Update app.py

app.py

@@ -8,34 +8,6 @@ import pandas as pd
 
 from Gradio_UI import GradioUI
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
-@tool
-def f1_tack_getter(track_name: str)-> 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
-    """
-    Returns data for a specified race
-    Args:
-        track_name: A string respresenting a valid track name from the 2024 F1 calendar
-    """
-    df = pd.read_csv('./Formula1_2024season_raceResults.csv')
-    return df.groupby('Track').get_group(track_name)
-    
-@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:
-        # Create timezone object
-        tz = pytz.timezone(timezone)
-        # Get current time in that 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()
 
 # 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:
@@ -48,16 +20,34 @@ model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may
 custom_role_conversions=None,
 )
 
-
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+@tool
+def f1_tackinfo_getter(track_name: str)-> 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
+    """
+    Returns data for a specified race
+    Args:
+        track_name: A string respresenting a valid track name from the 2024 F1 calendar
+    Returns:
+        A string with information about the given race
+    """
+    df = pd.read_csv('./Formula1_2024season_raceResults.csv')
+    info =  str(df.groupby('Track').get_group(track_name))
+    client = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")
+    system_prompt = "You are an expert in F1 race analysis. You will be given data about a race and your goal is to provide a concise analysis of these recults"
+    output = client.chat.completions.create(
+        messages = [
+            {'role': 'system', 'content': system_prompt},
+            {'role': 'user', 'content': f'Here is the data about the race: {info}'}
+        ]
+    )
+    return output.choices[0].message.content
 
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
     
 agent = CodeAgent(
     model=model,
-    tools=[f1_tack_getter, final_answer], ## add your tools here (don't remove final answer)
+    tools=[f1_tackinfo_getter, final_answer], ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,