Weather Outfit Advisor#

Overview#

Provides personalized outfit recommendations based on real-time weather data for your destination and your personal clothing preferences.

Workflow#

Step 1: Check Information Completeness#

Before providing recommendations, must confirm the following three key pieces of information:

1. Destination (city name)
2. Date (specific date or date range)
3. Clothing Preferences (style preference, temperature sensitivity, special needs, etc.)

Information Extraction Strategy:

• Extract explicit destination, date, and preferences from user input
• Recognize relative date terms ("tomorrow", "day after tomorrow", "next Monday", etc.) and convert to specific dates
• If any item is missing, must ask the user first

Relative Date Conversion Rules:

• "tomorrow" → current date + 1 day
• "day after tomorrow" → current date + 2 days
• "three days later" → current date + 3 days
• "next Monday" → next Monday's date
• "this weekend" → this Saturday or Sunday (needs confirmation)

Example:

text

User: "I'm going to Hangzhou tomorrow"
→ Extract: destination = Hangzhou, date = tomorrow
→ Convert: get current date (e.g., 2026-04-01), calculate tomorrow = 2026-04-02
→ Missing: clothing preferences
→ Action: ask about clothing preferences

Step 2: Collect Necessary Information#

Adopt different clarification strategies based on the number of missing items:

Strategy A: Missing Only 1 Item (Targeted Question)

Ask only about the missing item:

If only missing clothing preferences:

text

Sure, I'll help you check the weather for [city] on [date].

To make the recommendation more suitable for you, please tell me about your clothing preferences:
- Are you usually sensitive to cold or heat?
- What style do you prefer? (casual/business/sports, etc.)
- Any special needs? (such as formal occasions, outdoor activities, etc.)

If only missing date:

text

When are you planning to travel? (Please provide a specific date, or say "tomorrow", "day after tomorrow", etc.)

If only missing destination:

text

What is your travel destination? (Please provide the city name)

Strategy B: Missing 2-3 Items (Ask All at Once)

When multiple items are missing, list all questions at once:

text

To provide you with the most suitable outfit recommendations, I need some information:

1. What is your travel destination?
2. When exactly are you traveling? (You can say "tomorrow", "April 5th", etc.)
3. What are your usual clothing habits? (For example, sensitive to cold/heat, preferred style, etc.)

Relative Date Handling

When users use relative dates:

Recognize Keywords:

• tomorrow, day after tomorrow, three days later
• next Monday, next Tuesday... next Sunday
• this weekend, next weekend
• next week, next month

Conversion Logic:

1. Get current system date
2. Calculate specific date based on relative term
3. Confirm the converted date in the response

Example:

text

User: "I'm going to Hangzhou tomorrow"
→ System gets current date: 2026-04-01
→ Calculate: tomorrow = 2026-04-02
→ Response confirmation: "Sure, I'll check the weather for Hangzhou tomorrow (April 2nd)..."

Step 3: Query Weather Data#

Use the provided Python script (recommended):

bash

# Basic usage
python scripts/get_weather.py <city> [date]

# Examples
python scripts/get_weather.py Hangzhou 2026-04-02
python scripts/get_weather.py Beijing tomorrow
python scripts/get_weather.py Shanghai tomorrow

Script Features:

• ✅ Automatic relative date parsing (tomorrow, day after tomorrow, next Monday, etc.)
• ✅ City name normalization (supports Chinese/English and common aliases)
• ✅ Calls wttr.in API to get weather data
• ✅ Returns structured JSON data
• ✅ Includes current weather and forecast information

Output Example (JSON format):

json

{
  "success": true,
  "city": "Hangzhou",
  "query_date": "2026-04-01",
  "target_date": "2026-04-02",
  "current": {
    "temp_c": 19,
    "feels_like_c": 19,
    "humidity": 56,
    "weather_desc": "Patchy rain nearby",
    "wind_speed_kmph": 6,
    "uv_index": 5
  },
  "forecast": {
    "max_temp_c": 21,
    "min_temp_c": 11,
    "avg_humidity": 60,
    "daily_chance_of_rain": 70,
    "weather_desc": "Light drizzle"
  }
}

