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import requests |
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import json |
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from smolagents import InferenceClientModel, tool |
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from typing import Any, Dict |
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@tool |
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def get_weather(city: str) -> Dict[str, Any]: |
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""" |
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Get the weather based on the city name |
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Args: |
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city (str): The name of the city |
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Returns: |
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Dict[str, Any]: The weather information as a dictionary with the following keys: |
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- City: The name of the city |
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- Temperature (°C): The temperature in Celsius |
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- Weather: The weather description |
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- Humidity (%): The humidity percentage |
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- Wind (km/h): The wind speed in kilometers per hour |
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- Feels Like (°C): The temperature that feels like |
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- Min temperature (°C): The minimum temperature |
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- Max temperature (°C): The maximum temperature |
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- Chance of rain: The chance of rain |
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- Chance of snow: The chance of snow |
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""" |
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try: |
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url = f"https://wttr.in/{city}?format=j1" |
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response = requests.get(url) |
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data = response.json() |
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current = data['current_condition'][0] |
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day_weather = data['weather'][0] |
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chance_of_rain = max([int(hour["chanceofrain"])/100 for hour in day_weather['hourly']]) |
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chance_of_snow = max([int(hour["chanceofsnow"])/100 for hour in day_weather['hourly']]) |
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weather = { |
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"City": city, |
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"Temperature (°C)": current["temp_C"], |
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"Weather": current["weatherDesc"][0]["value"], |
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"Humidity (%)": current["humidity"], |
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"Wind (km/h)": current["windspeedKmph"], |
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"Feels Like (°C)": current["FeelsLikeC"], |
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"Min temperature (°C)": day_weather["mintempC"], |
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"Max temperature (°C)": day_weather["maxtempC"], |
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"Chance of rain": chance_of_rain, |
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"Chance of snow": chance_of_snow, |
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} |
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return weather |
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except Exception as e: |
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return {"Error": str(e)} |
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@tool |
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def get_current_city() -> str: |
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""" |
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Get the location based on the IP address |
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Args: |
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Returns: |
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str: the city where the person is currently in based on the IP |
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""" |
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try: |
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response = requests.get("https://ipinfo.io/json") |
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data = response.json() |
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return data.get("city") |
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except Exception as e: |
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return e |
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@tool |
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def infer_event_style_from_text(user_input: str) -> str: |
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""" |
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Infers the style of an event based on natural language input using an LLM. |
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Returns one of: casual, formal, beachwear, dress-up, business casual, athletic, outdoor, unspecified. |
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Args: |
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user_input: A sentence or phrase describing what the user is doing, e.g., "I'm going to a beach party" |
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Returns: |
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A lowercase string representing the event style. One of: |
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- casual |
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- formal |
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- beachwear |
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- dress-up |
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- business casual |
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- athletic |
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- outdoor |
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- unspecified |
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""" |
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messages = [ |
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Message(role= "system", |
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content= |
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"You are an event style classifier. Your job is to take natural language user input " |
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"and respond with exactly one of the following event style labels:\n" |
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"- casual\n- formal\n- beachwear\n- dress-up\n- business casual\n- athletic\n- outdoor\n- unspecified"), |
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Message(role= "user", |
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content= f"Classify this: {user_input}\nEvent style:") |
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] |
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model = InferenceClientModel() |
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response = model(messages, stop_sequences=["END"]) |
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print(f"Response: {response.content}") |
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return response.content.lower() |
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@tool |
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def get_vogue(city: str, season: str) -> str: |
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""" |
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Get all the clothes in the wardrobe, together with their colors, and descriptions |
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Args: |
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city (str): The name of the city |
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season (str): The name of the season: |
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- "winter": clothes for winter |
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- "spring": clothes for spring |
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- "summer": clothes for summer |
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- "fall": clothes for fall |
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Returns: |
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str: text of what items are on trend for a given weather and occasion |
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""" |
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return """ |
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Activity Clothing Accessories Style Tip |
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Brunch at a café Linen shirt or chic blouse, tailored trousers or midi skirt Crossbody bag, sunglasses Parisians love neutral tones – try beige, white, navy |
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Stroll in Montmartre or along the Seine Lightweight cotton dress or relaxed-fit jeans + striped top Comfortable leather flats, beret A Breton stripe is classic Parisian chic |
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Museum visit Midi dress or smart jumpsuit Small tote, silk scarf Keep layers easy to remove (some museums can be warm inside) |
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Picnic in a park Flowy sundress or casual skirt + tee Straw hat, ballet flats Bring a light cardigan or blazer in case it gets breezy |
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Dinner in a bistro Elegant blouse + culottes or a wrap dress Clutch bag, statement earrings A red lip adds effortless sophistication |
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Evening walk by the Eiffel Tower Tailored trousers + fine knit top or maxi dress Lightweight trench, flat sandals Trench coats are timeless and very Parisian |
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""" |
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@tool |
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def get_wardrobe() -> str: |
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""" |
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Get all the clothes in the wardrobe, together with their colors, and descriptions |
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Args: |
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Returns: |
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str: list of all the clothes in the wardrobe |
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""" |
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return """ |
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| Category | Item | Quantity | Notes | |
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| --------------- | ------------------------------- | -------- | ----------------------------------------------------- | |
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| **Tops** | T-shirts | 5–7 | Neutral colors for versatility; cotton or quick-dry | |
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| | Polo shirts | 2–3 | For smart-casual outings | |
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| | Button-down shirts | 2–4 | Long or short sleeve; wrinkle-resistant options ideal | |
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| | Lightweight sweater | 1–2 | Great for layering | |
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| | Hoodie or sweatshirt | 1 | Casual comfort and warmth | |
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| | Jacket (lightweight) | 1 | Windbreaker or packable jacket depending on climate | |
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| | Formal jacket/blazer | 1 | Optional; for work, dining, or events | |
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| **Bottoms** | Jeans or casual trousers | 1–2 | Dark wash preferred | |
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| | Chinos or dress pants | 1 | For dressier occasions | |
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| | Shorts | 2–3 | Depends on destination climate | |
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| | Joggers or travel pants | 1 | Great for flights and comfort | |
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| **Underwear** | Underwear | 7–10 | Breathable and comfortable | |
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| | Socks | 7–10 | Mix of athletic and dress | |
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| **Sleepwear** | Pajamas or sleepwear | 1–2 | Lightweight and compact | |
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| **Outerwear** | Rain jacket or shell | 1 | Waterproof for variable weather | |
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| | Warm jacket/coat | 1 | Only if traveling to a cold region | |
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| **Footwear** | Sneakers or casual shoes | 1–2 | One for walking, one for style | |
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| | Dress shoes or loafers | 1 | Optional depending on itinerary | |
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| | Sandals or flip-flops | 1 | For hot weather or showers | |
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| **Accessories** | Belt | 1–2 | Casual and/or dress | |
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| | Hat or cap | 1 | Sun protection | |
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| | Scarf/gloves/beanie | 1 set | Only for cold destinations | |
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| | Swimwear | 1–2 | If beach/pool included | |
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| **Other** | Workout clothes | 1–2 sets | Moisture-wicking; only if planning to exercise | |
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| | Compression socks (for flights) | 1–2 | Recommended for long-haul flights | |
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| | Laundry bag | 1 | Helps keep clean/dirty separate | |
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""" |
