To create an agent of a variety of various various agents using lease models
In this lesson, we start by setting up Age Age Age in ACRCHONKONE but works well, Sugging Face Transformers. It includes a generation generation, answering questions, emotional, stubs searching the web, climate appearance, and secure counting in a single Python phase. As improved, we only apply important information libraries, Upload light models that respect Colab memory limit, and wrap each power within formal, usable methods. Together, we test that all part, from finding the purpose of receiving the device model, fits the united activity, which gives us the strength to show multiple multi-service agents.
!pip install transformers torch accelerate datasets requests beautifulsoup4
import torch
import json
import requests
from datetime import datetime
from transformers import (
AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification,
AutoModelForQuestionAnswering, pipeline
)
from bs4 import BeautifulSoup
import warnings
warnings.filterwarnings('ignore')We start by installing the important libraries of Python, converts, Tots, acceleration, datasets, applications, requests, and beautifies, so our colab environment has everything that you need. Next, we introduce PyTTORT, HTS resources, HTTP and Date assistants, were not able to deal with the generation, submission and QA, and QA, and Quatama, and Qeasing unnecessary warning to clean a letter of writing a book.
class AdvancedAIAgent:
def __init__(self):
"""Initialize the AI Agent with multiple models and capabilities"""
self.device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"🚀 Initializing AI Agent on {self.device}")
self._load_models()
self.tools = {
"web_search": self.web_search,
"calculator": self.calculator,
"weather": self.get_weather,
"sentiment": self.analyze_sentiment
}
print("✅ AI Agent initialized successfully!")
def _load_models(self):
"""Load all required models"""
print("📥 Loading models...")
self.gen_tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
self.gen_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
self.gen_tokenizer.pad_token = self.gen_tokenizer.eos_token
self.sentiment_pipeline = pipeline(
"sentiment-analysis",
model="cardiffnlp/twitter-roberta-base-sentiment-latest",
device=0 if self.device == "cuda" else -1
)
self.qa_pipeline = pipeline(
"question-answering",
model="distilbert-base-cased-distilled-squad",
device=0 if self.device == "cuda" else -1
)
print("✅ All models loaded!")
def generate_response(self, prompt, max_length=100, temperature=0.7):
"""Generate text response using the language model"""
inputs = self.gen_tokenizer.encode(prompt + self.gen_tokenizer.eos_token,
return_tensors="pt")
with torch.no_grad():
outputs = self.gen_model.generate(
inputs,
max_length=max_length,
temperature=temperature,
do_sample=True,
pad_token_id=self.gen_tokenizer.eos_token_id,
attention_mask=torch.ones_like(inputs)
)
response = self.gen_tokenizer.decode(outputs[0][len(inputs[0]):],
skip_special_tokens=True)
return response.strip()
def analyze_sentiment(self, text):
"""Analyze sentiment of given text"""
result = self.sentiment_pipeline(text)[0]
return {
"sentiment": result['label'],
"confidence": round(result['score'], 4),
"text": text
}
def answer_question(self, question, context):
"""Answer questions based on given context"""
result = self.qa_pipeline(question=question, context=context)
return {
"answer": result['answer'],
"confidence": round(result['score'], 4),
"question": question
}
def web_search(self, query):
"""Simulate web search (replace with actual API if needed)"""
try:
return {
"query": query,
"results": f"Search results for '{query}': Latest information retrieved successfully.",
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}
except Exception as e:
return {"error": f"Search failed: {str(e)}"}
def calculator(self, expression):
"""Safe calculator function"""
try:
allowed_chars = set('0123456789+-*/.() ')
if not all(c in allowed_chars for c in expression):
return {"error": "Invalid characters in expression"}
result = eval(expression)
return {
"expression": expression,
"result": result,
"type": type(result).__name__
}
except Exception as e:
return {"error": f"Calculation failed: {str(e)}"}
def get_weather(self, location):
"""Mock weather function (replace with actual weather API)"""
return {
"location": location,
"temperature": "22°C",
"condition": "Partly cloudy",
"humidity": "65%",
"note": "This is mock data. Integrate with a real weather API for actual data."
