init commit.

.env

@@ -0,0 +1 @@
+HF_API_KEY= <your_huggingface_api_key>

README.md

@@ -1,15 +1,75 @@
 ---
-title: Template Final Assignment
-emoji: 🕵🏻‍♂️
+title: Agent with smolagents + DeepSeek
+emoji: 🧠
 colorFrom: indigo
-colorTo: indigo
+colorTo: blue
 sdk: gradio
-sdk_version: 5.25.2
-app_file: app.py
+app_file: main.py
 pinned: false
-hf_oauth: true
-# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
-hf_oauth_expiration_minutes: 480
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Hugging Face Agent Project
+
+This project implements a Hugging Face agent using the smolagents framework. The agent is designed to handle various tasks by utilizing custom tools and models.
+
+## Project Structure
+
+```
+huggingface-agent-project
+├── main.py               # Entry point for the agent
+├── agent_config.py       # Configuration and model loading
+├── tools                 # Directory for custom tools
+│   ├── __init__.py       # Package initializer for tools
+│   ├── wiki_tool.py      # Tool for fetching Wikipedia summaries
+│   ├── web_search_tool.py # Tool for performing web searches using DuckDuckGo
+│   ├── excel_analysis_tool.py # Tool for analyzing Excel files
+├── requirements.txt      # Project dependencies
+└── README.md             # Project documentation
+```
+
+## Setup Instructions
+
+1. Clone the repository:
+   ```
+   git clone <repository-url>
+   cd huggingface-agent-project
+   ```
+
+2. Install the required dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Usage
+
+To run the agent, execute the following command:
+```
+python main.py
+```
+
+## Agent Capabilities
+
+The agent is capable of performing various tasks by leveraging the following tools:
+
+### Tools
+- **WikiTool**: Fetches summaries of topics from Wikipedia, handling disambiguation and page errors gracefully.
+- **WebSearchTool**: Performs web searches using DuckDuckGo and retrieves the first result's snippet.
+- **ExcelAnalysisTool**: Loads an Excel file and sums sales from items labeled as "food", excluding drinks. Returns the total sales in USD with 2 decimal places.
+
+### Models
+- The agent uses the Hugging Face Inference API via `HfApiModel` to interact with models such as `DeepSeek-R1` or `DeepSeek-Coder`.
+
+### Example Workflow
+1. The agent receives a request.
+2. Based on the request, it selects the appropriate tool (e.g., WikiTool for Wikipedia lookups).
+3. The tool processes the request and returns the result.
+
+## Contributing
+
+Contributions are welcome! Please submit a pull request or open an issue for any enhancements or bug fixes.
+
+## Future Enhancements
+
+- Deployment to Hugging Face Spaces.
+- Adding more tools for advanced functionalities.
+- Improving error handling and logging.

agent_config.py

@@ -0,0 +1,38 @@
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+from tools.wiki_tool import WikiTool
+from tools.excel_analysis_tool import ExcelAnalysisTool
+import os
+
+def create_agent():
+    """
+    Creates and configures the CodeAgent with the necessary model and tools.
+
+    Returns:
+        CodeAgent: The configured agent instance.
+    """
+    # Load API key from environment variable (more secure)
+    api_key = os.getenv("HF_API_KEY")
+    if not api_key:
+        raise RuntimeError("Missing Hugging Face API key. Set HF_API_KEY environment variable.")
+
+    # Initialize the LLM model
+    model = HfApiModel(api_key=api_key, model_id="deepseek-ai/deepseek-coder-6.7b-instruct")
+
+    # Initialize tools
+    wiki_tool = WikiTool()                        # Custom Wikipedia wrapper
+    web_search_tool = DuckDuckGoSearchTool()      # Built-in smolagents web search tool
+    excel_analysis_tool = ExcelAnalysisTool()     # Custom Excel processing tool
+
+    # Create and return the agent
+    agent = CodeAgent(
+        model=model,
+        tools=[wiki_tool, web_search_tool, excel_analysis_tool],
+        max_steps=8,
+        verbosity_level=2
+    )
+    return agent
+
+# Example usage
+if __name__ == "__main__":
+    agent = create_agent()
+    print("Agent created with tools:", [tool.name for tool in agent.tools])