Extract Key Information from Output:

python

import json

# Assume weather_data is the JSON returned by the script
current = weather_data['current']
forecast = weather_data['forecast']

temperature_range = f"{forecast['min_temp_c']}-{forecast['max_temp_c']}°C"
weather_condition = forecast['weather_desc']
humidity = current['humidity']
rain_chance = forecast['daily_chance_of_rain']

Alternative: Direct API Call (if script is unavailable):

Current Weather API:

text

https://wttr.in/{city}?format=j1

Example:

bash

curl "https://wttr.in/Hangzhou?format=j1"

Parse JSON Response for Key Information:

python

import json

# Current weather
current = data['current_condition'][0]
temperature = current['temp_C']  # Celsius
feels_like = current['FeelsLikeC']  # Feels like temperature
humidity = current['humidity']  # Humidity
weather_desc = current['weatherDesc'][0]['value']  # Weather description
wind_speed = current['windspeedKmph']  # Wind speed

# Weather forecast
forecast = data['weather'][0]
max_temp = forecast['maxtempC']  # Max temperature
min_temp = forecast['mintempC']  # Min temperature

Alternative: OpenWeatherMap API (requires free API key):

If wttr.in is unavailable, you can use OpenWeatherMap:

API Endpoint:

text

https://api.openweathermap.org/data/2.5/weather?q={city}&appid={API_KEY}&units=metric&lang=en

Forecast API:

text

https://api.openweathermap.org/data/2.5/forecast?q={city}&appid={API_KEY}&units=metric&lang=en

Note:

• {API_KEY} needs to be requested from https://openweathermap.org/api (free version is sufficient)
• units=metric uses Celsius
• lang=en returns English description

Step 4: Analyze Weather Data#

Extract key information from the API response:

json

{
  "main": {
    "temp": 25,        // Current temperature
    "feels_like": 27,  // Feels like temperature
    "humidity": 60     // Humidity
  },
  "weather": [
    {
      "description": "Clear",  // Weather condition
      "main": "Clear"
    }
  ],
  "wind": {
    "speed": 3.5       // Wind speed
  }
}

Step 5: Generate Outfit Recommendations#

Generate recommendations based on weather data and user preferences:

Temperature Range Reference

Temperature RangeOutfit Recommendation< 5°CHeavy down jacket, thermal underwear, sweater, scarf, gloves, hat5-10°CThick coat, padded jacket, knitwear, thin sweater10-15°CTrench coat, jacket, hoodie, long-sleeve T-shirt15-20°CLight jacket, denim jacket, long-sleeve shirt, T-shirt + cardigan20-25°CLong-sleeve T-shirt, thin shirt, single layer clothing25-30°CShort-sleeve T-shirt, shirt, skirt, shorts> 30°CBreathable short-sleeve, tank top, sun protection clothing, sun hat

Consider Other Factors

Rainy Days:

• Carry umbrella or raincoat
• Choose waterproof shoes
• Avoid light-colored clothing

Windy Days:

• Avoid skirts or choose anti-exposure styles
• Wear windproof jacket
• Secure hat properly

High Humidity:

• Choose breathable quick-dry fabrics
• Avoid heavy clothing

Strong UV:

• Apply sunscreen
• Wear sunglasses
• Wear sun protection clothing or long sleeves

Add Image Reference (Optional but Recommended)

After providing outfit recommendations, call the image search script to provide visual references:

bash

# Search city street style
python scripts/search_images.py "{city} street style fashion" 5

# Search seasonal outfits
python scripts/search_images.py "{city} {season} outfit" 5

# Search occasion-specific outfits
python scripts/search_images.py "{city} casual wear" 5

Examples:

bash

# Paris street style
python scripts/search_images.py "Paris street style fashion" 5

# Hangzhou casual outfits
python scripts/search_images.py "Hangzhou casual outfit" 5

Step 6: Output Format#

Output recommendations in the following format:

markdown

## 📍 Destination: [City Name]
## 📅 Date: [Travel Date]
## 🌤️ Weather Overview

- **Temperature**: X°C (feels like Y°C)
- **Weather Condition**: [Clear/Cloudy/Rainy, etc.]
- **Humidity**: X%
- **Wind Speed**: X m/s

