added tools.py and app.py

app.py

@@ -1,7 +1,33 @@
 import gradio as gr
+import random
+from smolagents import GradioUI, CodeAgent, HfApiModel
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Import our custom tools from their modules
+from tools import DuckDuckGoSearchTool, WeatherInfoTool, HubStatsTool
+from retriever import load_guest_dataset
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Initialize the Hugging Face model
+model = HfApiModel()
+
+# 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()
+
+# Load the guest dataset and initialize the guest info tool
+guest_info_tool = load_guest_dataset()
+
+# Create Alfred with all the tools
+alfred = CodeAgent(
+    tools=[guest_info_tool, weather_info_tool, hub_stats_tool, search_tool], 
+    model=model,
+    add_base_tools=True,  # Add any additional base tools
+    planning_interval=3   # Enable planning every 3 steps
+)
+
+if __name__ == "__main__":
+    GradioUI(alfred).launch()

requirements.txt

@@ -1,4 +1,5 @@
-gradio
-langchain
-langchain-community
-smolagents
+datasets 
+smolagents 
+langchain-community 
+rank_bm25
+duckduckgo-search

tools.py

@@ -0,0 +1,56 @@
+from smolagents import DuckDuckGoSearchTool, Tool
+import random
+from huggingface_hub import list_models
+
+# Initialize the DuckDuckGo search tool
+search_tool = DuckDuckGoSearchTool()
+
+# Example usage
+# results = search_tool("Who's the current President of France?")
+# print(results)
+
+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)}"