohttp.ClientSession):
     if 'arxiv.org' in url:
-        return get_arxiv_content(url)
+        return await get_arxiv_content(url, aiohttp_session)
     else:
-        return get_wikipedia_content(url)
+        return await get_wikipedia_content(url, aiohttp_session)
 
-# Helper function to extract content from Wikipedia url (this is technically agnostic to URL type but will work best with Wikipedia articles)
+# Helper function to extract content from Wikipedia url (this is
-def get_wikipedia_content(url):
+# technically agnostic to URL type but will work best with Wikipedia
-    response = requests.get(url)
+# articles)
-    soup = BeautifulSoup(response.content, 'html.parser')
-    
-    content = soup.find('div', {'class': 'mw-parser-output'})
-    
-    if content:
-        return content.get_text()
-    else:
-        return "Failed to extract Wikipedia article content."
 
-# Helper function to extract content from arXiv url 
+
-def get_arxiv_content(url):
+async def get_wikipedia_content(url: str, aiohttp_session: aiohttp.ClientSession):
+    async with aiohttp_session.get(url) as response:
+        if response.status != 200:
+            return "Failed to download Wikipedia article."
+
+        text = await response.text()
+        soup = BeautifulSoup(text, 'html.parser')
+
+        content = soup.find('div', {'class': 'mw-parser-output'})
+
+        if content:
+            return content.get_text()
+        else:
+            return "Failed to extract Wikipedia article content."
+
+# Helper function to extract content from arXiv url
+
+
+async def get_arxiv_content(url: str, aiohttp_session: aiohttp.ClientSession):
     if '/abs/' in url:
         url = url.replace('/abs/', '/pdf/')
     if not url.endswith('.pdf'):
         url += '.pdf'
 
-    response = requests.get(url)
+    async with aiohttp_session.get(url) as response:
-    if response.status_code == 200:
+        if response.status != 200:
-        pdf_file = io.BytesIO(response.content)
+            return "Failed to download arXiv PDF."
+
+        content = await response.read()
+        pdf_file = io.BytesIO(content)
         pdf_reader = PdfReader(pdf_file)
         text = ""
         for page in pdf_reader.pages:
             text += page.extract_text()
         return text
-    else:
-        return "Failed to download arXiv PDF."
 
-# This is the main function that handles STT -> LLM -> TTS 
+# This is the main function that handles STT -> LLM -> TTS
+
+
 async def main():
     url = input("Enter the URL of the article you would like to talk about: ")
-    article_content = get_article_content(url)
-    article_content = truncate_content(article_content, model_name="gpt-4o-mini")
 
     async with aiohttp.ClientSession() as session:
+        article_content = await get_article_content(url, session)
+        article_content = truncate_content(article_content, model_name="gpt-4o-mini")
+
         (room_url, token) = await configure(session)
 
         transport = DailyTransport(
@@ -108,25 +122,22 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="4d2fd738-3b3d-4368-957a-bb4805275bd9",  # British Narration Lady: 4d2fd738-3b3d-4368-957a-bb4805275bd9
+            voice_id=os.getenv("CARTESIA_VOICE_ID", "4d2fd738-3b3d-4368-957a-bb4805275bd9"),
-            sample_rate=44100, 
+            # British Narration Lady: 4d2fd738-3b3d-4368-957a-bb4805275bd9
+            sample_rate=44100,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
             model="gpt-4o-mini")
 
-        messages = [
+        messages = [{
-            {
+            "role": "system", "content": f"""You are an AI study partner. You have been given the following article content:
-                "role": "system",
-                "content": f"""You are an AI study partner. You have been given the following article content:
 
 {article_content}
 
 Your task is to help the user understand and learn from this article in 2 sentences. THESE RESPONSES SHOULD BE ONLY MAX 2 SENTENCES. THIS INSTRUCTION IS VERY IMPORTANT. RESPONSES SHOULDN'T BE LONG.
-""",
+""", }, ]
-            },
-        ]
 
         tma_in = LLMUserResponseAggregator(messages)
         tma_out = LLMAssistantResponseAggregator(messages)
@@ -146,7 +157,9 @@ Your task is to help the user understand and learn from this article in 2 senten
         async def on_first_participant_joined(transport, participant):
             transport.capture_participant_transcription(participant["id"])
             messages.append(
-                {"role": "system", "content": "Hello! I'm ready to discuss the article with you. What would you like to learn about?"})
+                {
+                    "role": "system",
+                    "content": "Hello! I'm ready to discuss the article with you. What would you like to learn about?"})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -154,4 +167,4 @@ Your task is to help the user understand and learn from this article in 2 senten
         await runner.run(task)
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())