## 👔 Outfit Recommendations

### Recommended Outfit
- Top: [Specific recommendation]
- Bottom: [Specific recommendation]
- Shoes: [Specific recommendation]
- Accessories: [Specific recommendation]

### Notes
- [Special reminder 1]
- [Special reminder 2]

### Backup Plan
[Alternative suggestions for weather changes or other situations]

## 🖼️ Outfit Reference Images

[Call search_images.py script to search related images]

### Style References
- [Image 1 description and URL]
- [Image 2 description and URL]
- [Image 3 description and URL]

Special Cases Handling#

API Call Failure#

If API call fails or returns error:

text

Sorry, temporarily unable to get weather data for [city name]. Possible reasons:
1. City name recognition failed, please check spelling
2. API service temporarily unavailable
3. Network connection issue

Suggestions:
- Check if city name is correct
- Try again later
- Or tell me the current weather conditions (temperature, weather), I can also provide recommendations

Extreme Weather Warning#

If extreme weather is detected (typhoon, blizzard, heavy rain warning, etc.):

text

⚠️ **Weather Warning**: [City Name] is about to experience [extreme weather type]

According to latest weather forecast:
- [Describe extreme weather situation]
- [Impact level and timing]

**Recommendations**:
1. Reconsider if travel is necessary
2. If travel is required, prepare adequate protection:
   - [Specific protective measures]
3. Monitor latest warnings from local meteorological department
4. Recommended items: [Special equipment like life jacket, anti-slip shoes, etc.]

Multi-Day Trip#

If user provides a date range:

text

Detected your trip is a multi-day travel (Month Day - Month Day), I will:
1. Query weather forecast for each day separately
2. Provide daily outfit recommendations
3. Suggest clothing packing list (considering laundry needs)

Personalization Adjustments#

Adjust recommendations based on user's clothing habits:

Cold-sensitive users: Recommend adding extra warm layers to base outfit Heat-sensitive users: Recommend more breathable fabrics, reduce layers Business needs: Maintain professional appearance while ensuring comfort Outdoor sports: Prioritize functional clothing (quick-dry, waterproof, windproof) Pregnant/elderly/children: Pay extra attention to warmth and comfort

Example Dialogues#

Example 1: Complete Information#

User: I'm going to Hangzhou tomorrow, I'm usually sensitive to cold, prefer casual style.

Assistant: Step 1: Query weather

bash

python scripts/get_weather.py Hangzhou tomorrow

Step 2: Parse data Extract from returned JSON:

• Temperature range: 11-21°C
• Weather condition: Patchy rain nearby
• Humidity: 56%
• Rain chance: 70%

Step 3: Generate recommendations

According to weather forecast, Hangzhou tomorrow:

• Temperature: 11-21°C
• Weather: Patchy rain nearby
• Humidity: 56%

Outfit recommendations:

• Top: Long-sleeve T-shirt + knit cardigan + thin trench coat (considering you're sensitive to cold, recommend bringing thermal underwear as backup)
• Bottom: Jeans or casual pants
• Shoes: Sneakers or casual shoes
• Accessories: Can bring a thin scarf, umbrella

Example 2: Incomplete Information#

User: I'm going out to play in a few days, how should I dress?

Assistant: To provide you with the most suitable outfit recommendations, I need some information:

1. What is your travel destination?
2. When exactly are you traveling?
3. What are your usual clothing habits? Any special preferences (such as sensitive to cold/heat, prefer casual/business style, etc.)?

Example 3: Real Call Case#

User: I'm going to Hangzhou tomorrow, I'm quite heat-sensitive

Assistant: Execution Flow:

1. Information Extraction:

   • Destination: Hangzhou ✓
   • Date: tomorrow (relative date) → convert to specific date
   • Clothing preferences: heat-sensitive ✓

2. Date Conversion:

   • Get current system date: 2026-04-01
   • Calculate: tomorrow = 2026-04-02
   • Confirm: "Sure, I'll check the weather for Hangzhou tomorrow (April 2nd)"

3. Use wttr.in API to query Hangzhou weather: curl "https://wttr.in/Hangzhou?format=j1"

4. Parse JSON to get data:

   • Current temperature: 19°C
   • Max temp: 21°C, Min temp: 11°C
   • Weather: Patchy rain nearby
   • Humidity: 56%

5. Adjust recommendations based on heat-sensitive preference

Then output complete outfit recommendations...

