Update app.py

app.py

@@ -1,17 +1,23 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
 import datetime
 import requests
 import pytz
 import yaml
 from tools.final_answer import FinalAnswerTool
 
+# Add imports for the new weather tool
+import openmeteo_requests
+import requests_cache
+from retry_requests import retry
+import pandas as pd
+
 from Gradio_UI import GradioUI
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
+# Below is an example of a tool that does nothing. Amaze us with your creativity!
 @tool
-def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
-    #Keep this format for the description / args / args description but feel free to modify the tool
-    """A tool that does nothing yet 
+def my_custom_tool(arg1: str, arg2: int) -> str:
+    # Keep this format for the description / args / args description but feel free to modify the tool
+    """A tool that does nothing yet
     Args:
         arg1: the first argument
         arg2: the second argument
@@ -33,29 +39,94 @@ def get_current_time_in_timezone(timezone: str) -> str:
     except Exception as e:
         return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
+# A new tool to get weather forecast using Open-Meteo
+@tool
+def get_weather_forecast(city: str) -> str:
+    """Fetches the hourly temperature forecast for a specified city.
+    
+    Args:
+        city: The name of the city (e.g., 'London', 'New York').
+    """
+    try:
+        # Set up the Open-Meteo API client with cache and retry on error
+        cache_session = requests_cache.CachedSession('.cache', expire_after=3600)
+        retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
+        openmeteo = openmeteo_requests.Client(session=retry_session)
+
+        # First, use the Open-Meteo Geocoding API to get coordinates for the city
+        geocoding_url = f"https://geocoding-api.open-meteo.com/v1/search?name={city}&count=1&language=en&format=json"
+        geocoding_response = requests.get(geocoding_url)
+        geocoding_response.raise_for_status()
+        geocoding_data = geocoding_response.json()
+        
+        if not geocoding_data.get('results'):
+            return f"Error: Could not find coordinates for city '{city}'."
+
+        result = geocoding_data['results'][0]
+        latitude = result['latitude']
+        longitude = result['longitude']
+        
+        # Next, use the weather API to get the forecast with the new coordinates
+        url = "https://api.open-meteo.com/v1/forecast"
+        params = {
+            "latitude": latitude,
+            "longitude": longitude,
+            "hourly": ["temperature_2m"],
+        }
+        
+        responses = openmeteo.weather_api(url, params=params)
+        response = responses[0]
+
+        hourly = response.Hourly()
+        hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
+        
+        # Create a pandas DataFrame for better presentation
+        hourly_data = {
+            "date": pd.date_range(
+                start=pd.to_datetime(hourly.Time(), unit="s", utc=True),
+                end=pd.to_datetime(hourly.TimeEnd(), unit="s", utc=True),
+                freq=pd.Timedelta(seconds=hourly.Interval()),
+                inclusive="left"
+            ),
+            "temperature_2m": hourly_temperature_2m
+        }
+        hourly_dataframe = pd.DataFrame(data=hourly_data)
+
+        # Format the output for the agent
+        forecast_summary = f"Hourly temperature forecast for {city} ({latitude}°N, {longitude}°E):\n"
+        for index, row in hourly_dataframe.iterrows():
+            forecast_summary += f"- Time {row['date'].strftime('%Y-%m-%d %H:%M')}: {row['temperature_2m']}°C\n"
+        
+        return forecast_summary
+    
+    except requests.exceptions.RequestException as e:
+        return f"An error occurred while fetching data: {str(e)}"
+    except Exception as e:
+        return f"An unexpected error occurred: {str(e)}"
+
 
 final_answer = FinalAnswerTool()
 
 # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
+# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
 
 model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-custom_role_conversions=None,
+    max_tokens=2096,
+    temperature=0.5,
+    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',  # it is possible that this model may be overloaded
+    custom_role_conversions=None,
 )
 
-
 # Import tool from Hub
 image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+duckduckgo_search = DuckDuckGoSearchTool()
 
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
-    
+
 agent = CodeAgent(
     model=model,
-    tools=[final_answer], ## add your tools here (don't remove final answer)
+    tools=[final_answer, duckduckgo_search, image_generation_tool, get_weather_forecast],  # Added the new weather tool
     max_steps=6,
     verbosity_level=1,
     grammar=None,
@@ -65,5 +136,4 @@ agent = CodeAgent(
     prompt_templates=prompt_templates
 )
 
-
 GradioUI(agent).launch()