The Startup guide that forms AI ai-variable AI for Pipecat and Huggingface
In this lesson, we examine how we can build a fully functional AI agent from the beginning to use Pipeat Outline. We go by setting a pipe linking framesSsosSor, another for the input process and replying answers with a Huggainface model, and another formatting and displaying the flow of the discussion. We also use Discouringererator to imitate the dialogue, and use the Pipinerunner and Pipelatask to make ASYNCHRONOUNCLY frequency flow. This structure shows how the pipecat deals with the structural removal, enables the integration of nutrients such as language models, a healthy shape, and future add-to-date. Look Full codes here.
!pip install -q pipecat-ai transformers torch accelerate numpy
import asyncio
import logging
from typing import AsyncGenerator
import numpy as np
print("🔍 Checking available Pipecat frames...")
try:
from pipecat.frames.frames import (
Frame,
TextFrame,
)
print("✅ Basic frames imported successfully")
except ImportError as e:
print(f"⚠️ Import error: {e}")
from pipecat.frames.frames import Frame, TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from transformers import pipeline as hf_pipeline
import torchWe begin by installing the required libraries, including pipecat, converts, and pytroch, and moving the importation of us. We bring main parts of the PipeCat, such as a pipe, powderer, and framedoprossor, and the Pipelineface's Pipeline API Pipeline API. This prepares our environment to build and use AI for conversion agent without seams. Look Full codes here.
class SimpleChatProcessor(FrameProcessor):
"""Simple conversational AI processor using HuggingFace"""
def __init__(self):
super().__init__()
print("🔄 Loading HuggingFace text generation model...")
self.chatbot = hf_pipeline(
"text-generation",
model="microsoft/DialoGPT-small",
pad_token_id=50256,
do_sample=True,
temperature=0.8,
max_length=100
)
self.conversation_history = ""
print("✅ Chat model loaded successfully!")
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame):
user_text = getattr(frame, "text", "").strip()
if user_text and not user_text.startswith("AI:"):
print(f"👤 USER: {user_text}")
try:
if self.conversation_history:
input_text = f"{self.conversation_history} User: {user_text} Bot:"
else:
input_text = f"User: {user_text} Bot:"
response = self.chatbot(
input_text,
max_new_tokens=50,
num_return_sequences=1,
temperature=0.7,
do_sample=True,
pad_token_id=self.chatbot.tokenizer.eos_token_id
)
generated_text = response[0]["generated_text"]
if "Bot:" in generated_text:
ai_response = generated_text.split("Bot:")[-1].strip()
ai_response = ai_response.split("User:")[0].strip()
if not ai_response:
ai_response = "That's interesting! Tell me more."
else:
ai_response = "I'd love to hear more about that!"
self.conversation_history = f"{input_text} {ai_response}"
await self.push_frame(TextFrame(text=f"AI: {ai_response}"), direction)
except Exception as e:
print(f"⚠️ Chat error: {e}")
await self.push_frame(
TextFrame(text="AI: I'm having trouble processing that. Could you try rephrasing?"),
direction
)
else:
await self.push_frame(frame, direction)We use SimpleckeckCkeckProCocessor, Loading a magnificent diagratface model of text and keeps the status of the situation. As each Texframe name arrives, processing user's input, producing a model response, cleaning them, and casts them to the pipecat pipe to be displayed. The project ensures that our AI agent can have a unified conversation, many variables in real time. Look Full codes here.
class TextDisplayProcessor(FrameProcessor):
"""Displays text frames in a conversational format"""
def __init__(self):
super().__init__()
self.conversation_count = 0
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame):
text = getattr(frame, "text", "")
if text.startswith("AI:"):
print(f"🤖 {text}")
self.conversation_count += 1
print(f" 💭 Exchange {self.conversation_count} completen")
await self.push_frame(frame, direction)
class ConversationInputGenerator:
"""Generates demo conversation inputs"""
def __init__(self):
self.demo_conversations = [
"Hello! How are you doing today?",
"What's your favorite thing to talk about?",
"Can you tell me something interesting about AI?",
"What makes conversation enjoyable for you?",
"Thanks for the great chat!"