}
def detect_intent(self, user_input):
"""Simple intent detection based on keywords"""
user_input = user_input.lower()
if any(word in user_input for word in ['calculate', 'math', '+', '-', '*', '/']):
return 'calculator'
elif any(word in user_input for word in ['weather', 'temperature', 'forecast']):
return 'weather'
elif any(word in user_input for word in ['search', 'find', 'look up']):
return 'web_search'
elif any(word in user_input for word in ['sentiment', 'emotion', 'feeling']):
return 'sentiment'
elif '?' in user_input:
return 'question_answering'
else:
return 'chat'
def process_request(self, user_input, context=""):
"""Main method to process user requests"""
print(f"🤖 Processing: {user_input}")
intent = self.detect_intent(user_input)
response = {"intent": intent, "input": user_input}
try:
if intent == 'calculator':
import re
expr = re.findall(r'[0-9+-*/.() ]+', user_input)
if expr:
result = self.calculator(expr[0].strip())
response.update(result)
else:
response["error"] = "No valid mathematical expression found"
elif intent == 'weather':
words = user_input.split()
location = "your location"
for i, word in enumerate(words):
if word.lower() in ['in', 'at', 'for']:
if i + 1 < len(words):
location = words[i + 1]
break
result = self.get_weather(location)
response.update(result)
elif intent == 'web_search':
query = user_input.replace('search', '').replace('find', '').strip()
result = self.web_search(query)
response.update(result)
elif intent == 'sentiment':
text_to_analyze = user_input.replace('sentiment', '').strip()
if not text_to_analyze:
text_to_analyze = "I'm feeling great today!"
result = self.analyze_sentiment(text_to_analyze)
response.update(result)
elif intent == 'question_answering' and context:
result = self.answer_question(user_input, context)
response.update(result)
else:
generated_response = self.generate_response(user_input)
response["response"] = generated_response
response["type"] = "generated_text"
except Exception as e:
response["error"] = f"Error processing request: {str(e)}"
return responseJoin the faster AI devette News Newsletter read by Devids and researchers from Envidia, Open, Deepmind, Meta, Microsoft Fargo and 100s more …..
We allow all our tools within the destructive AdchiaaAgent class in GPU when available, cargo cars, emotions, weather, and arithmetic. With the acquisition of the Lightweight purpose based on the word, we are the power of each user's user in the relevant pipeline or back to free form generation, to provide unified agent in a few pure ways.
if __name__ == "__main__":
agent = AdvancedAIAgent()
print("n" + "="*50)
print("🎯 DEMO: Advanced AI Agent Capabilities")
print("="*50)
test_cases = [
"Calculate 25 * 4 + 10",
"What's the weather in Tokyo?",
"Search for latest AI developments",
"Analyze sentiment of: I love working with AI!",
"Hello, how are you today?"
]
for test in test_cases:
print(f"n👤 User: {test}")
result = agent.process_request(test)
print(f"🤖 Agent: {json.dumps(result, indent=2)}")
"""
print("n🎮 Interactive Mode - Type 'quit' to exit")
while True:
user_input = input("n👤 You: ")
if user_input.lower() == 'quit':
break
result = agent.process_request(user_input)
print(f"🤖 Agent: {json.dumps(result, indent=2)}")
"""
We conclude from streaming AdchiaaAAIAGent, announces the fast-fuel phase, and fired five inspirational motives that test the number, the weather, search, sensitive, and conversation of one sweeping. After reviewing JSON's formal relevant answers, we keep the loop of choice in standby, ready for a live testing whenever we decide not to be answered.
In conclusion, we check the variety of emergence in the original world and we are rediring weather data, redesigning weather data, and applies to the natural risk, all through one united interface using other face models. This work indicates how we can submit many NLP tasks into upper-case frameworks in Colob sources.
Look Codes. All credit for this study goes to research for this project.
Join the faster AI devette News Newsletter read by Devids and researchers from Envidia, Open, Deepmind, Meta, Microsoft Fargo and 100s more …..
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