init

.gitignore

@@ -0,0 +1 @@
+venv/

app.py

@@ -1,64 +1,33 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import random
+from smolagents import GradioUI, CodeAgent, HfApiModel
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Import our custom tools from their modules
+from tools import DuckDuckGoSearchTool, WeatherInfoTool, HubStatsTool
+from retriever import load_guest_dataset
 
+# Initialize the Hugging Face model
+model = HfApiModel()
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Initialize the web search tool
+search_tool = DuckDuckGoSearchTool()
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Initialize the weather tool
+weather_info_tool = WeatherInfoTool()
 
-    messages.append({"role": "user", "content": message})
+# Initialize the Hub stats tool
+hub_stats_tool = HubStatsTool()
 
-    response = ""
+# Load the guest dataset and initialize the guest info tool
+guest_info_tool = load_guest_dataset()
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# 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__":
-    demo.launch()
+    GradioUI(alfred).launch()

requirements.txt

@@ -1 +1,5 @@
-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+datasets 
+smolagents 
+langchain-community 
+rank_bm25

retriever.py

@@ -0,0 +1,53 @@
+from smolagents import Tool
+from langchain_community.retrievers import BM25Retriever
+from langchain.docstore.document import Document
+import datasets
+
+
+class GuestInfoRetrieverTool(Tool):
+    name = "guest_info_retriever"
+    description = "Retrieves detailed information about gala guests based on their name or relation."
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "The name or relation of the guest you want information about."
+        }
+    }
+    output_type = "string"
+
+    def __init__(self, docs):
+        self.is_initialized = False
+        self.retriever = BM25Retriever.from_documents(docs)
+       
+
+    def forward(self, query: str):
+        results = self.retriever.get_relevant_documents(query)
+        if results:
+            return "\n\n".join([doc.page_content for doc in results[:3]])
+        else:
+            return "No matching guest information found."
+
+
+def load_guest_dataset():
+    # Load the dataset
+    guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+    # Convert dataset entries into Document objects
+    docs = [
+        Document(
+            page_content="\n".join([
+                f"Name: {guest['name']}",
+                f"Relation: {guest['relation']}",
+                f"Description: {guest['description']}",
+                f"Email: {guest['email']}"
+            ]),
+            metadata={"name": guest["name"]}
+        )
+        for guest in guest_dataset
+    ]
+
+    # Return the tool
+    return GuestInfoRetrieverTool(docs)
+
+
+

tools.py

@@ -0,0 +1,56 @@
+from smolagents import DuckDuckGoSearchTool
+from smolagents import Tool
+import random
+from huggingface_hub import list_models
+
+
+# Initialize the DuckDuckGo search tool
+#search_tool = DuckDuckGoSearchTool()
+
+
+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)}"
+