Example 4: Boundary Case Testing#

Scenario A: Seriously Insufficient Information

User: How should I dress?

Assistant: To provide you with the most suitable outfit recommendations, I need some information:

1. What is your travel destination?
2. When exactly are you traveling? (You can say "tomorrow", "April 5th", etc.)
3. What are your usual clothing habits? (For example, sensitive to cold/heat, preferred style, etc.)

Scenario B: Only Partial Information

User: I'm going to Shanghai the day after tomorrow

Assistant: Sure, I'll help you check the weather for Shanghai the day after tomorrow.

To make the recommendation more suitable for you, please tell me about your clothing preferences:

• Are you usually sensitive to cold or heat?
• What style do you prefer? (casual/business/sports, etc.)
• Any special needs? (such as formal occasions, outdoor activities, etc.)

(Wait for user response, then query weather and provide complete recommendations)

Scenario C: Multi-Day Trip

User: I'm going to Beijing on business from next Monday to Friday, I'm sensitive to cold

Assistant: Execution Flow:

1. Recognize this is a date range: next Monday to Friday (total 5 days)
2. Query weather forecast for each of the 5 days separately
3. Provide daily outfit recommendations
4. Give overall suggestions for clothing packing list (considering laundry and matching)

Output format:

markdown

## 📍 Destination: Beijing
## 📅 Trip: Next Monday to Friday (April 7th - April 11th)

### Daily Outfit Recommendations

#### Monday (April 7th)
- Weather: Clear, 8-18°C
- Recommendation: ...

#### Tuesday (April 8th)
- Weather: Cloudy, 10-20°C
- Recommendation: ...

...

### Clothing Packing List
Considering 5-day trip and temperature variations, recommend bringing:
- Tops: X pieces (including...)
- Bottoms: X pieces
- Outerwear: X pieces
- Other accessories: ...

Example 5: Special Cases Handling#

Extreme Weather Warning

If extreme weather is detected (typhoon, heavy rain red warning, etc.):

text

⚠️ **Weather Warning**: [City Name] is about to experience [extreme weather type]

According to latest weather forecast:
- [Describe extreme weather situation]
- [Impact level and timing]

**Recommendations**:
1. Reconsider if travel is necessary
2. If travel is required, prepare adequate protection:
   - [Specific protective measures]
3. Monitor latest warnings from local meteorological department
4. Recommended items: [Special equipment like life jacket, anti-slip shoes, etc.]

API Call Failure

If API call fails or returns error:

text

Sorry, temporarily unable to get weather data for [city name]. Possible reasons:
1. City name recognition failed, please check spelling
2. API service temporarily unavailable
3. Network connection issue

Suggestions:
- Check if city name is correct
- Try again later
- Or tell me the current weather conditions (temperature, weather), I can also provide recommendations

Prompt Optimization#

When actually using, can add the following prompt optimization suggestions based on situation:

• Remind users that areas with large temperature differences between morning and evening need "onion-style" dressing
• Suggest bringing easy-to-match items to deal with weather changes
• Recommend practical layering combinations
• Consider luggage space limitations, suggest versatile items

Appendix: Script Usage Instructions#

get_weather.py Script#

Location: scripts/get_weather.py

Features:

1. ✅ Supports Chinese/English city names
2. ✅ Automatic relative date parsing (tomorrow, day after tomorrow, next Monday, etc.)
3. ✅ City name normalization (e.g., "Beijing" → "Beijing")
4. ✅ Returns structured JSON data
5. ✅ Includes current weather and forecast information
6. ✅ No API key required (uses wttr.in free service)

Usage:

bash

# Basic syntax
python scripts/get_weather.py <city> [date]

# Specific date
python scripts/get_weather.py Hangzhou 2026-04-02

# Relative date (English)
python scripts/get_weather.py Beijing tomorrow
python scripts/get_weather.py Shanghai day after tomorrow

