Upload ai_agents_course_submission.py

ai_agents_course_submission.py

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+# -*- coding: utf-8 -*-
+"""AI Agents Course_Submission.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/138KHbO69DpJpUvuTVAvsZ53EyXZAdr6K
+"""
+
+!pip install smolagents -U
+
+pip install duckduckgo-search
+
+!pip install requests
+
+#login to hugging face
+from huggingface_hub import notebook_login
+
+notebook_login()
+
+from smolagents import CodeAgent, tool, DuckDuckGoSearchTool, InferenceClientModel
+import requests
+
+@tool
+def suggest_outfit(occasion: str) -> str:
+    """
+    Suggests an outfit based on the occasion.
+    Args:
+        occasion (str): The type of occasion for the event. Allowed values are:
+                        - "casual": Outfit for casual event.
+                        - "formal": Outfit for formal event.
+                        - "active": Outfit for doing sport.
+                        - "custom": Custom outfit.
+    """
+    if occasion == "casual":
+        return "T-shirt, Jeans and sneakers."
+    elif occasion == "formal":
+        return "White shirt, tie, blue suit and Oxford shoes."
+    elif occasion == "active":
+        return "Jogging trousers, T-shirt and trainers."
+    else:
+        return "Custom outfit for the fashion advisor."
+
+@tool
+def define_coordinates(city: str) -> dict:
+    """
+    Returns a dictionary with the geographical coordinates of a city.
+    Args:
+        city (str): A city name.
+    Returns:
+        A dictionary with the latitude, longitude and timezone of the city
+    """
+    city = city.strip()
+    if not city:
+        return {"error": "City name cannot be empty."}
+
+    url = f"https://geocoding-api.open-meteo.com/v1/search?name={city}&count=1"
+
+    try:
+        response = requests.get(url)
+        data = response.json()
+
+        if "results" not in data or not data["results"]:
+            return {"error": f"No location found for city: {city}"}
+
+        result = data["results"][0]
+        return {
+            "latitude": result["latitude"],
+            "longitude": result["longitude"],
+            "timezone": result["timezone"]
+        }
+
+    except Exception as e:
+        return {"error": str(e)}
+
+@tool
+def get_weather(city: str) -> dict:
+    """
+    Returns a dictionary with the current weather conditions in a city.
+    Args:
+        city (str): A city name.
+    Returns:
+        A dictionary with: temperature, wind_speed, weather_code, weather_description and a summary of the current weather.
+    """
+    coords = define_coordinates(city)
+    if "error" in coords:
+     return {"error": coords["error"]}
+
+    url = f'https://api.open-meteo.com/v1/forecast?latitude={coords["latitude"]}&longitude={coords["longitude"]}&current_weather=true&timezone={coords["timezone"]}'
+
+    try:
+        response = requests.get(url)
+        data = response.json()
+        temperature = data["current_weather"]["temperature"]
+        wind_speed = data["current_weather"]["windspeed"]
+        weather_code = data["current_weather"]["weathercode"]
+
+        return {
+                "temperature": temperature,
+                "wind_speed": wind_speed,
+                "weather_code": weather_code,
+           }
+
+
+    except Exception as e:
+        return {"error": str(e)}
+
+@tool
+def classify_weather(city: str) -> str:
+    """
+    Classifies the weather in the city based on a conversion table.
+    Args:
+      city (str): A city name.
+    Return:
+        Weather description based on the following conversion table:
+        0	Clear sky
+        1–3	Mainly clear to partly cloudy
+        45, 48	Fog and depositing rime fog
+        51–57	Drizzle (light to dense)
+        61–67	Rain (light to heavy)
+        71–77	Snow fall (light to heavy)
+        80–82	Rain showers
+        85–86	Snow showers
+        95	Thunderstorm
+        96–99	Thunderstorm with hail
+    """
+    weather = get_weather(city)
+    if "error" in weather:
+        return f"Unable to classify weather due to error: {weather['error']}"
+
+    temperature = weather["temperature"]
+    wind_speed = weather["wind_speed"]
+    weather_code = weather["weather_code"]
+
+    if weather_code == 0:
+        weather_description = "clear sky"
+    elif 1 <= weather_code <= 3:
+        weather_description = "mainly clear to partly cloudy"
+    elif weather_code in [45, 48]:
+        weather_description = "fog or depositing rime fog"
+    elif 51 <= weather_code <= 57:
+        weather_description = "drizzle (light to dense)"
+    elif 61 <= weather_code <= 67:
+        weather_description = "rain (light to heavy)"
+    elif 71 <= weather_code <= 77:
+        weather_description = "snow fall (light to heavy)"
+    elif 80 <= weather_code <= 82:
+        weather_description = "rain showers"
+    elif 85 <= weather_code <= 86:
+        weather_description = "snow showers"
+    elif weather_code == 95:
+        weather_description = "thunderstorm"
+    elif 96 <= weather_code <= 99:
+        weather_description = "thunderstorm with hail"
+    else:
+        weather_description = "unknown weather conditions"
+
+    return (
+        f"The current temperature in {city} is {temperature}°C, "
+        f"with a wind speed of {wind_speed} km/h. "
+        f"Weather description: {weather_description}."
+    )
+
+agent = CodeAgent(tools=[DuckDuckGoSearchTool(), suggest_outfit, define_coordinates, get_weather, classify_weather], model=InferenceClientModel())
+
+response = agent.run("""
+First, find the current weather in Rome.
+Then, classify the weather based on standard weather codes (e.g. clear, rain, snow, etc).
+Third, assess what kind of outfit would be comfortable for a park run given this weather.
+Fourth, call the appropriate tool to suggest an outfit for that context.
+Finally, summarize both the weather and outfit recommendations clearly in one paragraph.
+
+""")
+print(response)
+
+import json
+
+entry = {
+    "task_id": "task_id_rome_weather_outfit",
+    "model_answer": "The current weather in Rome is clear with a temperature of 29.5°C and a wind speed of 13.0 km/h. A comfortable outfit for a park run in this weather would be jogging trousers, a T-shirt, and trainers.",
+    "reasoning_trace": [
+        "Step 1: Called get_weather('Rome') to fetch current conditions.",
+        "Step 2: Used classify_weather('Rome') to interpret weather code (code 0 = clear sky).",
+        "Step 3: Determined that the task involves running in a park, which corresponds to the 'active' occasion.",
+        "Step 4: Called suggest_outfit('active') to retrieve clothing suitable for physical activity.",
+        "Step 5: Composed a final answer combining weather data and outfit suggestion into one summary."
+    ]
+}
+
+with open("submission.jsonl", "w") as f:
+    f.write(json.dumps(entry) + "\n")
+
+from google.colab import files
+files.download("submission.jsonl")
+
+more_entries = [
+    {
+        "task_id": "task_id_milan_weather_outfit",
+        "model_answer": "The current weather in Milan is mainly clear with a temperature of 22.3°C and wind speed of 9.5 km/h. A suitable outfit for a walk would be a light jacket, casual trousers, and comfortable shoes.",
+        "reasoning_trace": [
+            "Step 1: Fetch Milan weather using get_weather('Milan').",
+            "Step 2: Interpret code via classify_weather — partly cloudy.",
+            "Step 3: Occasion inferred as casual walk.",
+            "Step 4: Called suggest_outfit('casual').",
+            "Step 5: Merged weather and outfit into a final summary."
+        ]
+    },
+    # Add more entries here as needed
+]
+
+with open("submission.jsonl", "a") as f:
+    for entry in more_entries:
+        f.write(json.dumps(entry) + "\n")