main.py

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+import gradio as gr
+from agent_config import create_agent
+
+agent = create_agent()
+
+def ask_agent(prompt: str) -> str:
+    try:
+        return agent.chat(prompt)
+    except Exception as e:
+        return f"Error: {e}"
+
+iface = gr.Interface(
+    fn=ask_agent,
+    inputs=gr.Textbox(lines=2, placeholder="Ask the agent something..."),
+    outputs="text",
+    title="Agent with smolagents + DeepSeek",
+    description="This agent uses smolagents, DeepSeek LLM, and custom tools to answer factual and data questions. ",
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -1,2 +1,6 @@
 gradio
-requests
+smolagents
+wikipedia
+pandas
+openpyxl
+huggingface_hub

tools/__init__.py

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+# This file is intentionally left blank.

tools/excel_analysis_tool.py

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+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+class ExcelAnalysisTool:
+    def __init__(self):
+        self.name = "ExcelAnalysisTool"
+        self.description = (
+            "Loads an Excel file from the GAIA dataset on Hugging Face and calculates "
+            "the total sales for items labeled as 'food', excluding drinks. "
+            "Provide input as a string with the filename, e.g., 'sales_data.xlsx'."
+        )
+        self.repo_id = "gaia-benchmark/GAIA"
+
+    def __call__(self, filename: str) -> str:
+        """
+        Loads and processes the Excel file.
+
+        Args:
+            filename (str): The name of the Excel file (e.g., 'sales_data.xlsx').
+
+        Returns:
+            str: Total food sales in USD, or an error message.
+        """
+        try:
+            # Download the file from Hugging Face Hub
+            file_path = hf_hub_download(
+                repo_id=self.repo_id,
+                filename=filename,
+                repo_type="dataset"
+            )
+
+            # Load the Excel file into a DataFrame
+            df = pd.read_excel(file_path)
+
+            # Filter rows: category == 'food' and item != 'drinks'
+            food_sales = df[
+                (df['category'].str.lower() == 'food') &
+                (df['item'].str.lower() != 'drinks')
+            ]
+
+            total_sales = food_sales['sales'].sum()
+            return f"Total sales for food items: ${total_sales:.2f}"
+
+        except FileNotFoundError:
+            return "Error: The specified file was not found."
+        except KeyError as e:
+            return f"Error: Missing expected column in the Excel file: {str(e)}"
+        except Exception as e:
+            return f"An unexpected error occurred: {str(e)}"

tools/wiki_tool.py

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+import wikipedia
+
+class WikiTool:
+    def __init__(self):
+        self.name = "WikiTool"
+        self.description = (
+            "Performs Wikipedia lookups. Supports actions: 'summary' and 'is_historical_country'.\n"
+            "Usage:\n"
+            "- {'action': 'summary', 'topic': 'France'}\n"
+            "- {'action': 'is_historical_country', 'country_name': 'Yugoslavia'}"
+        )
+
+    def fetch_summary(self, topic: str) -> str:
+        try:
+            summary = wikipedia.summary(topic, sentences=3)
+            return summary
+        except wikipedia.DisambiguationError as e:
+            return f"Disambiguation error: The topic '{topic}' is ambiguous. Suggestions: {e.options[:5]}"
+        except wikipedia.PageError:
+            return f"Page error: The topic '{topic}' does not exist on Wikipedia."
+        except Exception as e:
+            return f"An unexpected error occurred: {str(e)}"
+
+    def is_historical_country(self, country_name: str) -> bool:
+        try:
+            summary = wikipedia.summary(country_name, sentences=2).lower()
+            keywords = [
+                "former country", "no longer exists", "historical country",
+                "was a country", "defunct", "dissolved", "existed until",
+                "disestablished", "merged into"
+            ]
+            return any(keyword in summary for keyword in keywords)
+        except Exception:
+            return False
+
+    def __call__(self, args: dict):
+        action = args.get("action")
+
+        if action == "summary":
+            return self.fetch_summary(args.get("topic", ""))
+        elif action == "is_historical_country":
+            return self.is_historical_country(args.get("country_name", ""))
+        else:
+            return "Error: Unknown action. Use 'summary' or 'is_historical_country'."