]
async def generate_conversation(self) -> AsyncGenerator[TextFrame, None]:
print("🎭 Starting conversation simulation...n")
for i, user_input in enumerate(self.demo_conversations):
yield TextFrame(text=user_input)
if i < len(self.demo_conversations) - 1:
await asyncio.sleep(2)We create TEXTYDISPORISTROPHINDER IN FOOD NEW AND SHOWING AI ANSWERS, follow the exchange rate in the conversation. Next to it, the booklet View of the user's messages as text objects such as text objects, add short slopes between them to imitate the backward flow and demo. Look Full codes here.
class SimpleAIAgent:
"""Simple conversational AI agent using Pipecat"""
def __init__(self):
self.chat_processor = SimpleChatProcessor()
self.display_processor = TextDisplayProcessor()
self.input_generator = ConversationInputGenerator()
def create_pipeline(self) -> Pipeline:
return Pipeline([self.chat_processor, self.display_processor])
async def run_demo(self):
print("🚀 Simple Pipecat AI Agent Demo")
print("🎯 Conversational AI with HuggingFace")
print("=" * 50)
pipeline = self.create_pipeline()
runner = PipelineRunner()
task = PipelineTask(pipeline)
async def produce_frames():
async for frame in self.input_generator.generate_conversation():
await task.queue_frame(frame)
await task.stop_when_done()
try:
print("🎬 Running conversation demo...n")
await asyncio.gather(
runner.run(task),
produce_frames(),
)
except Exception as e:
print(f"❌ Demo error: {e}")
logging.error(f"Pipeline error: {e}")
print("✅ Demo completed successfully!")In a simple time, we fasten everything together by combining the dialog process, to show the processor, and the installation generator in one pipe pipe. The Run_demo methodology introduces pipelinernner to process asynchronous independent processing while the installation generator submits user messages made. This orchestreat decoration is allowing an agent to process the input, produce answers, and reflect the actual time, completing the last converting flow. Look Full codes here.
async def main():
logging.basicConfig(level=logging.INFO)
print("🎯 Pipecat AI Agent Tutorial")
print("📱 Google Colab Compatible")
print("🤖 Free HuggingFace Models")
print("🔧 Simple & Working Implementation")
print("=" * 60)
try:
agent = SimpleAIAgent()
await agent.run_demo()
print("n🎉 Tutorial Complete!")
print("n📚 What You Just Saw:")
print("✓ Pipecat pipeline architecture in action")
print("✓ Custom FrameProcessor implementations")
print("✓ HuggingFace conversational AI integration")
print("✓ Real-time text processing pipeline")
print("✓ Modular, extensible design")
print("n🚀 Next Steps:")
print("• Add real speech-to-text input")
print("• Integrate text-to-speech output")
print("• Connect to better language models")
print("• Add memory and context management")
print("• Deploy as a web service")
except Exception as e:
print(f"❌ Tutorial failed: {e}")
import traceback
traceback.print_exc()
try:
import google.colab
print("🌐 Google Colab detected - Ready to run!")
ENV = "colab"
except ImportError:
print("💻 Local environment detected")
ENV = "local"
print("n" + "="*60)
print("🎬 READY TO RUN!")
print("Execute this cell to start the AI conversation demo")
print("="*60)
print("n🚀 Starting the AI Agent Demo...")
await main()We define the main function of starting in, set the Searchaaaaant, and runs short while shaping useful progress and summary messages. We also see that the code works on Google Colab or location, environmental information, and make a phone call wait for the full killings of Ai Pipeline Pipeline.
In conclusion, we have an active AI agent where the converting variable inputs in which the user's frames (or frames made) are transmitted with the processing pupine, the Huggingface model produces answers, and the results are displayed in a systematic conversion. The implementation shows how PiteCat's construction supports asynchronous processing, manage safe negotiations, as well as the pure classification of concern between different categories of processing. Through this basis, we can now combine the most advanced features, such as Express-to–to-Synther, prediction of the text, persistence, or revisiting Modes, while maintaining a magnetic code.
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