app.py

from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
import datetime
import requests
import pytz
import yaml
from tools.final_answer import FinalAnswerTool
from Gradio_UI import GradioUI

# Search Tool using DuckDuckGo
search_tool = DuckDuckGoSearchTool()

# Text Summarization Tool
@tool
def summarize_text(text: str) -> str:
    """Summarizes a given text into a concise version.
    Args:
        text: The text to be summarized.
    """
    try:
        api_url = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
        headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_KEY"}
        response = requests.post(api_url, headers=headers, json={"inputs": text})
        summary = response.json()[0]['summary_text']
        return summary
    except Exception as e:
        return f"Error in summarization: {str(e)}"

# Weather Information Tool
@tool
def get_weather(city: str) -> str:
    """Fetches current weather information for a given city.
    Args:
        city: Name of the city.
    """
    try:
        api_key = "YOUR_OPENWEATHERMAP_API_KEY"
        url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
        response = requests.get(url).json()
        temp = response["main"]["temp"]
        weather_desc = response["weather"][0]["description"]
        return f"The current temperature in {city} is {temp}°C with {weather_desc}."
    except Exception as e:
        return f"Error fetching weather data: {str(e)}"

# Currency Conversion Tool
@tool
def convert_currency(amount: float, from_currency: str, to_currency: str) -> str:
    """Converts currency based on real-time exchange rates.
    Args:
        amount: The amount to be converted.
        from_currency: The source currency (e.g., 'USD').
        to_currency: The target currency (e.g., 'EUR').
    """
    try:
        api_url = f"https://api.exchangerate-api.com/v4/latest/{from_currency}"
        response = requests.get(api_url).json()
        rate = response["rates"].get(to_currency, None)
        if rate:
            converted_amount = amount * rate
            return f"{amount} {from_currency} is approximately {converted_amount:.2f} {to_currency}."
        else:
            return f"Conversion rate for {to_currency} not found."
    except Exception as e:
        return f"Error in currency conversion: {str(e)}"

# Fun Fact Generator Tool
@tool
def get_fun_fact() -> str:
    """Fetches a random fun fact."""
    try:
        response = requests.get("https://uselessfacts.jsph.pl/random.json?language=en").json()
        return response["text"]
    except Exception as e:
        return f"Error fetching fun fact: {str(e)}"

# Text Sentiment Analyzer
@tool
def analyze_sentiment(text: str) -> str:
    """Analyzes the sentiment of a given text (positive, neutral, or negative).
    Args:
        text: The text to be analyzed.
    """
    try:
        api_url = "https://api-inference.huggingface.co/models/cardiffnlp/twitter-roberta-base-sentiment"
        headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_KEY"}
        response = requests.post(api_url, headers=headers, json={"inputs": text})
        sentiment = response.json()[0][0]['label']
        return f"The sentiment of the given text is: {sentiment}"
    except Exception as e:
        return f"Error in sentiment analysis: {str(e)}"

# Time Zone Tool
@tool
def get_current_time_in_timezone(timezone: str) -> str:
    """A tool that fetches the current local time in a specified timezone.
    Args:
        timezone: A string representing a valid timezone (e.g., 'America/New_York').
    """
    try:
        tz = pytz.timezone(timezone)
        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
        return f"The current local time in {timezone} is: {local_time}"
    except Exception as e:
        return f"Error fetching time for timezone '{timezone}': {str(e)}"

final_answer = FinalAnswerTool()
model = HfApiModel(
    max_tokens=2096,
    temperature=0.5,
    model_id='https://wxknx1kg971u7k1n.us-east-1.aws.endpoints.huggingface.cloud',
    custom_role_conversions=None,
)

# Import tool from Hugging Face Hub
image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)

with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)

# Define Agent with All Tools
agent = CodeAgent(
    model=model,
    tools=[final_answer, search_tool, image_generation_tool, summarize_text, get_weather, convert_currency, get_fun_fact, analyze_sentiment, get_current_time_in_timezone],
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)

GradioUI(agent).launch()