Update app.py

app.py

@@ -1,87 +1,70 @@
 
-# Import the parts of the AI framework we need.
-# - CodeAgent: Helps build our agent (the digital helper).
-# - HfApiModel: Sets up the model (the "brain") that processes text.
-# - @tool: Marks functions as "tools" our agent can use.
-from smolagents import CodeAgent, HfApiModel, tool
-
-# ------------------------------------------------------------------------------
-# Import YAML to load extra instructions from a file.
+from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
+import datetime
+import requests
+import pytz
 import yaml
-
-# ------------------------------------------------------------------------------
-# Import our helper modules.
-# - FinalAnswerTool: Packages the final answer for the user.
-# - GradioUI: Creates a simple webpage so users can interact with our agent.
 from tools.final_answer import FinalAnswerTool
-from Gradio_UI import GradioUI
 
-# ------------------------------------------------------------------------------
-# Set up our AI model.
-# The model processes text and returns answers.
-model = HfApiModel(
-    max_tokens=2096,      # Maximum length of the answer.
-    temperature=0.5,      # How creative or focused the answer is.
-    # Use this endpoint as our model's address.
-    model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',
-    # (To use the original model instead, change the model_id to 'Qwen/Qwen2.5-Coder-32B-Instruct')
-    custom_role_conversions=None
-)
+from Gradio_UI import GradioUI
 
-# ------------------------------------------------------------------------------
-# Define the simplify_text tool.
-# This tool takes technical text and asks the model to convert it into plain language.
-# The prompt instructs the model to break down complex ideas into short sentences and to define any technical terms in simple words.
+# Below is an example of a tool that does nothing. Amaze us with your creativity !
 @tool
-def simplify_text(text: str) -> str:
+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 
+    Args:
+        arg1: the first argument
+        arg2: the second argument
     """
-    Converts technical text into plain, everyday language.
-    It explains technical terms using simple definitions (in parentheses)
-    and breaks complex ideas into short, clear sentences.
+    return "What magic will you build ?"
 
+@tool
+def get_current_time_in_timezone(timezone: str) -> str:
+    """A tool that fetches the current local time in a specified timezone.
     Args:
-        text: A technical sentence or paragraph.
-
-    Returns:
-        A simplified explanation of the text.
+        timezone: A string representing a valid timezone (e.g., 'America/New_York').
     """
-    # Build a detailed prompt for the model:
-    prompt = (
-        "Please convert the following technical text into plain, everyday language as if you were talking to someone with little technical knowledge. "
-        "Break down complex ideas into simple, short sentences. If you need to use any technical terms, provide a brief definition in parentheses right after the term. "
-        "Do not use confusing jargon; instead, use simple words. \n\n"
-        f"Technical text:\n{text}\n\n"
-        "Simplified explanation:"
-    )
-    # Call the model with our prompt and get its response.
-    response = model(prompt)
-    return response
+    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:
+# 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,
+)
+
+
+# Import tool from Hub
+image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
-# ------------------------------------------------------------------------------
-# Load additional instructions from the prompts.yaml file.
-# This file contains extra guidance for how the model should behave.
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
-
-# ------------------------------------------------------------------------------
-# Create our AI agent by combining the model and our tools.
-# Our agent uses:
-#   - FinalAnswerTool to package the final answer.
-#   - simplify_text to convert technical text into plain language.
+    
 agent = CodeAgent(
     model=model,
-    tools=[
-        FinalAnswerTool(),
-        simplify_text
-    ],
-    max_steps=6,           # Limit the number of steps in reasoning.
-    verbosity_level=1,     # Keep internal debug details minimal.
+    tools=[final_answer], ## add your tools here (don't remove final answer)
+    max_steps=6,
+    verbosity_level=1,
+    grammar=None,
+    planning_interval=None,
+    name=None,
+    description=None,
     prompt_templates=prompt_templates
 )
 
-# ------------------------------------------------------------------------------
-# Launch the interactive user interface.
-# GradioUI creates a webpage so users can type in technical text and see the simplified version.
-# The launch command includes share=True so the app remains active with a public URL.
-if __name__ == "__main__":
-    GradioUI(agent).launch(server_name="0.0.0.0", server_port=7860, share=True)
+
+GradioUI(agent).launch()