# Relative date (Chinese)
python scripts/get_weather.py Guangzhou tomorrow
python scripts/get_weather.py Shenzhen day after tomorrow

# No date specified (defaults to today)
python scripts/get_weather.py Chengdu

Output Format:

• stdout: JSON format weather data (for program processing)
• stderr: Human-readable formatted output (for direct viewing)

JSON Data Structure:

json

{
  "success": true,
  "city": "City English Name",
  "query_date": "Query Date",
  "target_date": "Target Date",
  "current": {
    "temp_c": Current Temperature (Celsius),
    "feels_like_c": Feels Like Temperature,
    "humidity": Humidity Percentage,
    "weather_desc": "Weather Description",
    "wind_speed_kmph": Wind Speed (km/h),
    "uv_index": UV Index
  },
  "forecast": {
    "max_temp_c": Max Temperature,
    "min_temp_c": Min Temperature,
    "avg_humidity": Average Humidity,
    "daily_chance_of_rain": Rain Chance,
    "weather_desc": "Weather Condition"
  }
}

Error Handling: If query fails, returns:

json

{
  "success": false,
  "error": "Error message",
  "message": "Failed to get weather data: specific reason"
}

Dependencies:

• Python 3.6+
• Standard library: sys, json, urllib.request, datetime
• No additional third-party libraries required

search_images.py Script#

Location: scripts/search_images.py

Features:

1. ✅ Searches fashion/outfit images related to destination
2. ✅ Supports multiple image APIs (Pexels, Pixabay, etc.)
3. ✅ Returns structured image information (URL, photographer, dimensions, etc.)
4. ✅ Automatically generates optimized search keywords
5. ✅ Provides manual search alternative

Supported APIs:

APIRequires KeyFree QuotaRecommendationPexels✅ Yes20,000/month⭐⭐⭐⭐⭐Pixabay✅ Yes500/day⭐⭐⭐⭐Bing Search❌ NoLimited⭐⭐⭐

Usage:

bash

# Basic syntax
python scripts/search_images.py <query> [count]

# Search Paris street style
python scripts/search_images.py "Paris street style fashion" 5

# Search Hangzhou casual outfits
python scripts/search_images.py "Hangzhou casual outfit" 5

# Search specific season
python scripts/search_images.py "Beijing winter fashion" 10

Output Format:

• stdout: JSON format image list (for program processing)
• stderr: Human-readable formatted output (for viewing)

JSON Data Structure:

json

{
  "success": true,
  "query": "Search Keyword",
  "total_results": 1000,
  "images": [
    {
      "url": "Large Image URL",
      "thumbnail": "Thumbnail URL",
      "photographer": "Photographer Name",
      "width": 1920,
      "height": 1080,
      "alt": "Image Description"
    }
  ]
}

Smart Keyword Generation:

Script automatically generates optimized search terms based on city context:

• {city} street style fashion - Street style
• {city} outfit ideas - Outfit inspiration
• {city} casual wear - Casual outfits
• what to wear in {city} - Travel outfits

API Key Configuration:

Recommend using Pexels API (highest quality):

1. Apply for free API key: https://www.pexels.com/api/
2. Set in script:

python

pexels_api_key = "your_api_key_here"

1. Or via environment variable:

bash

export PEXELS_API_KEY="your_api_key_here"

Fallback Strategy:

If API key is not configured, script will:

1. Provide manual search link
2. List available API options
3. User can visit search link to view images manually

Usage Scenario:

Add image references in outfit recommendations:

markdown

## 🖼️ Outfit References

Based on Paris fashion style, recommend the following references:

📸 **Street Style**
- Image 1: [Description] - [View Image](URL)
- Image 2: [Description] - [View Image](URL)
- Image 3: [Description] - [View Image](URL)

💡 **Styling Tips**:
- Learn layering techniques from images
- Pay attention to color coordination (mainly neutral colors)
- Choose appropriate accessories (scarves, bags)

Notes:

• Image copyright belongs to original photographer
• For personal reference only, not for commercial use
• Respect photographers' work
• Recommend using high-quality image API for better experience