Primer Commit

.gitignore

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+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# VSCode
+.vscode/
+*.code-workspace
+
+# PyCharm
+.idea/
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Logs
+*.log
+logs/
+*.log.*
+
+# Local development settings
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Database
+*.db
+*.sqlite3
+*.sqlite
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db

app.py

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+import gradio as gr
+import random
+from smolagents import GradioUI, CodeAgent, HfApiModel
+from smolagents import Tool
+from langchain_community.retrievers import BM25Retriever
+from langchain.docstore.document import Document
+import datasets
+from smolagents import DuckDuckGoSearchTool
+from smolagents import Tool
+import random
+from huggingface_hub import list_models
+import os
+from huggingface_hub import login
+from dotenv import load_dotenv
+
+# Cargar variables de entorno desde el archivo .env
+load_dotenv()
+
+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)
+
+
+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)}"
+
+
+
+
+# Initialize the Hugging Face model
+token = os.getenv("HUGGINGFACE_TOKEN")
+if not token:
+    raise ValueError("Por favor, configura HUGGINGFACE_TOKEN en el archivo .env")
+login(token=token)
+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
+    max_steps=5,  # Limitar el número máximo de pasos
+)
+
+if __name__ == "__main__":
+    GradioUI(alfred).launch(share=False)