Basic functionality test

app.py

@@ -3,6 +3,20 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from google.colab import userdata
+from smolagents import ToolCallingAgent, CodeAgent, LiteLLMModel
+
+from tools import DuckDuckGoSearchTool, WeatherInfoTool, HubStatsTool
+
+# Initialize the web search tool
+search_tool = DuckDuckGoSearchTool()
+
+# Initialize the weather tool
+weather_info_tool = WeatherInfoTool()
+
+# Initialize the Hub stats tool
+hub_stats_tool = HubStatsTool()
+
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -13,11 +27,28 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+
+        ## new agent code
+        model = LiteLLMModel(model_id="gemini/gemini-2.5-pro-preview-06-05", api_key=os.getenv(key="GEMINI_API_KEY"))
+        agent = CodeAgent(
+            model=model,
+            tools=[DuckDuckGoSearchTool()]
+        )
+        answer = agent.run(question)
+        
+        print(f"Agent returning answer: {answer}")
+        return answer
+
+def test_agent():
+    # Use Google Gemini
+    model = LiteLLMModel(model_id="gemini/gemini-2.5-pro-preview-06-05", api_key=os.getenv(key="GEMINI_API_KEY"))
+
+    agent = ToolCallingAgent(tools=[DuckDuckGoSearchTool()], model=model)
+    agent.run("Search for the best music recommendations for a party at the Wayne's mansion.")
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

tools.py

@@ -0,0 +1,51 @@
+from smolagents import DuckDuckGoSearchTool
+from smolagents import Tool
+import random
+from huggingface_hub import list_models
+
+
+class WeatherInfoTool(Tool):
+    name = "weather_info"
+    description = "Fetches dummy weather information for a given location."
+    inputs = {
+        "location": {
+            "type": "string",
+            "description": "The location to get weather information for."
+        }
+    }
+    output_type = "string"
+
+    def forward(self, location: str):
+        # Dummy weather data
+        weather_conditions = [
+            {"condition": "Rainy", "temp_c": 15},
+            {"condition": "Clear", "temp_c": 25},
+            {"condition": "Windy", "temp_c": 20}
+        ]
+        # Randomly select a weather condition
+        data = random.choice(weather_conditions)
+        return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"
+
+class HubStatsTool(Tool):
+    name = "hub_stats"
+    description = "Fetches the most downloaded model from a specific author on the Hugging Face Hub."
+    inputs = {
+        "author": {
+            "type": "string",
+            "description": "The username of the model author/organization to find models from."
+        }
+    }
+    output_type = "string"
+
+    def forward(self, author: str):
+        try:
+            # List models from the specified author, sorted by downloads
+            models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
+            
+            if models:
+                model = models[0]
+                return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
+            else:
+                return f"No models found for author {author}."
+        except Exception as e:
+            return f"Error fetching models for {author}: {str(e)}"